Publications

In this section, you will find a list of total of 116 publications, including journal papers, books and book chapters, invited keynotes, conference papers, talks, posters and presentations. For each of these publications, you can open and read the full-text, or the presentation slides. In the graphs below, you can also see a summary of my publications by type and year.

A summary of my publications by type

A summary of my publications by year of publication

Publication Keywords

A word cloud of the keywords in my publication list

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Publication Abstracts

A word cloud of my abstracts in my publication list

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Publication Categories:

Papers and Books

Alexandridis, K. (2021). Machine Learning Computer Vision Applications for Spatial AI Object Recognition in Orange County, California. TechRxiv. Preprint. https://doi.org/10.36227/techrxiv.15157689.v1


Abstract: We provide an integrated and systematic automation approach to spatial object recognition and positional detection using AI machine learning and computer vision algorithms for Orange County, California. We describe a comprehensive methodology for multi-sensor, high-resolution field data acquisition, along with post-field processing and pre-analysis processing tasks. We developed a series of algorithmic formulations and workflows that integrate convolutional deep neural network learning with detected object positioning estimation in 360\textdegree~equirectancular photosphere imagery. We provide examples of application processing more than 800 thousand cardinal directions in photosphere images across two areas in Orange County, and present detection results for stop-sign and fire hydrant object recognition. We discuss the efficiency and effectiveness of our approach, along with broader inferences related to the performance and implications of this approach for future technological innovations, including automation of spatial data and public asset inventories, and near real-time AI field data systems.
Keywords: machine learning, image processing and computer vision, deep learning, spatial feature classification, unsupervised classification, object detection, spatial position recognition,traffic signs
2021-Alexandridis TechRxiv IEEE TAI Proof.pdf

Alexandridis, K., Takemura, S., Webb, A., Lausche, B., Culter, J., and Sato, T. (2018). Semantic knowledge network inference across a range of stakeholders and communities of practice. Environmental Modelling & Software, 109, 202-222. doi:https://doi.org/10.1016/j.envsoft.2018.08.026


Abstract: This paper provides empirical and experimental assessments of thematic knowledge discourses based on two case studies in the US Virgin Islands and Florida. We utilize a latent semantic indexing analysis over natural language corpus to classify and categorize knowledge categories. We computed TF*IDF scores and associated co-occurrence Jaccard similarity scores to construct semantic knowledge networks. Using network analysis, we computed structural metrics over four composite groups: neighbor-based, centrality, equivalence and position. The analysis show that structural network characteristics of environmental knowledge can exponentially predict associations between knowledge categories. We show that connectivity play a critical role on acquisition, representation, and diffusion patterns of knowledge within local communities. We provide evidence of a global prevalence of a shared knowledge core. We show that core social-ecological attributes of knowledge follow scale-free, power law distributions and stable, equilibrium network structures. We identify two distinct models of bidirectional translation: a bottom-up and a top-down.
Keywords: Semantic networks; Knowledge networks; Social-ecological systems; Integrated local ecological knowledge; Latent semantic analysis; social network analysis
2018-Alexandridis et al-ENVSOFT.pdf

Alexandridis, K. (2018). Assessing Cognitive and Social Attitudes toward Environmental Conservation in Coral Reef Social-Ecological Systems. Social Sciences, 7(7), 109. doi:https://doi.org/10.3390/socsci7070109


Abstract: This study addresses the latent construct of attitudes toward environmental conservation based on study participant’s responses. We measured and evaluated the latent scale based on an 18-item scale instrument, over four experimental strata (N = 945) in the US Virgin Islands and the Caribbean. We estimated the latent scale reliability and validity. We further fitted multiple alternative two-parameter logistic (2PL) and graded response models (GRM) from Item-Response Theory. We finally constructed and fitted equivalent structural and generalized structural equation models (SEM/GSEM) for the attitudinal latent scale. All scale measures (composite, alpha-based, IRT-based, and SEM-based) were consistently and reliably valid measures of the study participants’ latent attitudes toward conservation. We found statistically significant differences among participant’s attributes relating to socio-demographic, physical, and core environmental characteristics of participants. We assert that the nature of relationship between cognitive attitudes and individual as well as social behavior related to environmental conservation.
Keywords: environmental attitudes; coral reefs; scale development; item-response theory; graded response model; social-ecological systems; reliability; generalized structural equation model
2018-Alexandridis-SOCSCI.pdf

Engerman, K., Alexandridis, K., Drost, D., and Michailidis, S. (2015). The Pedagogical Use of Creative Problem Solving. In D. Pardlow and M. A. Trent (Eds.), Cultivating Visionary Leadership by Learning for Global Success: Beyond the Language and Literature Classroom (pp. 196-207). Cambridge, UK: Cambridge Scholars Publishing.


Abstract: This book chapter provides an overview of a pedagogical study and use of creative problem solving. It approaches creative problem solving within a cognitive, behavioral and social framework and approach to student retention. Specifically, the three dimensions of study included cognitive and individual student engagement, social and academic integration, and behavioral/retention study of outcomes. Our framework used a range of methodological approaches including student interviews, classroom exercises and observations, creative problem solving training, participatory analysis, and statistical analysis of student outcomes and success rates in key STEM variables and outcomes.
Keywords: STEM retention; creative problem solving; pedagogical approaches; mixed methods
2015-Engerman et al-BookChapter.pdf

Alexandridis, K., and Pijanowski, B. (2013). Spatially-Explicit Bayesian Information Entropy Metrics for Calibrating Landscape Transformation Models. Entropy, 15(7), 2480-2509. doi:https://doi.org/10.3390/e15072480


Abstract: Assessing spatial model performance often presents challenges related to the choice and suitability of traditional statistical methods in capturing the true validity and dynamics of the predicted outcomes. The stochastic nature of many of our contemporary spatial models of land use change necessitate the testing and development of new and innovative methodologies in statistical spatial assessment. In many cases, spatial model performance depends critically on the spatially-explicit prior distributions, characteristics, availability and prevalence of the variables and factors under study. This study explores the statistical spatial characteristics of statistical model assessment of modeling land use change dynamics in a seven-county study area in South-Eastern Wisconsin during the historical period of 1963–1990. The artificial neural network-based Land Transformation Model (LTM) predictions are used to compare simulated with historical land use transformations in urban/suburban landscapes. We introduce a range of Bayesian information entropy statistical spatial metrics for assessing the model performance across multiple simulation testing runs. Bayesian entropic estimates of model performance are compared against information-theoretic stochastic entropy estimates and theoretically-derived accuracy assessments. We argue for the critical role of informational uncertainty across different scales of spatial resolution in informing spatial landscape model assessment. Our analysis reveals how incorporation of spatial and landscape information asymmetry estimates can improve our stochastic assessments of spatial model predictions. Finally our study shows how spatially-explicit entropic classification accuracy estimates can work closely with dynamic modeling methodologies in improving our scientific understanding of landscape change as a complex adaptive system and process.
Keywords: artificial neural networks; land use change; Bayesian information; Bayesian entropy; maximum entropy
2013-Alexandridis and Pijanowski-ENTROPY.pdf

Alexandridis, K., and Maru, Y. (2012). Collapse and Reorganization Patterns of Social Knowledge Representation in Evolving Semantic Networks. Information Sciences, 200(1), 1-21. doi:https://doi.org/10.1016/j.ins.2012.02.053

Abstract: This study introduces semantic network analysis of natural language processing in collective social settings. It utilizes the spreading-activation theory of human long-term memories from social psychology to extract information and graph-theoretic linguistic approximations supporting rational propositional inference and formalisms. Using an empirical case study we demonstrate the process of extracting linguistic concepts from data and training a Hopfield artificial neural network for semantic network classification. We further develop an agent-based computational model of network evolution in order to study the processes and patterns of collective semantic knowledge representation, introducing incidents of collapses in central network structures. Large ensembles of simulation replication experiments are conducted and the resulted networks are analysed using a variety of estimation techniques. We show how collective social structure emerges from simple interactions among semantic categories. Our findings provide evidence of the significance of collapse and reorganization effects in the structure of collective social knowledge; the statistical importance of the within-factor interactions in network evolution, and; stochastic exploration of whole parameter spaces in large ensembles of simulation runs can reveal important self-organizing aspects of the system’s behaviour. The last session discusses the results and revisits the issues of generative semantic inference and the semantic networks as inferential formalisms in guiding self-organizing systemic complexity.Keywords: Self-organization; Semantic network analysis; Natural language processing; Collective knowledge representation; Network evolution; Agent-based modeling
2012-Alexandridis and Maru-INS.pdf

Alexandridis, K., Coe, K., and Garnett, S. (2010). Semantic Analysis of Natural Language Processing in a Study of Nurse Mobility in the Northern Territory, Australia. Journal of Population Research, 27(1), 15-42. doi:http://dx.doi.org/10.1007/s12546-010-9030-5


Abstract: In lieu of diverse consequences in the demand and supply of health care professionals such as nurses and midwifes in Australia and the world, a firm understanding the characteristics of staff mobility and the factors influencing their retention could lead to achieving enhanced service delivery, greater job satisfaction, and the establishment of a more stable and robust workforce. The research reported in this paper attempts to shed light into qualitative aspects of mobility in health care professional staff in the Northern Territory of Australia. It builds upon an existing survey study of the quantitative factors that determine why nurses and midwives come to the Northern Territory, why some stay and why many leave, by analysing additional qualitative textual responses of participants using semantic network approaches to natural language processing. Our results illustrate the methodological and policy significance of semantic approaches to knowledge acquisition and representation, especially in complementing findings of traditional survey analysis techniques, and in analysing the broader social settings, impacts and consequences of staff retention and mobility.
Keywords: Semantic analysis; Semantic networks; Knowledge representation; Health care professionals; Population mobility; Nurses and midwives; Northern Territory, Australia
2010-Alexandridis et al-JPOR.pdf

Alexandridis, K., Maru, Y., Davies, J., Box, P., and Hueneke, H. (2009). Constructing Semantic Knowledge Networks from the Ground Up: livelihoods and employment outcomes in Anmatjere region, central Australia. In R. S. Anderssen, R. D. Braddock, and L. T. H. Newham (Eds.), 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation (pp. 2819-2825). Cairns, Australia, 13-17 July 2009: Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation.


Abstract: People need real opportunities to live the kind of life to which they aspire - to undertake livelihood activities they have reason to value, to achieve good health and well being outcomes, and to have resilience to shocks and stresses. A range of stakeholders consider that economic development is constrained by lack of engagement between Aboriginal people and labor markets, particularly given planned expansion of horticultural and mining operations. Aboriginal people of the Anmatjere region of Central Australia speak their own languages at home, have customary responsibilities for care of the region’s natural and cultural resources, and have low levels of formal mainstream education. They aspire to jobs in their region and are engaged relatively strongly in employment in the community services sector and seasonal work in the pastoral industry, but not in other private sector employment. Their high dependence for income on social security payments and government funded jobs makes their livelihoods vulnerable to changes in government institutions. The modelling work presented in this paper is based on the views, attitudes and experiences of people living in the Anmatjere region about jobs and livelihoods. We have organized these as a collective knowledge representation, using semantic networks. This has elicited understanding of the structure, strength and quality of connections amongst social, economic, environmental and cultural dimensions important in people’s livelihoods. The qualitative data were analysed using (a) natural language processing and linguistic algorithms; (b) exploration of semantic associations among knowledge constructs using a Hopfield-type Artificial Neural Network; and (c) graph-theoretic network analyses. We present the findings of this analysis in light of critical challenges that the Anmatjere community is facing. We show that culturally-explicit local Aboriginal institutions, world views and behaviours play significant and central roles in maintaining the community’s knowledge representations. They connect people and establish the social and cultural roles that are critical in people’s search for opportunity, income and the sustainability of their livelihoods in the region. ‘Top down’ actions including changes to government institutions aimed at enhancing individual Aboriginal people’s engagement with employment have little chance of success unless they take into account the locally and culturally-specific ways in which the community is collectively functioning.
Keywords: Indigenous communities; sustainable livelihoods; modelling; semantic networks; social networks; knowledge representation; artificial neural networks; participatory research
2009-Alexandridis-MODSIM.pdf

Maru, Y., Alexandridis, K., and Perez, P. (2009). Taking 'participatory' in participatory modelling seriously. In R. S. Anderssen, R. D. Braddock, and L. T. H. Newham (Eds.), 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation (pp. 3011-3017). Cairns, Australia, 13-17 July 2009: Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation.


Abstract: Over the last three decades participatory research processes have informed much international development and conservation work in developing countries. Public participation is also a growing legislative requirement in natural resource and environmental management in developed countries. So far, multiple participatory approaches have been formulated and applied in different contexts, including so-called participatory modelling methods. The latter have developed alongside a growing unease and fundamental critique of the participatory approaches and their theoretical underpinnings. One of the central themes running through the critique is the naïveté with which complexities of power relations are assumed to be understood and addressed in participatory approaches. The critique also highlights the danger that participatory approaches become legitimising instruments that simply maintain and reinforce existing power relations. In this paper we engage with the critical literature in the hope of drawing lessons and requirements for participatory modelling. We also empirically evaluate participatory modelling case studies with regard to the fundamental critique. While we do not agree with some demands from the critique that imply abandoning the whole participatory enterprise, we suggest that claims to participatory modelling be taken seriously and that each claim be accompanied by critical reflection. Based on a review of the literature we suggest initial set of questions towards developing a framework for critical reflection.
Keywords: participation; participatory modeling; power relations; critical reflection; participatory research; community-based decision making; community-based management; community-based resource management
2009-Maru et al-MODSIM.pdf

Larson, S., and Alexandridis, K. (2009). Socio-Economic Profiling of Tropical Rivers. Canberra, ACT: Australian Government, Department of the Environment, Water, Herritage, and the Arts, Land and Water Australia, National Water Commission, Tropical Rivers and Coastal Knowledge (TRaCK) Research Hub (ISBN: 978-1-921544-99-6), pp.70.


Abstract: This document reports on four major objectives of stage (B) of the TRaCK project 3.1: People and the economy. The four objectives were: (a) to develop an integrated conceptual framework for the socio-economic profiling; (b) to update existing knowledge with data from the 2006 Census; (c) to develop profiles of individual catchments based on their individual socio-economic characteristics; and (d) to compare and contrast the TR catchments and to identify catchments which are socio-economically ‘similar’ or ‘dissimilar’.
Keywords: socio-economics; tropical rivers; sustainability
2009-Larson and Alexandridis-TRaCK.pdf

Alexandridis, K., and Wang, X. (2008). Simulation and Modelling of Urban and Regional Transitions (SMURT): Proceedings of a CSIRO Workshop, Melbourne, Australia, December 4-6, 2007. Canbera, ACT, Australia: CSIRO Sustainable Ecosystems (ISBN: 978-0-643-09603-5), pp.195.


Abstract: Urban and regional transition is a human-driven process; unless we clearly understand the degree and magnitude of transitions’ social significance, we cannot achieve levels of policy and management responses that would enhance the social, economic, institutional and cognitive capacity of societies to respond and adapt to the transitions. Simulation and modelling may provide an alternative pathway to the answers. It becomes critical to develop a practical approach, but also maintain the accuracy in the simulation of complex regional and urban dynamics, in order to identify the vulnerability of our regional and urban systems, and to help develop more effective policies to secure their sustainability. Facing the challenges, the workshop of simulation and modelling of urban and regional transition (SMURT) aims to achieve an understanding of existing regional and urban models and their analysis techniques, and identify pathways or roadmaps to apply the simulation to regional and urban policy development, planning and management in the real world. The Proceedings summarises the workshop, including semantic analysis of the participants’ responses to four questions of SMURT in chapter one, introduction of workshop program and participants in chapter two, abstracts of workshop presentation in chapter three, and results of workshop exercises and interactions in chapter four. All presentation details are provided in appendix A. Presentations by keynote speakers, on the subjects of the challenge of coastal growth, fundamentals of urban and regional dynamics, modelling and simulation techniques to support understanding of sustainable socio-technological systems, using diverse modelling methods to forecast land use change, and GIS-based modelling and visualisation tools to assist urban and regional planning and management, highlights research needs, critical issues, and approaches in urban and regional sustainable planning and development.
Keywords: land use change; urbanization; modeling; Australia; urban sprawl
2008-Alexandridis and Wang-SMURT.pdf

Alexandridis, K. (2008). Monte Carlo Extreme Event Simulations for Understanding Water Quality Change Classifications in the GBR Region. Technical Report. Townsville, Queensland, Australia: Commonwealth Scientific and Industrial Research Organization (CSIRO), pp. 18.


Abstract: Thus study explores the usefulness of a Monte Carlo simulation approach as an analysis tool aiming to capture the properties and patterns of change for sequences of events, and to generate scenarios and classifications of water quality change in the Great Barrier Reef region of Australia. Extreme events have serious impacts on policy, social and economic characteristics of the systems they affectm and can go byond the spatial and temporal boundaries of their immediate physical systems. We use probabilistic distribution modeling and Monte Carlo simulations to simulate water quality implications of extreme events and provide risk event assessment based on moving thresholds of tolerance.
Keywords: water quality; natural resource management; simulation; Monte Carlo; classification; modeling; extreme events; hazards; decision sciences
2008-Alexandridis-CSIROTechnicalReport.pdf

Alexandridis, K. T., and Pijanowski, B. C. (2007). Assessing Multiagent Parcelization Performance in the MABEL Simulation Model Using Monte Carlo Replication Experiments. Environment and Planning B: Planning and Design, 34(2), 223-244. doi:https://doi.org/10.1068/b31181


Abstract: In this paper we present and test the functionality of a parcelization algorithm, implemented in our spatially explicit, agent-based land-use-change model which we call the Multi Agent-based Behavioral Economic Landscape (MABEL) model. In order to test the best possible spatial configuration of the algorithm and its efficiency compared with historically observed land-use changes, we employed a Monte Carlo simulation approach with a series of replication experiments across time, and compared observed changes between 1970 and 1990, and across two different landscapes in Michigan, USA. We compare the simulated parcel shapes with historically observed land-use changes using the landscape-ecology metric program, FRAGSTATS
Keywords: experiment; MABEL; model; Monte Carlo; parcelization; performance; replication; simulation; simulation model; land use change; land cover; parcelization; agent-based simulation; agent-based modeling; multi-agent
2007-Alexandridis and Pijanowski-EPB.pdf

Alexandridis, K., and Measham, T. G. (2007). Modelling Regional Grazing Viability in Outback Australia Using Bayesian Livelihood Networks. Canberra, ACT: CSIRO Sustainable Ecosystems (ISBN: 978-0-643-09498-7), pp.65.


Abstract: Outback Australia is characterised by multiple competing trajectories to regional social and economic viability, including a tension between agricultural production and other land uses, reflecting broader social and economic values. However, different regions of the outback experience this tension in different ways. In this context, the concept of sustainable livelihoods represents an important way of conceptualising the health and viability of outback regions and the people who live in them. This concept is receiving increased attention in Australia as a way to understand and address the linkages between social and ecological concerns in rural environments. The scope of the research undertaken and reported in this document is to identify and link key social and economic issues affecting the viability and sustainability of livelihoods in Outback regions. Specifically, the research focuses on enhancing our scientific understanding and filling knowledge gaps pertaining to issues of viability and community health in Outback Australia. Our research integrates cultural, social, and economic dimensions with existing ecological and biophysical understanding of these regions. Also improves existing understanding of the network of relationships among livelihood elements that affect natural resource management and regional viability in general. Specific research objectives include: investigating and exploring advanced methodological and modelling techniques such as probabilistic and social networks; linking qualitative with quantitative approaches for social-ecological complex systems; enhancing of the contribution of community-driven decision making on pathways to alternative futures and regional priorities; and understanding regional viability and sustainability of livelihood systems from the “ground-up”. Finally, the researchers’ overarching goal is to assist the Tropical Savannas CRC in fulfilling its unique role in Outback Australia in understanding the multiplicity of environmental, economic, cultural and social dimensions and contributing to the sustainability and management of outback regions. An important issue is understanding the factors that influence sustainable livelihoods in different contexts. These issues are explored through a review of literature on the livelihoods concept in general followed by a detailed case study of the factors affecting grazing livelihoods in the upper Burdekin catchment. The upper Burdekin region is strongly oriented towards pastoralism, with a predominance of owner-operated family-based enterprises. The aim of the upper Burdekin case study is to improve understanding of the factors influencing outback livelihoods through a participatory Bayesian Belief Network approach. In the context of regional outback Australia, this report redefines the concept of sustainable livelihoods as a system of livelihood elements that contribute uniquely, collaboratively and conjunctively to the viability of the region, communities and individuals. In other words, the notion of sustainable livelihoods adopted for the scope of this research, moves away from collective capital accumulation, and represents a more fundamental, generative and emergent mechanism for social, economic and environmental system integration. The report demonstrates the methodological and technical elements of a Bayesian livelihoods network for grazing systems in outback Australia. We define a Bayesian livelihoods network as a probabilistic network of relationships among livelihood elements present in a subjective system of heuristic inference. We use qualitative, participatory and community-driven information to construct a livelihoods network involving issues of viability and sustainability in grazing livelihoods. Beyond the social science basis of our approach we are demonstrating the use of advanced Bayesian network techniques for representing such systems. We describe the model construction process and analyse key drivers of probabilistic elicitation of livelihood elements as graph nodes in the network. We examine different types of nodes and their probabilistic distributions that emerge from (a) self-reported perceptions and inductive inference of citizens and community members; (b) objectively verified elements of the physical and environmental drivers of the livelihoods system, and; (c) heuristically inferred relationships amongst key members and determinants of sustainable grazing livelihoods. We discuss the importance of social science and qualitative research to inform quantitative and inductive paradigms of probabilistic and cognitive inference, using innovative, bottom-up approaches. Finally, the report presents recommendations for future research and the potential role of heuristics in representing dynamic concepts of structure and form in social systems. Fortunately, many outback farming households have a degree of choice in how they go about maintaining a viable livelihood that includes off farm work, diversification into other sectors and financial investments. The drivers affecting these decisions are many and complex. Understanding these is the key to making improved decisions in the long term. The drivers of choices and outcomes presented in a modelling framework must be robust enough to function across a number of theoretical and empirical cases. Such a robust framework renders the use of a heuristic and logical interpretation of actions and outcomes as an essential mechanism of livelihoods representation.
Keywords: livelihoods; participatory Bayesian Belief Networks; pBBN; sustainability; grazing; social networks; sustainable livelihoods; alternative livelihoods; livelihood networks; Bayesian Belief Networks
2007-Alexandridis and Measham-CSIRO.pdf

Alexandridis, K., and Measham, T. G. (2007). Modeling Grazing Livelihood Systems in the Australian Outback Using Participatory Bayesian Networks. In L. Oxley and D. Kulasiri (Eds.), MODSIM 2007 International Congress on Modelling and Simulation (pp. 2680-2685). New Zealand: Modelling and Simulation Society of Australia and New Zealand.


Abstract: This paper describes the use of participatory Bayesian Belief Networks (pBBN) as tools for modelling a representative livelihood system for the graziers of the Outback areas in Northern Queensland (Upper Burdekin region). We use qualitative participatory techniques (community interviews, stakeholder and expert feedback) to manage for uncertainty in decision making related to key determinants of grazing livelihoods in the region. The process yielded the “BOLNet”, a livelihood representation, graph-theoretic network of relationships between key aspects of living within the grazing community. BOLNet is a combination of graphical and qualitative representations of livelihood linkages and relationships. It is a form of “graphical narrative” that bridges the traditional divisions between an extrapolative or descriptive measurement and prescriptive or normative observation. Using a combination of Bayesian Belief network analysis for the strength of the relationships and graphtheoretic network metrics for the structure of the network, we highlight a set of important findings that can aid communities, stakeholders, decision makers and policy makers to improve the quality and efficiency of sustainability approaches and actions.
Keywords: livelihoods; participatory Bayesian Belief Networks; pBBN; sustainability; grazing; social networks; sustainable livelihoods; alternative livelihoods; livelihood networks; Bayesian Belief Networks
2007-Alexandridis and Measham-MODSIM.pdf

Pijanowski, B. C., Olson, J. M., Washington-Ottombre, C., Campbell, D. J., Davis, A. Y., and Alexandridis, K. T. (2007). Pluralistic Modelling Approaches to Simulating Climate-Land Change Interactions in East Africa. In L. Oxley and D. Kulasiri (Eds.), MODSIM 2007 International Congress on Modelling and Simulation (pp. 636-642). New Zealand: Modelling and Simulation Society of Australia and New Zealand.


Abstract: We summarize the use of several different modelling approaches we are employing to understand how climate change impacts changes in land use in East Africa. A role playing game model has been employed that helps to elucidate the behavioural drivers of land use change and how these factors are integrated with other biophysical (e.g. climate) and socioeconomic drivers. Outcomes from the game include a qualitative list and integrative understanding of these drivers over spatial and temporal scales and a series of decision maps produced by game participants. A second method includes the use of expert systems or knowledge acquisition approaches that attempt to synthesize expert opinion on how societies may adapt to changes in climate. Outcomes of these knowledge acquisition activities include expert maps and systems diagrams both of which are used to construct and validate models. These two qualitative approaches were used to construct three different sets of models: those that use multi-criteria evaluation techniques integrating a variety of spatial data layers using a geographic information system; a machine learning based model employing artificial neural networks that learn from patterns in data, and a behavioural model using Bayesian Belief Networks that simulates individual behaviour in the context of social interactions. We show how these diverse methods are used together to aid in our understanding of the drivers and impacts of climate change on land use systems in East Africa. In particular, we are interested in the impacts climate change might have on pastoralist, cropping, and urban systems over the next 10-50 years. These methods are being used along with process based models of regional climate change and crop production models to understand the coupling of climate and land systems in this geographically diverse area of the world. In our discussion we compare and contrast these very different, but complementary, modelling approaches to understand climate change at local to regional scales.
Keywords: land use change; agent-based models; climate change; role-playing simulations; RPG; expert systems; qualitative models; East Africa
2007-Pijanowski et al-MODSIM.pdf

Alexandridis, K. (2007). The Interplay between Pattern and Structure in Sea Change Communities Across Australia: An Analysis of Land Use Change Patterns. Technical Report, June 2007. Townsville, Queensland, Australia: CSIRO Sustainable Ecosystems, Davies Laboratory, pp. 30.


Abstract: Land use landscape and change patterns are in the forefront of international research. The pervasiveness and speed of land use changes is unprecedented both internationally and within Australia. This study aims to provide some insight in the importance of studying land use changes across a diversity of landscapes and scales of analysis. It provides a rudimentary approach to studying primary land use change patterns in Australia, by examining both biophysical landscape and demographic changes in space and time. It also goes beyond this primary level of analysis and attempts a further insight into secondary – yet extremely important look into structure and patterns of urbanization patches across the landscape. Finally it attempts to synthesize a comprehensive conceptual model for understanding dynamics of change patterns for the sea change communities across Australia.
Keywords: land use change; sea change; Australia; urbanization; landscape dynamics; demographics
2007-Alexandrdis-CSIROTechnicalReport.pdf

Pijanowski, B. C., Alexandridis, K. T., and Müller, D. (2006). Modelling Urbanization Patterns in Two Diverse Regions of the World. Journal of Land Use Science, 1(2-4), 83 - 108. doi:https://doi.org/10.1080/17474230601058310


Abstract: We present work applying a similarly parameterized urbanization model to two diverse regions of the world, one in the USA and another in Albania. Eight calibration metrics are used to estimate model goodness of fit: four location-based measures (e.g. kappa), and four patch metrics based on patch size, shape and configuration. We conclude that if we use location goodness of fit estimates, the model fits observed data very well for most simulations. The model fit to data better in Albania than in the USA probably owing to top-down land ownership policies occurring in Albania and owing to the fact that commonly used land use change model drivers, such as distance to road, are not likely to capture individual behaviours that are important in the USA. Patch metrics provided additional information on model fit to observed data, and we suggest that, in some circumstances, patch metrics may be more useful than location metrics to calibrate a land use change model.
Keywords: LTM; land use change; neural networks; land use; urbanization; artificial neural networks; model calibration; landscape pattern; landscape metrics
2006-Pijanowski et al-JLUS.pdf

Alexandridis, K. T. (2006). Exploring Complex Dynamics in Multi Agent-Based Intelligent Systems: Theoretical and Experimental Approaches Using the Multi Agent-based Behavioral Economic Landscape (MABEL) Model. (Doctor of Philosophy Ph.D. Dissertation), Purdue University, West Lafayette, Indiana, ISBN: 9780542864230, pp. 262. Available from ProQuest Information and Learning Company UMI Dissertation Services (Source: DAI-B 67/09, p.4949, March 2007) database. (3232142).. URL: https://docs.lib.purdue.edu/dissertations/AAI3232142/


Abstract: This dissertation adopts a holistic and detailed approach to modeling spatially explicit agent-based artificial intelligent systems, using the Multi Agent-based Behavioral Economic Landscape (MABEL) model. The research questions that addresses stem from the need to understand and analyze the real-world patterns and dynamics of land use change from a coupled human-environmental systems perspective. Describes the systemic, mathematical, statistical, socio-economic and spatial dynamics of the MABEL modeling framework, and provides a wide array of cross-disciplinary modeling applications within the research, decision-making and policy domains. Establishes the symbolic properties of the MABEL model as a Markov decision process, analyzes the decision-theoretic utility and optimization attributes of agents towards comprising statistically and spatially optimal policies and actions, and explores the probabilogic character of the agents' decision-making and inference mechanisms via the use of Bayesian belief and decision networks. Develops and describes a Monte Carlo methodology for experimental replications of agent's decisions regarding complex spatial parcel acquisition and learning. Recognizes the gap on spatially-explicit accuracy assessment techniques for complex spatial models, and proposes an ensemble of statistical tools designed to address this problem. Advanced information assessment techniques such as the Receiver-Operator Characteristic curve, the impurity entropy and Gini functions, and the Bayesian classification functions are proposed. The theoretical foundation for modular Bayesian inference in spatially-explicit multi-agent artificial intelligent systems, and the ensembles of cognitive and scenario assessment modular tools build for the MABEL model are provided. Emphasizes the modularity and robustness as valuable qualitative modeling attributes, and examines the role of robust intelligent modeling as a tool for improving policy-decisions related to land use change. Finally, the major contributions to the science are presented along with valuable directions for future research. (Publication Number: AAT 3232142; ProQuest document ID: 1221730431).
Keywords: land use; MABEL; multi-agent simulation; landscape model; intelligent systems; environmental science; forestry; artificial intelligence
2006-Alexandridis-PhD Dissertation.pdf

Alexandridis, K. T., and Pijanowski, B. C. (2006). Modular Bayesian Inference and Learning of Decision Networks as Stand-Alone Mechanisms of the MABEL Model: Implications for Visualization, Comprehension, and Policy-Making. In C. M. Macal, M. J. North, and D. L. Sallach (Eds.), Agent 2005 Conference on: Generative Social Processes, Models, and Mechanisms (pp. 419-438). Gleacher Center, Chicago IL, October 13-15, 2005: Argonne National Laboratory and the University of Chicago.


Abstract: This paper describes a modular component of the MABEL model agents’ cognitive inference mechanism. The probabilistic and probabilogic representation of the agents’ environment and state space is coupled with a Bayesian belief and decision network functionality, which in fact holds Markovian semiparametric properties. Different approaches to modeling multi-agent systems are described and analyzed; problem-, model-, and knowledge-driven approaches to agent inference and learning are emphasized. The notion of modularity in agent-based modeling components is conceptualized. The modular architecture of the decision inference mechanism allows for a flexible architectural design that can be either endogenous or exogenous to the agent-based simulation model. A suite of decision support tools for modular network inference in the MABEL model is showcased; the emphasis is on the component object model versus interoperability development interfaces. These tools provide the complex functionality of developing “models within models,” thus simplifying the need for extensive research support and for a high-end level of knowledge acquisition from the end-users’ perspective. Finally, the paper assesses the validity of visual modeling interfaces for data- and knowledge-acquisition mechanisms that can provide an essential link between an in vitro research model, and the complex realities that are observed and processed by decision-makers, policy-makers, communities, and stakeholders.
Keywords: agent-based model; MABEL; Bayesian Belief Networks; Bayesian Decision Networks; decision-theoretic inference; policy making
2006-Alexandridis-Agent2005.pdf

Lei, Z., Pijanowski, B. C., and Alexandridis, K. T. (2005). Distributed Modeling Architecture of a Multi Agent-based Behavioral Economic Landscape (MABEL) Model. Simulation: Transactions of the Society for Modeling & Simulation International, 81(7), 503-515. doi:https://doi.org/10.1177/0037549705058067


Abstract: In this paper, we discuss a distributed modeling architecture in a Multi Agentbased Behavioral Economic Landscape (MABEL) model that simulates land use changes over time and space. Based on Agent-Based Modeling (ABM) methodologies, MABEL presents a bottom-up approach to allow the analysis of dynamic features and relations among geographic, environmental, human, and socio-economic attributes of landowners, as well as comprehensive relational schematics of land use change. We adopt a distributed modeling architecture (DMA) in MABEL to separate the modeling of agent behaviors in Bayesian Belief Networks from task-specific simulation scenarios. Through a client-server infrastructure, MABEL provides an efficient and scalable decision request-response mechanism among heterogeneous agents, scenarios, and behavioral models. In addition, our DMA allows us to distribute simulation tasks to remote computers over the Internet, and facilitates parallel operations in multiple computers for large tasks that demand high-end computational capabilities. Furthermore, our DMA offers an extensible platform to integrate external components, including Bayesian Belief Networks, geographic information systems, and database packages across different operation systems. As an important part of the land use change model, a market-bidding system and an adaptive land partition algorithm for land transactions are also discussed.
Keywords: agent-based modeling; MABEL; distributed modeling architecture; distributed artificial intelligence; Bayesian belief networks; land use change; artificial intelligence
2005-Lei et al-SIMULATION.pdf

Pijanowski, B. C., Pithadia, S., Shellito, B. A., and Alexandridis, K. (2005). Calibrating a Neural Network-Based Urban Change Model for Two Metropolitan Areas of the Upper Midwest of the United States. International Journal of Geographical Information Sciences, 19(2), 197-215. doi:https://doi.org/10.1080/13658810410001713416


Abstract: We parameterized a GIS and neural net-based Land Transformation Model for the Detroit and Twin Cities Metropolitan Areas using historical land use data derived from aerial photography. We built several neural net models and attempted to test whether these models were transferable across the two metropolitan areas. Two different types of simulations were conducted. First, we trained and tested the neural nets within each region's data and assessed how well they performed against observed urban change. Second, we used the training neural network weights from one area and applied that to the other. We examined model output over a set of simulations differing in the number of training cycles. Finally, we selected one region within the Twin Cities Metropolitan Area that was 1% the entire area. We examined the results of model simulations for training cycles in this subset that ranged from 100 to 5,000,000. Four different techniques were used to judge model performance: (1) ability to predict correct locations of change using the Kappa coefficient; (2) the scale at which correct and paired omission/commission errors exceeded 50%; (3) the ability of the model to produce similar patterns of change in terms of the number of patches, patch size, patch shape and level of aggregation; and (4) the percentage of cells that were in agreement between model simulations. We found that the neural net model in most cases performed well on pattern but not location according to the kappa coefficient, with kappas ranging from .12 to .30. The model performed well only in one case where the neural net weights from one area were used to simulate the other. We suggest that landscape metrics are good to judge model performance of land use change models but that kappa might not be reliable for situations where a small percentage of the uses change. We also suggest that large number of training cycles and a large proportion of change within the study area are probably needed to produce acceptable kappas and adequate pattern of change.
Keywords: aerial photography; artificial neural networks; cycles; data; errors; GIS; kappa; land; land use; land use change; landscape metrics; landscape pattern
2005-Pijanowski et al-IJGIS.pdf

Alexandridis, K. T., Pijanowski, B. C., and Lei, Z. (2005). The Use of Robust and Efficient Methodologies in Agent-Based Modeling: Case Studies Using Repeated Measures and Behavioral Components in the MABEL Simulation Model. In C. M. Macal, D. Sallach, and M. J. North (Eds.), Proceedings of the Agent 2004 Conference on: Social Dynamics, Interaction, Reflexivity, and Emergence (pp. 127-158). Chicago, IL: Argonne National Laboratory and The University of Chicago.


Abstract: In recent years, the modeling of realistic relationships by agent-based models (ABMs) has been gaining significant ground because of the ability of ABMs to overcome the generalizations and statistical moment assumptions of traditional modeling approaches. ABMs follow a bottom-up approach to modeling, allowing issues of scale, time, and space to be taken into account simultaneously. This paper uses case studies as examples to demonstrate these significant properties in an ABM environment that also incorporates and utilizes traditional statistical assumptions and properties at an individual agent level. In this way, the design of individual agents can be used to more accurately represent existing real-world relationships and reduce the level of uncertainty in predicting individual and collective agent behaviors for sustainable futures. Specific case studies from the Multi Agent-based Behavioral Economic Landscape (MABEL) model are used to illustrate the usefulness of the proposed methods for studying land use change, natural resource management, efficiency, and environmental-specific considerations that affect the decision-making capabilities of the agents. These methods are designed with the end user and decision maker in mind, so that robust and efficient outcomes can be backpropagated to the model in ways that enhance the adaptivity and veridicality of our experiments.
Keywords: agent-based modeling; MABEL; simulation; artificial intelligence; distributed artificial intelligence; land use change; artificial neural networks
2005-Alexandridis-Agent2004.pdf

Pijanowski, B. C., Shellito, B., Pithadia, S., and Alexandridis, K. (2002). Forecasting and assessing the impact of urban sprawl in coastal watersheds along eastern Lake Michigan. Lakes and Reservoirs: Research and Management, 7(3), 271-285. doi:https://doi.org/10.1046/j.1440-1770.2002.00203.x


Abstract: The Land Transformation Model (LTM), which has been developed to forecast urban-use changes in a grid-based geographical information system, was used to explore the consequences of future urban changes to the years 2020 and 2040 using non-urban sprawl and urban-sprawl trends. The model was executed over a large area containing nine of the major coastal watersheds of eastern Lake Michigan. We found that the Black-Macatawa and Lower Grand watersheds will experience the most urban change in the next 20-40 years. These changes will likely impact the hydrological budget, might reduce the amount of nitrogen exported to these watersheds, result in a significant loss of prime agricultural land and reduce the amount of forest cover along the streams in many of these watersheds. The results of this work have significant implications to the Lake Michigan Lake Area Management Plan (LaMP) that was recently developed by the United States Environmental Protection Agency.
Keywords: land use change; coastal watersheds; ecological assessment; lake Michigan; farmland; riparian areas; urban sprawl; Land transformation model; LTM; artificial neural Networks; natural resource management; management plan
2002-Pijanowski et al-LAKESRESERVOIRS.pdf

Alexandridis, K. T., and Pijanowski, B. C. (2002). Multi Agent-Based Environmental Landscape (MABEL) - An Artificial Intelligence Simulation Model: Some Early Assessments. Michigan State University, Department of Agricultural Economics Staff Paper, 2002-09, pp. 27.


Abstract: The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving goals and representing existing relations observed in real-world scenarios, and goal-based efficiency. Intelligent MABEL agents acquire spatial expressions and perform specific tasks demonstrating autonomy, environmental interactions, communication and cooperation, reactivity and proactivity, reasoning and learning capabilities. Their decisions maximize both task-specific marginal utility for their actions and joint, weighted marginal utility for their time-stepping. Agent behavior is achieved by personalizing a dynamic utility-based knowledge base through sequential GIS filtering, probability-distributed weighting, joint probability Bayesian correlational weighting, and goal-based distributional properties, applied to socio-economic and behavioral criteria. First-order logics, heuristics and appropriation of time-step sequences employed, provide a simulation-able environment, capable of re-generating space-time evolution of the agents.
Keywords: agent-based modeling; distributed artificial intelligence; computational social science; GIS; Bayesian Belief networks; human-natural systems
2002-Alexandridis and Pijanowski-MSU Staff Paper.pdf

Papanagiotou, E., Alexandridis, K., and Melfou, K. (1994). Some implications of pesticide ban policies in Greece. In J. Michalek and C.-H. Hanf (Eds.), The Economic Consequences of a Drastic Reduction in Pesticide Use in the EU: Revised Papers of a Workshop Held in Tannenfelde (Schleswig-Holstein) November 13th-14th, 1993 (pp. 307-317). Kiel Germany: Wissenschaftsverlag Vauk Kiel KG.


Abstract: Taking into account the importance of the chemical industry for the Greek economy and the magnitude of pesticide contribution to the cost and productivity of agriculture, this paper estimates some negative effects of a pesticide ban policy, particularly for farmers. Research is mainly based on other European countries, as information on pesticides in Greece is limited.
Keywords: econometric estimation; pesticides; natural resource management; pollution; pesticide ban; agricultural policy; European Union; Common Agricultural Policy; Greece; economic policy; natural resource policy

Conferences and Presentations

Alexandridis, K., and Smith, C. (2021). Artificial Intelligence, Deep Learning and Public Works: Building Smart Spatial Infrastructure in Orange County, California. Training Presentation/Paper at the APWA PWX@Home Spotlight on Transportation 2020/21 Conference, American Public Works Association, June 7, 2021, APWA.

Abstract: The field of geospatial AI has gained recognition in the past few years. Mainly due to the significant proliferation of Deep Learning and Convolutional Neural Networks, the transformed nature of spatial technological innovations now spans across academic, government, industry and commercial sectors alike. We will demonstrate the experimental and production implementation of spatial AI workflows utilized by Orange County Public Works. They range from street imagery and 3D Lidar data collection, through Machine Learning object detection, algorithmic detection of object positioning, and feature extraction for spatial database construction. We processed and analyzed more than 100,000 360° street photosphere images in Orange County. We used augmented CNN algorithms for object detection. Our methodological workflows enable for the construction of automated big data spatial collections with important enhancements to local government geospatial and analytical workflows.
Keywords: artificial intelligence; object recognition; street signs; machine learning; deep learning; Artificial Neural Networks; deep Neural Networks; convolutional networks; photospheres; 360 panoramas; geospatial artificial intelligence

Alexandridis, K., and Smith, C. (2020). Deep Learning Spatial AI for Automatic Street Object Detection in Orange County. Paper at the CalGIS 2020 Conference, Innovation Emerging Technology Session. Long Beach, California, February 9-11, 2020, URISA.

Abstract: The field of geospatial AI has gained recognition in the past few years. Mainly due to the significant proliferation of Deep Learning and Convolutional Neural Networks, the transformed nature of spatial technological innovations now spans across academic, government, industry and commercial sectors alike. We will demonstrate the experimental and production implementation of spatial AI workflows utilized by Orange County Public Works. They range from street imagery and 3D Lidar data collection, through Machine Learning object detection, algorithmic detection of object positioning, and feature extraction for spatial database construction. We processed and analyzed more than 100,000 360° street photosphere images in Orange County. We used augmented CNN algorithms for object detection. Our methodological workflows enable for the construction of automated big data spatial collections with important enhancements to local government geospatial and analytical workflows.
Keywords: artificial intelligence; object recognition; street signs; machine learning; deep learning; Artificial Neural Networks; deep Neural Networks; convolutional networks; photospheres; 360 panoramas; geospatial artificial intelligence

Smith, C., and Alexandridis, K. (2019). How Orange County is Using Artificial Intelligence and Machine Learning in New Ways to Solve Surveying and Mapping Challenges. Keynote Guest Paper at the Cal Poly Pomona and ELAC Geomatics Conference. Cal Poly Pomona Campus, California, September 19, 2019, California State Polytechnic University.

Abstact: The field of geospatial AI has gain recognition in the past few years. Mainly owing to the significant proliferation of Deep Learning and Convolutional Neural Networks, the transformed nature of spatial technological innovations now spans across academic, government, industry and commercial sectors alike. In this presentation we will demonstrate the experimental and production implementation of spatial AI workflows. Such workflows range from street imagery and 3D Lidar data collection, through Machine Learning object detection, algorithmic development of detected object positioning, and feature extraction for spatial database construction. We processed and analyzed more than 100,000 360° street photosphere images, producing approximately 750,000 cardinal images (45° field view each x 8 cardinal directions) in Orange County (Anaheim Hills and North Tustin areas). We used augmented CNN algorithms for detection of objects such as street signs and fire hydrants, to name a few, and a new positional triangulation algorithm detecting recognized object position. Our methodological workflows enable for the construction of automated big data spatial collections with important enhancements to local government geospatial and analytical workflows.
Keywords: artificial intelligence; object recognition; street signs; machine learning; deep learning; Artificial Neural Networks; deep Neural Networks; convolutional networks; photospheres; 360 panoramas; geospatial artificial intelligence

Alexandridis, K., Chanes, C., and Baptiste, U. (2019). Water Quality after Hurricanes Irma and Maria: US Virgin Islands. Paper at the National Drought and Public Health Summit 2019. Atlanta, Georgia, June 17-19, 2019, University of Nebraska Medical Center, College of Public Health.

Abstract: The presentation showcases the impacts to the US Virgin Island of the Hurricanes Irma and Maria on September 2017. We also present the results and analysis of our survey with local USVI residents, focusing on community attitudes, perceptions and dispositions over the water-energy-climate nexus. We finally present the results of cistern water quality sampling after the hurricanes and map participant locations, dispositions and other critical attitudes toward water and energy conservation.
Keywords: climate change; hurricanes; extreme events; disaster mitigation; climate change adaptation; natural resource management

Alexandridis, K., and Chanes, C. (2018). Developing Geospatial IoT for Weather and Microclimate Grid Tracking Applications in the US Virgin Islands. Paper at the 2018 AAG Annual Meeting, Developing Spatio-Temporal Data Collection Methods in Disaster Risk Reduction Session. New Orleans, Louisiana, April 13, 2018, American Association of Geographers.

Abstract: While global climate change represents one of key challenges in the 21st century, small coastal and island ommunities are among the most vulnerable and riskexposed. The geodemographic data in the US Virgin Islands indicate that proximity to coastal areas is correlated and/or associated with low income and inequality population groups. Developing intelligent environmental sensor and spatial monitoring applications achieve multiple goals: exposing communities and decision makers to the realities of contemporary environmental change; aiding visual and comprehensive understanding of the core science of spatial environmental and climate change, and aiding citizens and communities at large in the collective effort to adapt to change, thus reducing vulnerability and mitigating associated risks. We developed a truly integrated and intelligent Internet of Things (IoT) spatial-environmental sensor technology application using weather, environmental and locational GPS sensors. The development and deployment of microprocessors/microcontrollers (e.g., Raspberry Pi, Arduino), combined with cloud-based sensor data sensor storage technology (e.g., Azure, Amazon Cloud), along with real-time visualization and data analytics (based on machine learning algorithms), allow us to visualize and analyze high-density and high spatiotemporal resolution data. The applied research study demonstrates how low-cost IoT sensor technology can provide scientific support data that can enhance and increase spatial and temporal accuracy of micro-climatic variation of weather conditions. Using historical hurricane data from the IBTrACS NOAA database, we can demonstrate how we can derive spatial prioritization of areas with high probabilistic estimates of vulnerability for deploying such grid sensor technologies in the US Virgin Islands.
Keywords: spatial IoT; microclimate; climate change; Caribbean; GIS; geospatial applications; geographic information science

Chanes, C., Orange, B., Alexandridis, K., Morris, D., and Latesky, S. (2018). Water Quality Ambassador Program: Furthering the Understanding and Protection of Watersheds, Hydrology and Agriculture, and their Health Impacts in the USVI. Poster at the AAG Annual Meeting, Human Geography: Economic and Education Session. New Orleans, Louisiana, April 13, 2018, American Association of Geographers.

Abstract: In the US Virgin Islands, residents on all islands draw water from cisterns filled by the collecting rainwater off of rooftops while others are dependent upon groundwater sources. There is and continues to be a need for spatial data collection on the status of streams and watersheds and a better understanding of climate and its impact on agriculture which affects health and nutrition in the territory. The goal is to reduce nonpoint source pollution(NSP) of underground and surface water, bring to attention of the youth the importance of water quality and show them how that directly relates to location, food, water, overall health and climate in the region. This requires the building of a geographic network of weather stations that researchers at UVI, as well as the public would access. Lessons include health and nutrition and hydrology which then tie together to focus on water conservation and water quality. This engages youth in STEM concepts by working with the GeoCAS Institute’s geospatial technologies, web-mapping, geographic information science (GISci), and crowdsourcing GIS. More than 1,000 youth participated in programs relating to water quality to help them sustain their quality of life through better nutrition and need for expanded agriculture in the territory. The program supports STEM learning and allows youth to meet with and engage in Citizen Science projects while promoting higher education and science career pathways.
Keywords: spatial mapping; education; water resources; water quality; nutirion; climate change; weather; agriculture; 4H; extension

Alexandridis, K., and Chanes, C. (2018). Water Quality and Health Impacts of Hurricanes Irma and Maria in the US Virgin Islands on September 2017. Paper at the ASTHO Insular Area Climate and Health Summit. Ala Moana, Honolulu, Hawaii, May 29-31, 2018, May 29-31, 2018, Association of State and Territory Health Officials.

Abstract: The presentation showcases the impacts to the US Virgin Island of the Hurricanes Irma and Maria on September 2017. We also present the results and analysis of our survey with local USVI residents, focusing on community attitudes, perceptions and dispositions over the water-energy-climate nexus. We finally present the results of cistern water quality sampling after the hurricanes and map participant locations, dispositions and other critical attitudes twoard water and energy conservation.
Keywords: climate change; health impacts; water quality; hurricanes

Alexandridis, K. (2016). Integrated IoT Technology Systems for Intelligent Environmental and Climate Sensor Analytics. Invited Paper at the VI-EPSCoR Annual Conference 2016. St. Thomas, US Virgin Islands, March 2016, Virgin Islands Experimental Program for Stimulating Competitive Research.

Abstract: Science and technological innovation go hand-in-hand in leading toward a new and exciting era of intelligent and robust human-computer interactions. Environmental science applications are especially in the forefront of science and technology development. The magnitude and scale of environmental and climate changes affect more and more our societies at all scales: from our planetary boundaries, all the way to the smallest communities. In the face of climate change, small coastal and island communities are among the most vulnerable and risk-exposed ones. The development of intelligent environmental sensors and monitoring applications allows us to achieve a multitude of goals: exposing communities, and decision makers to the realities of contemporary environmental change; aiding visual and comprehensive understanding of the core science of environmental and climate change, and; aiding citizens and communities at large in adapting to change, reducing their vulnerability, and mitigating associated risks. In this presentation, we will demonstrate a truly integrated and intelligent Internet of Things (IoT) environmental sensor technology application using weather, environmental and locational GPS sensors. The development and deployment of microprocessors (e.g., Raspberry Pi, Arduino) and cloud sensor technology, along with a real-time visualization and data analytics (machine learning) environment will be presented. Finally, we will show how real-time, intelligent data sensor streams of environmental applications can help raise awareness and improve understanding of how technological innovations can lead our efforts for a sustainable and resilient natural environment.
Keywords: IoT; sensors; environmental science; human-computer interaction; climate change; weather data

Alexandridis, K. (2015). Do Community Attitudes, Beliefs and Behaviors towards Environmental Conservation matter in the US Virgin Islands? Invited Paper at the VI-EPSCoR Annual Conference 2015. St. Croix, US VIrgin Islands, May 29, 2015, Virgin Islands Experimental Program for Stimulating Competitive Research.

Abstract: This presentation provides an overview of community attitudes, beliefs and behaviors toward environmental conservation in the US Virgin Islands. We discuss issues of stewardship in relation to social-ecological health, the confinements and challenges on boundaries (sociocultural, sociotechnical, and institutional government). We explore cross-sectional participatory science approaches to environmental conservation. We present the results of community surveys regarding participant dispositions toward climate-water-energy and conservation nexus. We find that (a) citizens are already ready for change, well-aware and consistent in their attitudes and beliefs; (b) environmental knowledge cricically depends on local social networks and social interactions; (c) critical challenges exist for governance and formal institutions; (d) positive role of informal institutions and institutional arrangements at the community level. Thus, change cannot achievd without broader and wider institutional reform and shift in citizen dispositions.
Keywords: social-ecological systems; environmental stewardship; resilience; environmental sustainability; attitudes; beliefs; behaviors

Alexandridis, K. (2014). Co-creation of Sharable Visions Among Diverse Stakeholders for Complex Social-Ecological System Management. Invited Paper at the LEK First International Symposium, Knowledge Translation: Bridging Gaps between Science and Society, Keynote Session. Lecture Hall, RIHN, Kyoto, Japan, September 13-14, 2014, Research Institute for Humanity and Nature (RIHN), National Institute for Humanities.

Abstract: This study investigates the structural characteristics of socially-integrated environmental knowledge. The CAKS’ functioning follows the ways in which such knowledge is acquired, represented and acted upon. In turn, it depends on the ways we organize and structure knowledge in our social-ecological interactions and in our collective mental models. Environmental knowledge as a primal social construct is influenced by the ways we interact with our natural environment; our core and intrinsic set of values; our norms; our institutional arrangements; our social learning capabilities, and; the level and degree of our scientific understanding of the functions of our natural world and ecosystems. Knowledge is thus more than mere information processing. It integrates and embeds both qualitative and quantitative characteristics. Such characteristics in turn allow us to assign value and evaluate judgments, impose hierarchies and systemic structures, and interface them with ways to link knowledge to collective action, behavior and mental modes of functioning. Parts of our knowledge systems exist within our core social organizations and institutional structures, but other parts are well integrated within our cultural, sociological, psychological and socioeconomic composition of our local societies.
Keywords: social-ecological systems; integrated local ecological knowledge; semantic analysis; latent semantic analysis; semantic networks

Alexandridis, K. (2014). Semantic Knowledge Construction from the Ground-Up: from Discourse to Mental Models and the Potential for Role-Playing ABM. Invited Paper at the ILEK Project Mini-Workshop: Potential of Agent-based Modeling in Understanding Social-Ecological Systems. RIHN, Kyoto, Japan, July 24, 2014, Research Institute for Humanity and Nature (RIHN), National Institute for Humanities, Japan.

Abstract: Semantic methodologies allow us to construct mental model representation of knowledge networks embedded into narratives and natural language. It provides us with insight into important and fundamental relationships among concepts and ideas that form the basis of our knowledge system in socially implicit situations and settings. While important in providing a framework and explore the complexity of relationships and connections that form our knowledge systems, they remain representations of the realities a nd discourses reflected in the data that generated them. Agent based models, probabilistic and/or statistically explicit formulations of hypotheses can greatly benefit further our exploration. Specifically those related to the nature and structure of knowledge, the broadening of our inferences and our ability to provide generative assumptions and hypotheses on the relationship between environmental knowledge and conservation action. Finally, ABM can provide a meaningful pathway on connecting community and participatory local environmental knowledge with scientific analysis and theory.
Keywords: agent-based modeling; semantic analysis; latent semantic analysis; semantic network; social-ecological systems


Alexandridis, K. (2014). Knowledge and Adaptation Pathways for Environmental and Climate Change Research. Invited Paper at the VI-EPSCoR Annual Conference. St. Thomas, US Virgin Islands, May 12, 2014, Virgin Islands Experimental Program for Stimulating Competitive Research.

Abstract: We need to understand the power of knowledge narratives in providing pathways and strategies to adaptation. The critical role of bi-directional science translation includes aiding visualization and understanding of future impacts, providing heuristics and related choice-outcome mappings, engaging communities in fostering dialogue, discourse and stewardship, and systematize and internalize collective cohesion for adaptation. Social-ecological knowledge in such context involves both quantitative dimensions (i.e., objectivelly and directly measured knowledge), and qualitative ones (i.e., measuring subjective and latent dimensions of knowledge). Thus knowledge is clearly a social construct, i.e., cannot exist without the social substrate that generates, maintains and aids its evolution. What follows: beyond knowledge itself, we must study associative processes in social-ecological systems: knowledge-conditioning processes; knowledge-brokerage processes; knowledge-mediative processes; knowledge-reflective processes; knowledge-affective processes, and knowledge-discoursive processes. We present a number of project outcomes and inferences in support of our propositions.
Keywords: social-ecological system; knowledge; Integrated local ecological knowledge; climate adaptation

Alexandridis, K. (2013). The Inspirational Power of Global Science for Healthy and Resilient Local Communities. Invited Keynote Luncheon Talk at the Seventh Annual National Conference on Health Disparities: Reducing Health Disparities Through Sustaining and Strengthening Healthy Comminities, Student Forum Session. Sugar Bay Resort, St. Thomas, US Virgin Islands, November 13-16, 2013.

Abstract: We face many contemporary environmental challenges. Our way of living affects our natural environment, which in turn affects our quality of life, our wellbeing and happiness, and inevitably, our health. The 1970's sparcehsip earth paradigm has ignored a number of critical environmental challenges, including increased environmental inequalities and a divergence set of realities. From global to local scales, multiple realities make it difficult to achieve unified solutions. The global indicators and their distributions show the rise of global vulnerabilities, i.e., how most vulnerable social and economic groups, communities and people are affected the most. For example, the rise of the Gini index of income inequality shows how often we fail to realize the complexities of our contemporary world. Science can help societies realize the power of their aspirations, utilize their traditional and local knowledge, help achieve a better level of stewardship and maintain a more sustainable and equitable world. The presentation ends with the assertion that while the relationship between livelihoods, wellbeing and happiness is far from simple, science has a critical and fundamental role to play in inspiring adaptive change towards sustainability in an interconnected world.
Keywords: resilience; community-based conservation; science

Alexandridis, K. (2013). Exploring the Heuristics of Knowledge Narratives – A Latent Semantic Analysis. Invited Paper at the 2013 ILEK Theory Group Meeting. Ishigaki Kenko Fukushi Center, Ishigaki and Okinawa, Japan, July 27-29, 2013, Research Institute for Humanity and Nature (RIHN), National Institute for Humanities, Japan.

Abstract: knowledge narratives represent a collective achievement. More likely than not the emergence of knowledge within a society is structured around a necessary level of stewardship and community discourse. The structure of the knowledge is heuristic, i.e., encodes volatility and uncertainty at levels that render necessary it’s probabilistic and probabilogic estimation. Any measured levels and structural characteristics of knowledge reflect simply a quantifiable latent instantiation of the true knowledge embedded within the human communities and societies under study. Latent semantic analysis present one of the methodological approaches for estimating the essence and mechanisms of prevalence in knowledge through collective narrative discourses. It reduces the dimensions of qualitative narratives to simple measures of association between mental representations and ideas contained within such narratives. As a result allows the statistical and probabilistic estimation of (a) the strongest and more likely to persist ideas and concepts forming a collective knowledge construct, and (b) the evaluation of the strength of the structure in knowledge connectivity. Such heuristic interpretation of latent knowledge narratives allows us to look at how citizens and community groups connect ideas and mental representations (perceptions, attitudes, beliefs) together to form a shared understanding of the past, present and future. It also allow us to ask key questions and form hypotheses about the strength and prevalence of the components forming a knowledge system, especially how such system interacts within and across groups, communities or key stakeholders.
Keywords: social-ecological systems; Integrated local ecological knowledge; heuristics; semantic networks; latent semantic analysis

Alexandridis, K. (2013). Participatory Reflections in Climate Change Adaptation Scenarios: A Social-Ecological Systems Perspective. Invited Paper at the USVI Climate Change Ecosystem-Based Adaptation Workshop: Allowing for Resilient Communities. Emerald Beach Resort, Lindbergh Bay, St. Thomas, US Virgin Islands, June 4-5, 2013, The Nature Conservancy.

Abstract: Climate adaptation efforts in the Caribbean face a number of challenges such as high level of risk exposure, limited adaptation options, and high dependency on externalities. The nature of historically unprecedented climate change realities, along with the nature of uncertainty within our cognitive and social perceptions, renders critical the role of science and evidence-based approaches to climate adaptation. Within such solution space, the power of narative human knowledge enables and fosters our ability to benchmark, i.e., successfully identifying drivers and heuristic determinants of past and future changes. Social-ecological resilience pathways include understanding of coping mechanisms, social learning for resilience, and mechanisms for adjusting and adapting to change. We present results of a climate adaptation scenario dataset. Our key inferences indicate that (a) climate adaptation have different meaning for different actors and groups; (b) without achieving local community cohesion and stewardship, adapting to climate change will necessarily be a win-lose proposition; (c) the people who can afford and can be more influential in shaping adaptation responses are the least interested and likely to adapt; (d) marginalized social groups are pulling most of the social weight of adaptation, while suffering the most economic consequences and are among the most vulnerable, and; (e) choices and pathways must be chosen carefully and from the bottom up to ensure equitable distribution of costs and benefits related to adaptation.
Keywords: climate change; ecosystem-based approach; social-ecological systems; environmental sustainability; resilience; participatory approaches

Alexandridis, K., Ragster, L., and Webb, A. (2013). ILEK SERV: Social-Ecological Resilience Visioning in US Virgin Islands and the Caribbean region. Invited Talk at the RIHN ILEK 2nd Project Meeting Workshop. Mote Marine Laboratory, Sarasota, Florida, May 7-9, 2013, Research Institute for Humanity and Nature (RIHN), National Institute for Humanities, Japan.

Abstract: Managin our environmental commons presents a few challenges in the areas of governance and policy, institutional changes, real-world conditions, elusive solution spaces, complexity of interactions and multiple knowledge constrains. Our research goals include the need to (a) operationalize knowledge as a process and a structure for managing the commons (Knowledge Acquisition); (b) explore collective social knowledge related to the drivers of change within a social-ecological system of interactions (Knowledge Representation); (c) assess the capacity of diverse groups to share a common vision of sustainability within St. Thomas, USVI (Knowledge Representation), and; (d) identify knowledge dissipation as a mechanism in bridging local knowledge, institutional capital, and governance (Knowledge Diffusion). We use scenario planning focus group exercises and variation of q-methodology to acquire and analyze qualitative narrative data and transform these visioning narratives into semantic knowledge networks. We presents our analysis results over five focus group exercises in St. Thomas, US Virgin Islands.
Keywords: social-ecological systems; environmental sustainability; resilience; social resilience

Alexandridis, K. (2013). How Critical is the Role of Human Dimensions and Social-Ecological Resilience in Understanding Environmental Transformations in the US Virgin Islands? Invited Paper at the VI-EPSCoR Annual Conference. St. Thomas, US Virgin Islands, April 13, 2013, Virgin Islands Experimental Program for Stimulating Competitive Research.

Abstract: Human dimensions in natural resource management and policy represents an emerging area of scientific research internationally. Linking social-ecological systems and the coupling of natural and human factors in areas with critical ecosystems is in the core of the science informing environmental sustainability and resilience. Among the most pressing issues at hand is the need to understand, study and analyze how individuals, human communities and societies respond, adapt and mitigate environmental changes and transformations. From human cognition, attitudes and perceptions, to collective knowledge and learning, through to decision and policy making, human dimensions affect and being affected in fundamental ways by environmental transformations including climate change, marine and landscape ecosystem degradation, and depletion of our natural resources. This presentation will provide an overview of some of the key inferences and findings informing our understanding of human dimensions research in marine and environmental social-ecological systems in the US Virgin Islands. It will also look at areas of research for which attention has been given and areas where additional research is needed to understand sustainability and resilience transformations for the future of our local and regional social-ecological systems.
Keywords: social-ecological systems; resilience; social resilience; environmental change; human dimensions

Webb, A., and Alexandridis, K. (2013). Community Perspectives on Sustainability and Resilience within a Social-Ecological Paradigm in St. Thomas, USVI. Poster at the UVI Research Day 2013 Conference. St. Thomas, US Virgin Islands, April 6, 2013, University of the Virgin Islands.

Abstract: One of the primary difficulties in understanding and managing for environmental sustainability within a systems context is the concept of uncertainty or surprise. However; uncertainty is an inherent component in all complex adaptive systems, such as a social-ecological system, and therefore should be embraced and anticipated . Managing for environmental sustainability then, includes not only an understanding of the normative values contextualizing it but also the ability to identify and promote resilient solutions in the face of an ever changing and dynamic world. This research project seeks to add to the rapidly growing discipline of sustainability science by examining the multidimensionality of collective community visions for the future. It will also investigate the mechanisms and processes that inform those visions. Analysis of the data will incorporate a hypothetical metal-learning framework.
Keywords: social-ecological systems; environmental sustainability; complex adaptive system theory

Engerman, K., Alexandridis, K., Drost, D., Michailidis, S., Ramsey, L., Huggins, D., Joseph, C., Mercer, D., and Brim, T. (2013). Education Research Grant: The Use of Creative Problem Solving as Curriculum Enhancement to Improve Cognitive, Behavioral and Social Transformation in STEM Retention. Poster at the UVI Research Day 2013 Conference. St. Thomas, US Virgin Islands, April 6, 2013, University of the Virgin Islands.

Abstract: Studies have shown that creative problem solving techniques have been effective in improving students’ problem solving skills in educational settings (Torrance, 1972; Torrance & Presbury, 1984; and Parnes & Brunelle, 1967). Furthermore, Fox (2005) presented preliminary evidence that taking one creative problem solving class increased the likelihood that education students would graduate college by over 70%. For this reason, the overall aim of this project is to see how this increase in retention as a result of creative problem solving can be replicated in STEM fields. More importantly, the degree to which a high percentage of non-STEM students having an interest in pursuing a STEM career will also be examined. Finally, the project will expose how cognitive factors (career aspirations in STEM fields, and attitudes and beliefs about STEM), social factors, (peers, family, and institutional) and behavioral factors (selecting STEM as a major, and remaining in STEM) may be molded or is molded by the effectiveness of creativity training. The specific objectives of the project are as follows: (1) administer and assess the impact of creative problem solving on academic performance of students; (2) assess the degree to which cognitive, social, and behavioral factors impact or is impacted by the efficacy of creative problem solving; and (3) provide creative problem solving skills so students can continue to use the techniques after they leave SCI 100.
Keywords: educational research; college retention; academic performance; STEM; creative problem solving; creativity

Engerman, K., Alexandridis, K., and Huggins, D. (2013). Undergraduates’ Perceived Peer Academic Support. Poster at the UVI Research Day 2013 Conference. St. Thomas, US Virgin Islands, April 6, 2013, University of the Virgin Islands.

Abstract: Retention of students in STEM education has been identified as a problem nationwide. Studies have shown that social support is a key factor in retention. Therefore, the objective of this study is to describe the academic support received from peers for students enrolled in a first year science course at a university. A self-report questionnaire was used to collect data on undergraduates’ perceived academic peer support. Academic peer support was measured in the form of four factors: (1) informational; (2) esteem; (3) motivational; and (4) venting support. Participants reported receiving no support, little support, or lots of support. Results indicate that 59.64% of participants received little informational support; 47.04% received little esteem support; and 36.75% receive lots of motivational and venting support. The following percentage of participants reported receiving no peer support: 12.18% for informational; 18.62% for esteem; 29.4% for motivational; and 27.93% for venting. In conclusion, since college can be stressful at times, peers provide motivation and a forum for venting. Therefore, having that support system in place is a factor that can lead to retention.
Keywords: educational research; STEM; college retention; peer support

Ramsey, L., Engerman, K., and Alexandridis, K. (2013). Perceived Family Support and Creative Ability on College Persistence. Poster at the UVI Research Day 2013 Conference. St. Thomas, US Virgin Islands, April 6, 2013, University of the Virgin Islands.

Abstract: College retention at many universities or colleges has been known to fluctuate over the past few years. Due to this fluctuation, task forces were established to combat low college enrollment, retention and graduation rates. The formations of new student organizations and student centers have also been initiated to help students adjust and feel a sense of belonging as they pursue their college studies. Yet, college retention has not improved. According to Bronfenbrenner’s social-ecological model, behaviors, attitudes, and beliefs are determined by the functioning of multiple systems. These multiple systems include one’s family, school, peer, neighborhood, and culture. Based on Bronfenbrenner’s model, researchers can infer that family support can be an influencing factor on whether students stay enrolled in college and yield better academic outcomes. This study looks to investigate if perceived family support and creative ability among college students is associated with academic success and college persistence. Experimental sample data were collected from undergraduate students enrolled in Science 100 (SCI 100) on the St. Thomas Campus for the period 2010-2012. Self-report survey was used to collect data on students’ attitudes and beliefs about family relationship. The Torrance Test for Creative Thinking was used to measure creative processing and skills. Finally academic records were accessed and correlate with survey responses. It is hypothesize that student’s perception of the role of their family and creative abilities will reveal a statistically significant relationship to their persistence in college. The study is vital in the investigation of improving college retention as it explores a complex interaction between situational and dispositional forces.
Keywords: educational research; retention; STEM; family support; social science; attitudes; beliefs; qualitative analysis

Martin, S., Engerman, K., and Alexandridis, K. (2013). An Observational Study of Gender and Peer Interactions. Poster at the UVI Research Day 2013 Conference. St. Thomas, US Virgin Islands, April 6, 2013, University of the Virgin Islands.

Abstract: This study seeks to investigate the role gender plays in peer interactions in the classroom. While many researchers have studied interactions between students in small groups as a whole, the inner workings of group interactions and how peer interaction is influenced by gender is still not adequately understood. Within a group, peer interactions include students that display helping behavior, active behavior or passive behavior. Helping behavior in the classroom can be bifurcated into receiving help or giving help and a subset of helping behavior is active behavior (Webb, 1982). In order to investigate the role of gender on peer interactions in the classroom, participants were videotaped in SCI 100 in groups while completing creative thinking skills assignments. These thinking skills entail assignments that prompted students to come up with creative solutions to different scenarios while interacting with each other. Classroom observations were analyzed, recording each within group interactions and across group interactions. Each interaction was categorized by gender as receiving help, giving help, passive behavior or active behavior. The findings of this study will help researchers further understand the influence of gender and roles on peer interactions in the classroom.
Keywords: educational research; gender; STEM

Joseph, C., Alexandridis, K., Michailidis, S., Engerman, K., and Drost, D. (2013). Regional Identity and its Relationship to Creative Ability. Poster at the UVI Research Day 2013 Conference, Poster Session. St. Thomas, US Virgin Islands, April 6, 2013, April 6, 2013, University of the Virgin Islands.

Abstract: The concept of creativity is a subject of scientific discourse in the past few decades, since academic, educational and organizational bodies have theorized about its importance. Creative ability has been linked to many aspects of life and is associated with a multitude of other positive concepts such as intelligence, achievement, motivation, etc. Furthermore, Kaufman (2010) added that people who are creative are more likely to have better physical health and a higher state of general well-being. Therefore, this study explores regional identity and its influence on one’s ability to generate unique ideas. Social norms and cultural values that place an emphasis on harmony and fitting in can also limit people’s sense of free expression and negatively impact originality (Ivcevic, 2009). Thus, this study hypothesize that students’ regional identity can influence creative ability. It examines students measured levels of creative thinking ability and its relationship to their social and environmental background. Pre- and post- assessments were administered to students in UVI’s Science 100 classes between Fall 2011 to 2012. The Torrance Test for Creative Thinking (TTCT) was utilized to measure students’ creative ability. The TTCT evaluates students’ fluency, originality, elaboration, titles, creative strengths and resistance to closure. Self-reported statements on survey and interviews documented participants’ regional identity. Only the pre-test of the TTCT was utilized and composite scores were recorded and ranked based on a creativity index and national percentile. The results can further add to the literature on the importance of creativity in everyday life.
Keywords: educational research; creativity; creative problem solving; identity

Huggins, D., Alexandridis, K., and Engerman, K. (2013). The Interrelationship between Time Perspectives, Motivational Factors and Perceived Familial Support in Participants’ Educational Experience. Poster at the UVI Research Day 2013 Conference, Poster Session. St. Thomas, US Virgin Islands, April 6, 2013, April 6, 2013, University of the Virgin Islands.

Abstract: Factors which contribute to motivation, the effects of time perspectives and the effects of perceived familial support have all been studied and examined in a variety of contexts. The nature of an interrelationship of the three, however, is an area that needs further exploration. This study is important to determine if social and cultural aspects, such as motivation and perceived family support. have any association with time perspective and the impact of there association academically. A study of the cognitive, behavioral and social influences of STEM retention on college students at the University of the Virgin Islands have yielded impactful data on time perspectives, motivation and support of the family (Huggins, Alexandridis & Engerman, 2011). We measured temporal perspectives of student’s thinking using the Zimbardo’s Time Perspective Inventory (ZTPI) and the Transcendental-Future Time Perspective (TFTP). Textual data were gathered from group interviews and surveys on motivational factors and institutional data including grades and GPA were also collected. The pairwise interaction and overall three way interactions of time perspective, motivation and perceived familial support were explored. Through previous studies and preliminary analysis of the data, it is hypothesized that an interrelationship exists both pairwise and among all three factors which have an effect on academic functioning and educational experiences. Findings from this research can help college academic centers improve persistence.
Keywords: time perspective; family support; STEM; educational research

Mercer, D., Brim, T., and Alexandridis, K. (2013). The Necessary Relationship Between Persistence and Institutional Satisfaction. Poster at the UVI Research Day 2013 Conference. St. Thomas, US Virgin Islands, April 6, 2013, University of the Virgin Islands.

Abstract: The ultimate goals of every person entering college are to graduate and earn their diploma. Earning a college degree enables enhanced career, financial and employment opportunities for the graduates. Data from the Bureau of Labor Statistics indicate that the unemployment rate for people with a Bachelor’s degree is 4.5% whereas if a person solely has a high school diploma the rate is 8.3%. Similarly, on average, a person with a Bachelor’s degree earns $414 dollars more per week than a person with a high school diploma. However, everyone that goes to college does not graduate. According to U.S. Department of Education, the national college graduation rate is 57.3%. Black men and women graduate from college at a rate of 33.1% and 44.8% respectively. Persistence and institutional satisfaction as important factors associated with retention and graduation rates were looked at by the researchers. The more satisfied a student is with her/his academic institution, the more likely they are to persist in subsequent semesters, perform better academically, and ultimately graduate from college. Both quantitative academic and qualitative satisfaction criteria as potential factors influencing retention and graduation rates at the University of the Virgin Islands were looked at by the researchers. A mixed research methods approach was used by the researchers, and focused on the factor composition and the factor interactions as influencers of academic performance, retention and graduation rates. The importance of this research rests on the proposition that institutional academic interventions aimed at improving satisfaction and performance can have a beneficial effect to college and graduation rates at the University of the Virgin Islands.
Keywords: educational research; institutional analysis; STEM

Etienne, N., and Alexandridis, K. (2013). Data Mining, Database Design for Qualitative Research Analysis. Poster at the UVI Spring Research Symposium. St. Thomas, US Virgin Islands, April 2013, University of the Virgin Islands.

Abstract: Qualitative research methods have increasingly providing a means for gathering and analyzing a wealth of data. Such data may include social interactions, computer interactions, blogs or even information from websites. With the increased availability of these data for computational scientists comes the need for advanced tools to analyze the complex structure. Data mining has the ability to transform raw fieldwork (qualitative) data into transformed databases (advanced) through the use of mining statistics and mathematical/computational algorithms. Data mining is an interdisciplinary scientific field which refers to the process of extracting and analyzing the content and value of information from existing datasets and databases. From loosely connected data, variables and information, associations are made through text mining (extracting or categorizing information within semantic categories from raw free textual information) and identifying statistically important associations and connectivity between attributes, among attribute states, and across states and attributes in the databases. This results in the discovery of patterns of interconnectivity within and across data variables and their states. This research looks at the design and implementation of advanced databases which associates and transforms qualitative information to quantitative for mining hidden patterns and relationships. Such are implemented using SQL language queries in corresponding SQL databases as well building the algorithmic tools necessary to discover, model and associate states and variable parameterizations. The proposed integrated database/analysis framework demonstrates the functionality of the database and its analysis models. The importance of this research includes: benchmarking in the development of a modeling platform for more advanced scientific inferences (especially in mixed qualitative and quantitative research methods); facilitating the emergence and discovery of complex patterns of associations within existing data structures, and linking parametric with non-parametric inference using machine learning and data mining tasks. Such analysis can aid the discovery and mining of complex patterns of relationships especially in large and seemingly unrelated relational database structures. Finally, it is useful for generating useful analytical data (Real world data).
Keywords: data mining; database; Qualitative analysis

Alexandridis, K. (2013). Studying Connectionism Interactions and Collective Knowledge Representation for Social-Ecological Stewardship. Invited Paper at the Department of Anthropology Seminar Series. Tampa, Florida, February 6-9, 2013, University of South Florida.

Abstract: In an era of connectionism achieving collective natural resource stewardship and resilience transformations depends critically on the nature and complexity of interactions within a social-ecological system. The ability of our communities and local societies to respond, adapt and adjust to critical environmental changes is inevitably linked to our collective capacities and capabilities to connect to each other, and link our cognitive, social and institutional inferential and decision-making mechanisms with the state and nature of our linked ecosystems. Transforming information into knowledge, and knowledge into action in sustainably governing our commons can only be achieved by enhancing stewardship, promoting evidence-based science, and deepening our collective learning in understanding the linked interactions, connections and feedback mechanisms that either exhibit or inhibit resilience. This seminar will provide a survey of key issues, inferential mechanisms and factor groups that are associated with both successes and failures in achieving sustainable and resilient pathways. It will also provide support for the role of guided self-organization in achieving collective social knowledge transformations for social-ecological resilience.
Keywords: social-ecological systems; environmental change; connectivism; decision-making; environmental stewardship

Alexandridis, K. (2013). The Role of Bayesian Probabilogic Inference in Assessing Human Dimensions in Coupled Social-Ecological Systems. Invited Keynote Paper at the ISBA Regional Meeting and International Workshop/Conference on Bayesian Theory and Applications (IWCBTA). Banaras Hindu University, Varanasi, Uttar Pradesh, India, January 6-10, 2013, International Society for Bayesian Analysis.

Abstract: Human dimensions in coupled human-natural or social-ecological systems are characterized by deep uncertainty, a very high level of volatility related to human judgmental heuristics, and a number of critical future changes that have never been experienced before by human societies and communities around the world. Bayesian approaches and probabilogic inferential dynamics allow for capturing key systemic characteristics of complexity in such changes. The character and nature of Bayesian inferential dynamics enable the exploration of key unobserved outcomes and “black swan” type of social-ecological events within a complex adaptive systems inferential framework. Bayesian inference enables us to go beyond the boundaries of rationality in traditional decision-theoretic frameworks and explore social-ecological dynamics from a computational psychology, computational linguistics and data mining perspectives to Bayesian modeling. Simulation and modeling of key socio-ecological network dynamics (social, belief, semantic and physical networks) enables the capturing of collective social mechanisms, mental model dynamics (both at the cognitive and social levels of inference), knowledge and informational mental structures, as well as geographical or biophysical configurations that affect and being affected by inferential mechanisms. Finally, Bayesian inference enables capturing key mechanisms that influence the prevalence and direction of multiple social and ecological systems as well as their feedback mechanisms.
Keywords: bayesian analysis; bayesian belief networks; human dimensions; probabilogic inference; bayesian inference; social-ecological systems

Alexandridis, K., and Webb, A. (2012). Navigating an Ocean of Complexity: Collective knowledge, resilient livelihoods, and social-ecological stewardship narratives in coastal Caribbean. Invited Paper at the JST-RISTEK and RIHN-ILEK Joint Kick-off Symposium: Knowledge Production, Action and Adaptive Governance for Local Communities. Kyoto, Japan, September 16-17, 2012, Research Institute for Humanity and Nature (RIHN), National Institute for Humanities, Japan.

Abstract: This paper provides an overview of human dimensions as a multi-layered reality involving cross- and inter-systems interactions. It makes the case for an inductive paradigm for research arguing that complex problems necessitate complex solutions. It addresses the depth and width of complexity, the need for complex solution spaces, the need for more accurate and performative solutions, and addressing the internal complexity, incomplete information and stochastic approximation to elusive solutions. It argues that there is a need for contextual and scientific nderstanding of knowledge structures that goes beyond myths and urban legends. The approach tackles key and fundamental aspects of human dimensions, specifically, (a) cognitive: how we understand ourselves; (b) perceptions: how we understand our environment and others around us; (c) behaviors: how we act and how we make decisions, and; (d) social learning: how we learn from impacts and consequences. It makes a case for the need for participatory knowledge co-management and empowerment, and uses examples of research methodologies for knowledge-driven science and policy, as well as providing methodological considerations for integrated knowledge processes.
Keywords: social-ecological systems; knowledge; Integrated local ecological knowledge; complex systems

Alexandridis, K. (2012). Knowledge Visioning as a Complex Social-Ecological System Metaphor. Invited Paper at the ILEK Project Meeting. Kyoto, Japan, July 21-23, 2012, Research Institute for Humanity and Nature (RIHN), National Institute for Humanities, Japan.

Abstract: This paper provides an overview of knowledge as a metaphor vof envisioning complex social-ecologica systems. It addresses issues of hidden complexity in cross-scale and cross-system interactions, and the emergence of cognitive and social psychology paradoxes and social ans institutional challenges of managing the complexity in social-ecological systems. It discusses the presence of endogenous deep uncertainty and incoplete information in environmental decision-making, sensitivities to initial conditions, and a series of complex system attributes (multiple thresholds, phase transitions, tipping points, complex attractors, emergence, etc.). We provide an integrated systemic assessment of knowledge transitions (transmitting vs. emitting processes in knowledge acquisition, distribution and representation). A case study in the STEER watershed of St. Thomas East End Reserve is used to showcase multiple human dimension challenges, such as user participation from the ground-up, whose reality counts, externalities, nested perceptions of reality, policy-driven science vs. science-driven policy issues, horizontal vs. vertical translations of knowledge and knowledge transformations. Finally, we provide a futures visioning space exploration and address the issues of resource dependence in local ecological knowledge, using latent semantic analysis and complex semantic networks.
Keywords: integrated local ecological knowledge; social-ecological systems; resilience; semantic networks

Alexandridis, K., Engerman, K., Drost, D., Michailidis, S., Ramsey, L., Huggins, D., Kobrinski, E., Joseph, C., Mercer, D., and Brim, T. (2012). Associating Creativity, STEM Retention, and Cogntive, Social, Behavioral and Time Perspective Transformations in Higher Education. Poster at the 2012 Joint Annual Meeting (JAM) of the National Science Foundation. Washington, DC, June 12-15, 2012, National Science Foundation.

Abstract: Studies have shown that creative problem solving (CPS) techniques have been effective in improving students’ problem solving skills in educational settings. The overall aim of this research is to investigate how increase in retention as a result of creative problem solving can be replicated in STEM fields. The study exposes how cognitive factors (career aspirations in STEM fields, and attitudes and beliefs about STEM), social factors, (peers, family, and institutional) and behavioral factors (selecting STEM as a major, and remaining in STEM) may be molded or is molded by the effectiveness of creativity training. Finally the study looks at the concept of student Time Perspectives from a socio-cultural approach to HBCU STEM UVI education and explores the influence of such associative factors and factor interactions in contributing to student success and STEM retention.
Keywords: educational research; STEM; student retention; creativity; creative problem solving; student success

Webb, A., and Alexandridis, K. (2012). Participatory Community Perspectives of Environmental Sustainability and Social-Ecological Resilience in US Virgin Islands. Poster at the UVI Research Day 2012 Conference, Poster Session. St. Thomas, US Virgin Islands, April 14, 2012, University of the Virgin Islands.

Abstract: Due to an unprecedented human presence and influence on the earth’s natural resources and processes the environmental sustainability and resilience of many areas is uncertain. This study will examine the collective perceptions of stakeholder and institutional groups, related to natural resource use, on the drivers that influence environmental sustainability and social resilience within the US Virgin Islands. The approach will use Social-Ecological Systems (SES) theory and involve a two-phase research methodology: (a) Participatory scenario planning to uncover the perceived drivers impacting sustainability and social-ecological resilience as well as examine the presence (or absence) and strength of a cohesive community vision regarding social-ecological stewardship, and (b) Game theoretic Role Playing Games (RPG) in which participants will engage in strategic collaborative decision making in exercises using scenarios identified during the participatory scenario planning phase. The benefits and broader impacts of this research are the identification, understanding, and in-depth analysis of real-world and place based emergent properties of an integrated social-ecological system of interactions; exploring and analyzing the multi-dimensionality of a diverse and integrated set of alternative collective social and mental perspectives; and, the provision of an evidence-based decision support mechanism to aid resource managers, end users and the broad community in the USVI and the Caribbean region. Funding for this research is provided by NSF VI-EPSCoR award no. 0814417.
Keywords: social-ecological systems; environmental sustainability; resilience; participatory; community

Ramsey, L., Engerman, K., and Alexandridis, K. (2012). College Student’s Time Perspectives and their Relationship to Academic Outcomes. Poster at the UVI Research Day 2012 Conference. St. Thomas, US Virgin Islands, April 14, 2012, University of the Virgin Islands.

Abstract: One of the most influential and silent force in one’s life is the notion of time. Time has a profound way in guiding our actions, thoughts, and emotions. Phillip Zimbardo spent many years investigating the significance of time, and how it is perceived by many. For more than ten years of conducting research in the field of psychology of time, he has coined the idea of time perspectives. Zimbardo and colleagues noted that there are six most common time perspectives in the western hemisphere. These perspectives include the past negative, past positive, present hedonistic, present fatalistic, future, and transcendental-future time perspective. The relevant literature support the proposition that one’s time perspective is reflected in her/his attitudes, beliefs, and values. The research presented here aims to identify, quantify, and evaluate the time perspective distribution among the University of the Virgin Islands’ students. The general goal is to investigate how cognitive and social perceptions, beliefs and student aspirations affect and being affected by their academic pursuits. Experimental sample data are collected from the UVI’s Science 100 students in the St. Thomas Campus for the period 2010-2012. To access the study participants’ construct of time researchers are administering the Zimbardo Time Perspective Inventory (ZTPI), and the Transcendental-Future Time Perspective Inventory (TFTPI). Academic pursuit goals are explored through studying student formal academic performance, educational plans and aspirations, retention rates, and broader social-economic factors. Preliminary results indicate the importance of time perspectives in social, cultural, and educational/academic interactions and vice versa. Furthermore, the relationship between time perspectives, socio-cultural and cognitive characteristics, reflects a complex system of associations with important implications for student academic outcomes and success. Funding for this research is provided by NSF/HRD ERP award no. 1036183.
Keywords: educational research; STEM; retention; academic achievement; time perspective

McKayle, C., Alexandridis, K., Liburd, K., and Burke, C. (2012). Roundtable: Insight and Action for Student Success. Roundtable at the UVI Research Day 2012 Conference. St. Thomas, US Virgin Islands, April 14, 2012, University of the Virgin Islands.

Abstract: Increasingly, students express a strong desire to obtain a college degree. Although these students possess the motivation needed to succeed; some lack appropriate academic preparation. This results in considerable attrition between the first two years of study at universities. Attrition affects both the institution and students. Institutions are unable to plan adequately because of the loss of tuition, fees, and potential alumni contributions. Moreover, students’ failure to graduate with a degree results in earning a significantly less income than one who graduated. Therefore, universities are actively engaged in research to identify underlying factors that contribute to retention. Additionally, universities are developing intervention programs to enhance academic achievement and social adjustment. This roundtable will explore how the University of the Virgin Islands is addressing the issue of retention and student success through three separate efforts: Junior University; the Center for Student Success; and the National Science Foundation, Education Research Project.
Keywords: educational research; student success; STEM

Kobrinski, E., and Alexandridis, K. (2012). Experiential Social Learning for Coral Reef Resilience. Poster at the UVI Research Day 2012 Conference. St. Thomas, US Virgin Islands, April 14, 2012, University of the Virgin Islands.

Abstract: The purpose of this research is determine if there are any changes in attitudes, beliefs and behaviors from experiential social learning after visiting a coral reef, and how that learning experience relates to coral reef conservation efforts, increasing coral reef resilience. This study is expanding upon a similar project that is taking place at the University of the Virgin Islands (Alexandridis et al. 2010). While the study at the University involves the experiential participation of undergraduate students, this study expands on the same dimensions of the research into the local community, similarly evaluating participants that are embarking on snorkeling trips at ecotourism business establishments in the U.S. and British Virgin Islands. A number participants in ecotourism activities in the US Virgin Islands (St. Thomas, St. John and St. Croix), as well as the British Virgin Islands (Tortola, Virgin Gorda) were surveyed and ecotourism business establishment managers were interviewed. Our results show that experiential learning in marine and environmental ecotourism activities enhances participant conservation attitudes and dispositions at varying degrees. We found that socioeconomic and demographic factors influence the level of conservation-based attitudes, and that increasing opportunities for local participation in such activities promotes and fosters attitudinal and dispositional changes with respect to environmental conservation. We argue that social-ecological sustainability and resilience depends on the level of stewardship between conservation-based ecotourism activities and local and/or regional perspectives in sustainable development. Funding for this research is provided by NSF VI-EPSCoR award no. 0814417.
Keywords: experiential learning; social learning; social-ecological systems; social resilience; environmental change; climate change; ecotourism; natural resource management; social science; attitudes

Engerman, K., Alexandridis, K., and Ramsey, L. (2012). Relationship between Peer Academic Support and Creative Problem Solving Skills. Poster at the UVI Research Day 2012 Conference. St. Thomas, US Virgin Islands, April 14, 2012, University of the Virgin Islands.

Abstract: Retention of students in science, technology, engineering and mathematics (STEM) education programs has been identified as a problem nationwide. Studies have shown that social support is a key factor in retention. Additionally, creative problem solving techniques have been effective in improving students’ problem solving skills in educational settings. Therefore, the objective of this study is to describe the relationship that exists between academic support received from peers and creative problem solving skills. Undergraduate students enrolled in Science 100 participated in the study. Science 100 is a mandatory course for all first year students enrolled in the university. Self-report questionnaire was used to collect data on perceived academic peer support. Academic peer support was measured in the form of four factors: (1) informational; (2) esteem; (3) motivational; and (4) venting support. The Torrance Test for Creative Problem Solving was used to assess creative problem solving skills. This paper will discuss the relationship between peer support and creative problem solving skills and how it could possibly affect retention. Funding for this award is provided by NSF/HRD ERP award no. 1036183.
Keywords: educational research; STEM; peer support; creativity; Creative problem solving

Engerman, K., Alexandridis, K., Drost, D., Michailidis, S., Ramsey, L., Kobrinski, E., Joseph, C., Mercer, D., and Brim, T. (2012). Education Research Grant: The Use of Creative Problem Solving as Curriculum Enhancement to Improve Cognitive, Behavioral, and Social Transformation in STEM Retention. Poster at the UVI Research Day 2012 Conference. St. Thomas, US VIrgin Islands, April 14, 2012, University of the Virgin Islands.

Abstract: Studies have shown that creative problem solving techniques have been effective in improving students’ problem solving skills in educational settings (Torrance, 1972; Torrance & Presbury, 1984; and Parnes & Brunelle, 1967). Furthermore, Fox (2005) presented preliminary evidence that taking one creative problem solving class increased the likelihood that education students would graduate college by over 70%. For this reason, the overall aim of this project is to see how this increase in retention as a result of creative problem solving can be replicated in STEM fields. More importantly, the degree to which a high percentage of non-STEM students having an interest in pursuing a STEM career will also be examined. Finally, the project will expose how cognitive factors (career aspirations in STEM fields, and attitudes and beliefs about STEM), social factors, (peers, family, and institutional) and behavioral factors (selecting STEM as a major, and remaining in STEM) may be molded or is molded by the effectiveness of creativity training. The specific objectives of the project are as follows: (1) administer and assess the impact of creative problem solving on academic performance of students; (2) assess the degree to which cognitive, social, and behavioral factors impact or is impacted by the efficacy of creative problem solving; and (3) provide creative problem solving skills so students can continue to use the techniques after they leave SCI 100. Funding for this research is provided by NSF/HRD ERP award no. 1036183.
Keywords: educational research; creativity; creative problem solving; STEM; retention

Alexandridis, K., Turner, T., Engerman, K., and Kobrinski, E. (2012). Visualization and Aesthetic Perceptions in Assessing Caribbean Coral Reef Resilience: An Experimental Study. Poster at the UVI Research Day 2012 Conference. St. Thomas, US VIrgin Islands, April 14, 2012, University of the Virgin Islands.

Abstract: Achieving sustainability and resilience in many of our contemporary global, regional and local ecosystems is of paramount importance. Natural (biophysical) and human (anthropogenic) factors, drivers and interactions are jointly responsible for a number of environmental challenges and transformations. Global and regional biophysical forces, such as climate change, sea-level and ocean temperature rise, changes in intensity and frequency distribution of extreme events, to name a few. Choices related to such global and regional transformations are either limited, or have not been part of a comprehensive national or regional approach. This research analyzes local Virgin Islander’s perceptions regarding visual interpretations of coral reef systems in our region. Participants were asked to rank 25 images based on either their ecosystem health, or their subjective aesthetic preferences, randomly. The participant average and individual rankings were associated with demographic/personal characteristics, and the 25 images were submitted to unsupervised signal processing mathematical analysis and algorithms, including pattern recognition, threshold identification, RGB signal separation, entropy threshold, and others. Signal processing signatures and average values per image was then modeled against preferences and participant attributes. Our results indicate that: (a) biophysical systemic characteristics (color, patterns, thresholds, entropic characteristics) of visualization perceptions can be strongly associated with aesthetic preferences and attitudes, and vice versa; (b) visual aesthetic characteristics affect and being affected by age, gender, and education, and; (c) experiential learning, i.e., learning by experience along with social learning affects both physical (aesthetic) and cognitive (perceptional) characteristics of participant’s marine and environmental conservation priorities. Funding for this research is provided by NSF VI-EPSCoR award no. 0814417.
Keywords: coral reefs; resilience; social resilience; aesthetic values; social-ecological systems; environmental change; environmental sustainability

Alexandridis, K., Engerman, K., Turner, T., and Kobrinski, E. (2012). The Role of Experiential Social Learning in Achieving Semantic Transformations in Community Attitudes, Beliefs and Behaviors Towards Coral Reef Resilience. Poster at the UVI Research Day 2012 Conference. St. Thomas, US Virgin Islands, April 14, 2012, University of the Virgin Islands.

Abstract: The Virgin Islands near shore reefs have almost all been affected by anthropogenic processes including but not limited to coral bleaching, nutrient enrichment, sedimentation, and overfishing. The overall goal of the research undertaken is twofold: (1) to prove the conjecture that experiential social learning contributes to achieving semantic transformation in relation to community attitudes, beliefs and behaviors towards coral reefs and their conservation; (2) to enhance and promote scientific and methodological discovery related to the human and cognitive dimensions of environmental and natural resource sustainability and resilience. The research presents a mix of experimental and observational study with local participants. Participants were administered a pre- and post- attitudinal test, and during the experiment their interactions was recorded and observed in parallel by researchers. During the boat trip interactions three general research study activities were undertaken: (a) observing social group formation and dynamics including informal peer networks; (b) interviewing subjects in group/focus discussion settings, and; (c) observing informal interactions among individuals both in a discourse and in an underwater setting. The intellectual merit of the proposed research is advancing scientific knowledge and discovery related to (a) human dimensions in environmental, marine and natural resource conservation; (b) methodological underpinnings of human behavior and actions related to environmental change; (c) cultural and social community attitudes, beliefs and dispositions, and (d) insight into the complexity of social and collective interactions. Funding for this research is provided by NSF VI-ESPCoR award no. 0814417.
Keywords: experiential learning; social learning; resilience; social-ecological systems; social resilience; attitudes; natural resource management; social science

Alexandridis, K., Engerman, K., and Huggins, D. (2012). Studying Associations of Key Time Perspectives to Cognitive, Cultural, and Social Characteristics of College Student Performance: Early Evidence from a STEM Retention Study at the University of the Virgin Islands. Poster at the UVI Research Day 2012 Conference. St. Thomas, US Virgin Islands, April 14, 2012, University of the Virgin Islands.

Abstract: The general purpose of this study is to examine the effects of Creative Problem Solving use on students in Science Technology Engineering and Mathematics subject areas. This study is being conducted on a sample of thirty nine (39) male and female undergraduate students at the University of the Virgin Islands attending Science 100, a mandatory course for sophomore UVI students, throughout two labs, one being a control group. We measured temporal perspectives of student’s thinking using the Zimbardo’s Time Perspective Inventory (ZTPI) and the Transcendental-Future Time Perspective (TFTP). In the combined scores, our study and control populations scored statistically significantly higher on their Past Negative, Present Fatalistic, and Transcendental Future dimensions, as compared to the reported national mean factor loadings, and in all three perspectives deviating further from the ideal scores (t scores of 3.84, 3.95 and 4.95 respectively; p<.001). We also found statistically significant gender and educational classification differences, as well as statistically significant differences across GPA performance levels (using a GPA=2.5 as a cutpoint). In this paper we will discuss the significance of these findings with respect to key demographic, educational, cognitive, social, and cultural or sociocultural characteristics of our sampled population. We will argue that key temporal dispositional and social characteristics of student attitudes have strong associations with achieved academic performance and could conceivably affect future retention rates. Funding for this work is provided by NSF/HRD ERP award no. 1036183.
Keywords: educational research; STEM; time perspective; creativity; creative problem solving

Alexandridis, K., and Olsen, D. A. (2011). A Pilot SES-ERAEF Approach to Caribbean Fisheries. Invited Talk at the Caribbean Fishery Management Council, Scientific and Statistical Committee Meeting. San Juan, Puerto Rico, November 15-16, 2011, Caribbean Fishery Management Council.

Abstract: The presentation addresses the need for integrating social-ecological system approaches to fisheries management by inrdoducing a social-ecological systems approach, in order to assess social-ecological risk we need to ground risk perceptions using an evidence-based community assessment. Specifically, addresses (a) the critical need to study and understand the relationship between social perceptions of risk (and vulnerability) and their direct or indirect mappings to the ecological risk and relevant systemic vulnerabilities; (b) the management and policy perspectives (e.g., emergence dynamics, decision-support measures and mechanisms, transparency and veridicality, uncertainty in decision-making); (c) multi-scale considerations (focal, above and below); (d) co-research and collaborative fishing community activities, and; (e) a participatory community and engagement framework for managing critical social-ecological systems in the Caribbean Fisheries.
Keywords: fisheries; social-ecological systems; resilience; natural resource management

Alexandridis, K. (2011). Mental Model Representation of Collective Learning in Sustainable and Resilient Livelihood Choices – From Knowledge to Action. Poster at the CAREER Award Regional Forum. Baton Rouge, Louisiana, November 7-9, 2011, Louisiana State University.

Abstract: The research studies the formation and prevalence of mental model representation of collective learning in sustainable and resilient livelihood choices. It focuses on the role of adaptive responses and response mechanisms to environmental change and environmental conservation. Specifically, the research focus on studying: (a) the interplay between livelihood choices and outcomes of collective actions; (b) experiential social learning and how it influences social resilience to a changing natural, social and economic environment; what are the conditions, dispositions, circumstances and systemic properties that either enable or confine sustainability and social resilience in adapting to environmental change; (c) clarifying the role of marine and natural resource-based livelihoods in promoting environmental conservation, and; (d) how collective (experiential) learning promotes sound attitudes, beliefs and behaviors that can transform into sustainable and resilient conservation actions.
Keywords: social-ecological systems; mental models; resilience; social-ecological resilience; social resilience; livelihoods; knowledge; environmental change

Ortiz, L., Alexandridis, K., and Pittman, S. (2011). From the Open Sea to a Pot of Kallalloo: the significance of the USVI fishery in identifying you. Paper at the 35th Scientific Conference of the Association of Marine Laboratories of the Caribbean (AMLC). San José, Costa Rica, 23-27 May 2011.

Abstract: Local social-ecological knowledge is increasingly recognized as an important source of information that can help society to understand environmental and historical changes regarding natural resources from the user groups’ perspective. Specifically, fishermen’s knowledge can highlight the importance of natural events such as fish migrations and spawning aggregations, as well as variations and trends in fish populations. This knowledge can also provide insight on the role a fishery has in defining the cultural and social identity of its fishing community. The objectives of this study are to gather and analyze local social-ecological knowledge and historical data on the USVI fishery in order to describe the socio-cultural ties that bind the physical environment of the fishery and its resources to the local fishing community’s identity, while providing a “voice” for the fishing community with regards to key concepts or issues concerning sustainability of the fishery; not just as an ecological and economic resource, but also as a socio-cultural institution. This has been done through employing an open-ended, snowballing qualitative interview methodology that facilitates the free flow of informational and dialogical content using references and judgments of community participants, as well as collection of historical data and archives. This data will be compiled and qualitatively analyzed using associative semantic and social network content analyses to empirically identify the magnitude and strength of intangible, non-market community assets and values and the emergence of any complex social and contextual relationships that influence USVI fishing community identity. Ultimately, it is important to understand the perspectives and social structure and functions of the local community in order to determine the necessary approaches for maintaining and ensuring long-term sustainability of any natural resource whilst maintaining and expanding social and cultural resilience, and that of the USVI is no exception.
Keywords: social-ecological systems; local ecological knowledge; sustainability; ethnoecology; fisheries; natural resource management; community participation

Alexandridis, K. (2011). Navigating Complexity of Interactions in Coupled Human-Natural Systems – from Theoretical to Empirical Knowledge. Invited Seminar Paper at the Santa Fe Institute Seminar Series, Invited Talk Session. Collins Conference Room, Santa Fe, New Mexico, March 18, 2011, Santa Fe Institute.

Abstract: The study of coupled human-natural systems has been among the increased focus of scientific investigation for the last decade. Whilst many of the theoretical foundations in the study of complexity in such systems exist for a while, our empirical evidence-based inference reveals more insights into the challenges and opportunities for deeper understanding of the complexity in the coupled systemic interactions. Going beyond simple inferences, such understanding leads us to explore collective pathways of complex group decision-making and the navigation across multi-dynamic and multi-scale landscapes of interactions integrating ecosystem-based considerations to social, cultural and economic social emergence. The presentation will focus on how strengthening collective knowledge flows and interactions related to knowledge acquisition, representation and diffusion provides insight into self-organization, enhancing adaptive capacity, and promotes sustainability and resiliency in such coupled systems. It would also argue that unlike traditional ecological resilience theory, social and thus coupled-systems resilience presents a certain degree of ergodic systemic properties, and has a fundamental probabilistic rather than deterministic character in its spatial and temporal transitions and transformations.
Keywords: social-ecological systems; complex systems; complexity; social networks; semantic networks

Alexandridis, K. (2011). Identifying Social Community Resilience in Collective Semantic Knowledge Transformations. Paper at the Resilience 2011 - Resilience, Innovation and Sustainability: Navigating the Complexities of Global Change. Second International Science and Policy Conference. University of Arizona, Tempe, Arizona, March 15, 2011.

Abstract: Contemporary environmental systems, especially the ones with significant human presence are highly complex, volatile and nonmonotonic, while maintaining and reinforcing their adaptive character and in non-equilibrium states. The complexity of systemic interactions between coupled human and natural systems has been a source of debate and dispute among disciplinary scientific domains, and only the last decade systematic approaches have been initiated to address the issues of coupled complexity between the biophysical and the human dimensions of today’s environmental challenges. In many cases our theoretical and methodological toolboxes do not suffice to capture and understand the nature, character and pattern of the systemic interactions. In this paper we propose a complementary and alternative approach in understanding the nature of systemic interactions in the concept of social community resilience as a direct consequence of transformative dynamics in collective knowledge transformation. We argue that knowledge (and thus, information) acquisition-retrieval, representation and retention in collective social settings and dynamics provides a basis in which social community resilience can be assessed and quantified. The methodological and theoretical analysis provided in this paper incorporates a three-tier paradigm: (a) Focus on collective social community interactions (such as deliberative and non-deliberative associations, community dialogue, community group and institutional dynamics, collective mental representations and inferences such as group beliefs, attitudes and behaviors); (b) Study the ways knowledge and information flows are connected and interact with our physical realities (via, for example semantic network analysis, social network analysis, natural language processing and pattern recognition/data mining techniques); (c) Analyze how patterns of social emergence, adaptation, collapse or reorganization facilitates social learning in terms of knowledge acquisition/retrieval, representation and retention and their feedback mechanisms with the physical/natural world realities. Finally, we argue that true social-ecological resilience cannot be achieved without building capacity and adaptive thresholds of persistence in collective patterns of self-organization in local and community knowledge transformations.
Keywords: social-ecological systems; resilience; social resilience; complexity; adaptive systems; coupled human-natural systems; collective representations; knowledge systems

Alexandridis, K., Ortiz, L., and Pittman, S. (2011). An Ethnoecological Investigation of Social Resilience and Adaptation Patterns in Caribbean/USVI Historical Fishing Community. Paper at the Resilience 2011 - Resilience, Innovation and Sustainability: Navigating the Complexities of Global Change. Second International Science and Policy Conference. University of Arizona, Tempe, Arizona, March 14, 2011.

Abstract: Social-ecological collective community resilience has only recently entered the domain of interest in scientific investigation. Collective ways of community interactions, decision-making, communication and action are central to our scientific understanding of coupled human-natural systems and their close relationships. Beyond simply individual attitudes, beliefs and behaviors, a number of collective social processes, such as social group formation, norms, and patterns of collective knowledge acquisition, knowledge representation, and knowledge inference present the key to our understanding of major and critical transitions and system transformations both at the environmental, as well as at the social or societal level of interactions. The research reported here presents a methodical, empirical, and epistemologically robust scientific investigation on historical social-ecological transformations in an ethnoecological context for the Caribbean local fishing community in St. Thomas, US Virgin Islands. In this paper we will study how local ecological knowledge (LEK) patterns of collective community understanding are critical for maintaining both the community cohesion and socio-cultural characteristics, but also to preserve a local, intuitive, and culturally robust sense of place, space, and community in relation to the relationship between humans and their marine environment. We showcase how coupled social-ecological resilience can be studied as a transformative system of interactions, encapsulating semantic knowledge transformations, and the network connections and associations that form an integral part of local-level fishing livelihoods. Our research raises a number of interesting questions regarding the functional definitions of sustainability of marine livelihoods, and identifies key non-tangible, yet critical community and social values that affect and being affected by contemporary fisheries management approaches and the lack of ground-up, participatory and ecosystem-based approaches to marine-based NRM. Finally, we identify key aspects affecting the ability of the local coupled fishing community – marine environment system to self-organize and adapt to changes at both the natural marine environment, and the policy-making realities of local (USVI) , national (USA), regional (Caribbean), and cross-national/multicultural natural resource management.
Keywords: social-ecological systems; environmental sustainability; local ecological knowledge; fisheries; collective representations; community participation; natural resource management; ethnoecology

Huggins, D., Alexandridis, K., and Engerman, K. (2011). Studying Associations of Key Time Perspectives to Cognitive, Cultural, and Social Characteristics of College Student Performance: Early Evidence from a STEM Retention Study at the University of the Virgin Islands. Poster at the AAAS 2011: Annual Meeting of the American Association for Advancement of Science. Washington, DC, February 17-21, 2011, American Association for the Advancement of Science (AAAS).

Abstract: The general purpose of this study is to examine the effects of Creative Problem Solving use on students in Science Technology Engineering and Mathematics subject areas. This study is being conducted on a sample of thirty nine (39) male and female undergraduate students at the University of the Virgin Islands attending Science 100, a mandatory course for sophomore UVI students, throughout two labs, one being a control group. We measured temporal perspectives of student’s thinking using the Zimbardo’s Time Perspective Inventory (ZTPI) and the Transcendental-Future Time Perspective (TFTP). In the combined scores, our study and control populations scored statistically significantly higher on their Past Negative, Present Fatalistic, and Transcendental Future dimensions, as compared to the reported national mean factor loadings, and in all three perspectives deviating further from the ideal scores (t scores of 3.84, 3.95 and 4.95 respectively; p<.001). We also found statistically significant gender and educational classification differences, as well as statistically significant differences across GPA performance levels (using a GPA=2.5 as a cutpoint). In this paper we will discuss the significance of these findings with respect to key demographic, educational, cognitive, social, and cultural or sociocultural characteristics of our sampled population. We will argue that key temporal dispositional and social characteristics of student attitudes have strong associations with achieved academic performance and could conceivably affect future retention rates.
Keywords: educational research; STEM; student retention; Academic achievement; time perspective

Engerman, K., Alexandridis, K., and Huggins, D. (2011). Undergraduates Perceived Peer Academic Support. Poster at the AAAS 2011: Annual Meeting of the American Association for Advancement of Science. Washington, DC, February 17-21, 2011, American Association for the Advancement of Science (AAAS).

Abstract: Retention of students in STEM education has been identified as a problem nationwide. Studies have shown that social support is a key factor in retention. Therefore, the objective of this study is to describe the academic support received from peers for students enrolled in a first year science course at a university. A self-report questionnaire was used to collect data on undergraduates’ perceived academic peer support. Academic peer support was measured in the form of four factors: (1) informational; (2) esteem; (3) motivational; and (4) venting support. Participants reported receiving no support, little support, or lots of support. Results indicate that 59.64% of participants received little informational support; 47.04% received little esteem support; and 36.75% receive lots of motivational and venting support. The following percentage of participants reported receiving no peer support: 12.18% for informational; 18.62% for esteem; 29.4% for motivational; and 27.93% for venting. In conclusion, since college can be stressful at times, peers provide motivation and a forum for venting. Therefore, having that support system in place is a factor that can lead to retention.
Keywords: educational research; STEM; academic success; student retention

Alexandridis, K., and DeFreitas, D. (2011). Perceptions and Fallacies in Spatial Planning and Decision Making: Integrating environmental, social, and economic realities to marine ecosystem-based management. Paper at the ASLO 2011: Aquatic Sciences Meeting. Puerto Rico Convention Center, San Juan, Puerto Rico, February 13-18, 2011.

Abstract: This paper will address how the multiplicity of perceptions, different ranges of attitudes, expectations, and aspirations from diverse community and stakeholder groups, as well as key fallacies, misconceptions and paradoxes that found their way decision making (and decision-makers) are critically affecting our ability to address marine and spatial planning challenges. They also prevent us from generating opportunities for a new and integrated ecosystem-based approach to addressing the complexity of interactions across spatial, social, and temporal scales. We will use a case study in the Great Barrier Reef in Australia's North Queensland's coast to look at community and stakeholder (fisher's) perceptions towards marine conservation zones and planning efficiency in general. We will demonstrate how perceptions of adequate consultation are as likely to affect community and stakeholder adoption of marine spatial planning protected zones, than other important conservation or environmental factors, across a range of spatially explicit zones. We finally will provide a set of inferences and recommendations for directions and challenges on promoting a decision-theoretic, ecosystem-based, coupled systems approach to marine and spatial planning/ decision-making.
Keywords: social-ecological systems; perceptions; attitudes; cognition; natural resource management; ecosystem-based approach; fisheries; community participation; complexity; decision-making

Alexandridis, K. (2010). Community Participation in Sustainable Development and Alternative Livelihoods Enterprises: Thinking Regionally and Acting Locally. Paper at the 2010 Annual VI-EPSCoR Conference: Integrating Science and Our Economy: A Model for Island Ecosystems. Frenchman's Reef Mariott, St. Thomas, US Virgin Islands, December 12, 2010.

Abstract: Some of the critical challenges of the post-climate-change awareness era are not simply to create and operate functional green economies, but primarily to establish, foster and promote “green” societies and local communities capable of nurturing an appropriate socioeconomic environment for green development. The Caribbean land and marine-based natural environment presents a series of serious challenges, along with a rich set of unique opportunities for regional, sustainable, and alternative livelihoods development. This talk will explore the linkages between the need for green regional coordination and the opportunities for grassroots community participation in marine and coastal-based sustainable and resilient sustainable development. It will also argue that community and natural resource-based sustainable development can also be catalyst in reducing social and economic disparities and inequalities by providing an inclusive and participatory environment, along with promoting and strengthening citizen voice, while establishing positive role models for younger and future generations of Virgin Islanders.
Keywords: social-ecological systems; community engagement; environmental sustainability; sustainable development; alternative livelihoods

Alexandridis, K. (2010). Bottom-up Community Participation in Fisheries Management: Challenges and future directions. Paper at the 63rd GCFI: Gulf and Caribbean Fisheries Institute Meeting. San Juan, Puerto Rico, November 1-5, 2010.

Abstract: In recent years an increasing number of case studies and evidence-based science has outlined the needs, and enhanced benefits of grassroots or bottom-up community participation in sustainable and resilience ecosystem management. The fishing communities both at global, and at local levels have a key role to play as valuable collaborators and partners in the scientific understanding of changes in the ways that human societies and groups interact with their natural and marine environment. In most cases local fishing communities have multiple and critical dependences to their local marine environment, including livelihoods and employment outcomes, social and community wellbeing and happiness, the sustainability of food and other economic and social services, as well as traditional and customary responsibilities for the sustainability, resilience and preservation of marine resources for future generations. Empowering communities to achieve adaptive, resilient, and self-organizing potential for the future has multiple benefits for the communities themselves and beyond. At the same time, such approaches are contributing to social and collective learning, promoting social cohesion, responsibility, and accountability at the community/grassroots level, and achieving alternative sustainable and resilient development outcomes that improve the flows and interactions among the natural, social, economic, financial and physical capital within and across them. We will present case studies of alternative and resilient community-based fisheries projects around the world, and will provide a case for a paradigm shift towards bottom-up community participatory ways for fisheries management. This research is funded by NSF/VI-EPSCoR, award number no 203056.
Keywords: social-ecological systems; community participation; fisheries; natural resource management; bottom-up; resilience; ecosystem-based approach

Ortiz, L., Alexandridis, K., and Pittman, S. (2010). Using LEK to Investigate the Historical Ecology and Cultural Heritage of the USVI Fishery. Poster at the 63rd GCFI: Gulf and Caribbean Fisheries Institute Meeting. San Juan, Puerto Rico, November 1-5, 2010.

Abstract: Fishermen’s knowledge is increasingly recognized as an important source of knowledge and information that can help society to understand environmental changes such as those that result in both increases or declines in fish populations for example. It can highlight the importance of natural events such as fish migrations and fish spawning aggregations to the past and present fishery. The objective of this study is to gather and communicate local social-ecological knowledge on the USVI fishery from as early as the pre-colonial era and to document the historical heritage of the USVI fishery. We use open-ended, snowballing qualitative interview methodologies that facilitate the free flow of informational and dialogical content, references and judgments of community participants. The local knowledge and historical information gathered is used to develop a web-based educational resource for the USVI fishing community, their families, and future generations. This project aspires to provide a “voice” for the local fishing community with regards to the sustainability of decision making and the fishing community heritage; demonstrating that science and communities can work together in alternative ways of managing the local and regional fisheries resources from a community-based perspective that is culturally appropriate and scientifically sound. Finally, the project anticipates collecting and analyzing a wealth of anecdotal data such as photographic evidence, geographical and spatially-explicit data, as well as historical ethnoecological accounts of social-ecological interactions and transformations. Funding for this research project is provided by NSF’s VI-EPSCoR Incubator Grant No. 203045
Keywords: local ecological knowledge; participatory management; ethnoecology; social science; fisheries management; resilience; social-ecological systems; social-ecological resilience; ecological resilience; social resilience; engineering resilience; cognitive resilience; brain resilience;

Alexandridis, K., Duffy, J., Tavernier, B., McCrae, K., and Ortiz, L. (2010). Studying the Historical Ethnoecology of the USVI - St. Thomas Fishing Community. Poster at the 63rd GCFI: Gulf and Caribbean Fisheries Institute Meeting. San Juan, Puerto Rico, November 1-5, 2010.

Abstract: Fishing in the US Virgin Islands has been a part of island survival and culture since before Europeans and slave trade found its way into the Caribbean. However, any potential decreases in Virgin Island fisheries, is likely to directly impact the viability of the local fishing industry, and have negative consequences to the fishing communities and their fisheries-dependent livelihoods. The aim of our research is to collect, study, and analyze local ecological knowledge of fishermen and the St. Thomas fishing community and how such knowledge of the past and the present can be best used to inform future sustainable and resilient decisions in regards to USVI fisheries and its management. Our research methodologies includes community-based participatory methods, observational studies, historical archival research, and literature review to gather subjective information to be evidence-based evaluated using qualitative models and methods of analysis. Such methods include content analysis, qualitative classification, photographic interpretation, longitudinal or panel analysis, classification matrixes, and mapping of social networks. We also seek to understand the conditions and thresholds that are likely to produce a sort of “domino effect” and negative feedback mechanisms, ensuing fishery decline resulting in species extinction, coral decay, loss of jobs and food, and increased fish prices in a recession weakened tourism-based economy. We will contribute to the construction of a web-based archive, which will include our findings and digitally cataloged photos and data. This archive will provide the fishing community and future generations with easily accessible public knowledge about themselves, their history, and their environment.
Keywords: community-based management; local ecological knowledge; participatory research; qualitative methods; ethnoecology

Bohensky, E., Maru, Y., Butler, J., Stevens, T., Alexandridis, K., and Coutts, V. (2009). Understanding Indigenous Enterprise on Palm Island: Is Resilience More than A Metaphor? Paper at the Indigenous Participation In Australian Economies Conference. Canberra, ACT, Australia, November 10, 2009, Australian National University. Audio broadcasted by the National Museum of Australia, Australian National University.

Abstract: The paper provides an overview of resilience theory and its application to a proposal for an aquaculture farm as a sustainable enterprise on Palm Island, North Queensland. A historical analysis and empirical insights are provided from interviews and photographic surveys. Some empirical insights include sutuational analysis, limitations, and implications for adaptive capacity and social-ecological feedbacks and interactions.
Keywords: indigenous livelihoods; community participation; social resilience; resilience; sustainability; sustainable livelihoods; alternative livelihoods; natural resource management

Alexandridis, K. (2009). Modeling, Simulation and Analysis Techniques in NRM Science. Invited Talk at the NRM Research Seminar. University of the Virgin Islands, St. Thomas, US Virgin Islands, September 3, 2009.

Abstract: The seminar provides an overview of social science in the context of natural resource management, and few key modeling and simulation methodologies. We provide insight into key techniques such as participatory 3D modeling, role-playing games and companion modeling, artificial neural networks, agent-based modeling, and Bayesian belief networks. We provide analysis of results of a study of participants in the Great Barrier Reefs region, and the Naive Bayes learning classifier. We also showcase livelihood networks case study in the Anmatjere outback region of Australia, and the research methodology for participatory assessment. We finally provide analysis of a case study on semantic attitudinal network analysis in the Northern Territory of Australia.
Keywords: natural resource management; simulation; environmental sustainability; agent-based models; bayesian belief networks; modeling

Alexandridis, K. (2009). The Role of Human Dimensions in Environmental and Natural Resource Management and Policy. Invited Talk at the Graduate Teaching Seminar, Center for Marine and Environmental Studies, University of the Virgin Islands. St. Thomas, US Virgin Islands, September 2, 2009, University of the Virgin Islands.

Abstract: The talk will review key insights from the theory and methodology of human dimensions in NRM science. A rationale for why human dimensions in NRM are important components for consideration will be provided and simple examples will be drawn from the social sciences perspective (e.g., sociology, social psychology, economics and decision sciences). We will overview some important emergent lessons and frameworks that illuminate social dimensions in NRM settings (e.g., empowerment framework, ARVIN framework, social accountability framework, decision theory framework), and additional factors and parameters will be explored (such as the role of public opinions, intuitive judgments, social-environmental priming)
Keywords: human dimensions; natural resource management; environmental policy; human-natural systems; social-ecological systems

Alexandridis, K., Maru, Y., Davies, J., Box, P., and Hueneke, H. (2009). Constructing Semantic Knowledge Networks from the Ground Up: livelihoods and employment outcomes in Anmatjere region, central Australia. Paper at the 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation. Cairns, Australia, July 13-17, 2009, Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation.

Abstract: People need real opportunities to live the kind of life to which they aspire - to undertake livelihood activities they have reason to value, to achieve good health and well being outcomes, and to have resilience to shocks and stresses. A range of stakeholders consider that economic development is constrained by lack of engagement between Aboriginal people and labor markets, particularly given planned expansion of horticultural and mining operations. Aboriginal people of the Anmatjere region of Central Australia speak their own languages at home, have customary responsibilities for care of the region’s natural and cultural resources, and have low levels of formal mainstream education. They aspire to jobs in their region and are engaged relatively strongly in employment in the community services sector and seasonal work in the pastoral industry, but not in other private sector employment. Their high dependence for income on social security payments and government funded jobs makes their livelihoods vulnerable to changes in government institutions. The modelling work presented in this paper is based on the views, attitudes and experiences of people living in the Anmatjere region about jobs and livelihoods. We have organized these as a collective knowledge representation, using semantic networks. This has elicited understanding of the structure, strength and quality of connections amongst social, economic, environmental and cultural dimensions important in people’s livelihoods. The qualitative data were analysed using (a) natural language processing and linguistic algorithms; (b) exploration of semantic associations among knowledge constructs using a Hopfield-type Artificial Neural Network; and (c) graph-theoretic network analyses. We present the findings of this analysis in light of critical challenges that the Anmatjere community is facing. We show that culturally-explicit local Aboriginal institutions, world views and behaviours play significant and central roles in maintaining the community’s knowledge representations. They connect people and establish the social and cultural roles that are critical in people’s search for opportunity, income and the sustainability of their livelihoods in the region. ‘Top down’ actions including changes to government institutions aimed at enhancing individual Aboriginal people’s engagement with employment have little chance of success unless they take into account the locally and culturally-specific ways in which the community is collectively functioning.
Keywords: Indigenous communities; sustainable livelihoods; modelling; semantic networks; social networks; knowledge representation; artificial neural networks; participatory research

Alexandridis, K. (2009). A Connectionist Perspective for Computational Social Science and Complex Systems Science (CSS2). Invited Seminar Lecture at the Leibniz Institute of Agricultural Development in Central and Eastern Europe (IAMO). Halle, Germany, May 5, 2009.

Abstract: The seminar provides an overview of both computational social science and complex systems science issues involved in the focal research approach. These include the theoretical, methodological and modelling/simulation issues in semantic networks encapsulating collective social representation of livelihoods. The seminar also provides an overview of connectionist perspectives in social learning theory (especially related with collective social emergence), issues of complexity in network evolution and guided self-organization of social knowledge representation, and empirical insights from case studies in Australia (e.g., Anmatjere semantic networks and livelihoods research, NT nurses and midwives mobility study, etc.).
Keywords: social-ecological systems; connectionism; computational social science; sustainable livelihoods; environmental sustainability; simulation; semantic networks; collective social learning

Alexandridis, K. (2009). Modelling Indigenous Livelihoods in Regional Context: A CSIRO Focal Research Area. Invited Paper at the Seminar Series, Geomatics Institute, Humboldt University of Berlin. Berlin, Germany, May 4, 2009, Humboldt-Universität zu Berlin.

Abstract: The seminar provides an overview of the CSIRO Indigenous Livelihoods Focal Research Project, the context of our scientific inquiry and research. The approach especially relates to the understanding of the coupled human – natural systems interactions, and issues related to indigenous sustainability of livelihoods. We provide case study insights (Palm Island, QLD, Solomon Islands, Melanesia, Mitchell River, North QLD), as well as methodological and epistemological considerations in NRM research. We make an argument for the need and outline our approach to inductive and participatory research involving livelihoods and livelihood change. Finally we address a set of decision-theoretic and uncertainty considerations in bridging the micro-to-macro qualitative and quantitative dimensions of social science research.
Keywords: indigenous livelihoods; sustainable livelihoods; alternative livelihoods; social-ecological systems; environmental sustainability

Alexandridis, K., and Measham, T. G. (2009). Dynamic Livelihoods and Change: The interplay between choice and outcome. Paper at the 7th International Science Conference on the Human Dimensions of Global Environmental Change. IHDP Open Meeting 2009. Bohn, Germany, April 26-30, 2009, International Human Dimensions Program.

Abstract: In this paper we explore the “micro-to-macro” linkages and interplay between choice and outcome across multiple scale continua: global to local, resource scarcity to availability, developing to developed frameworks, and accessible to remote communities. We are identifying key livelihood elements and their sensitivity to changes in demographic composition, in the magnitude and intensity of resource use, in the strength, pattern and structure of social networks, as well as a number of factors affecting rural viability and alternative community opportunities. The traditional notion of “Sustainable Livelihoods” is not adequate to provide a ground-up approach towards a unified framework of livelihoods across all scales and resource continuums. Our research illustrates a dynamic, nonparametric framework for assessing livelihood elements from the ground up, by enabling and empowering communities to self-identify key livelihood and viability factors, and by utilizing both qualitative and qualitative assessments as a dynamic network of inter-related livelihood elements. We use a case study of a relatively small region in Outback Australia, in the northern tropical Queensland in Australia. We argue that not only the character of growth (i.e., developing country context), but also key environmental, social and economic conditions (e.g., availability of choice, natural environment and resource conditions, remoteness, achieved outcomes or solutions), are important and critical parameters of regional and rural viability. We finally demonstrate a new, dynamic nonparametric (i.e., probabilistic or probabilogic) framework for assessing rural livelihoods, as a graph-theoretic network of inter-related and inter-connected livelihood elements.
Keywords: social-ecological systems; livelihoods; Bayesian Belief Networks; participatory bayesian networks; livelihood networks; sustainable livelihoods; alternative livelihoods

Alexandridis, K. (2008). Self-Organization of Semantic Networks in Natural Language Processing and Computational Social Science. Paper at the First International Workshop on Guided Self-Organisation (GSO-2008). Sydney, Australia, November 24-27, 2008, International Association for Guided Self-Organisation (TIA-GSO).

Abstract: A semantic network represents a structure for knowledge representation (KR) as a pattern of interconnected nodes and arcs (Sowa, 1991). Nodes and arcs in such a network encode a wide variety of semantic relationships, including linguistic approximations of knowledge and different types and scales of connectivity over complex hierarchical structures of subsumptional entities. Semantic expressions also introduce knowledge approximations (isomorphisms) to logic-based, rational propositional inference or formalisms (Schubert, 1991). According to Quillian (1967), semantic networks based on linguistic assertions represent an abstraction theory of the structure of the human long-term memory, able to embody knowledge as a computational model. The spreading-activation theory of semantic processing (Anderson, 1983; Collins and Loftus, 1975) allows the study of effects of priming in semantic memory via semantic network associations of interconnected relationships, stored and recalled in memory altogether (retention-activation properties). Semantic networks of memory processing are recently considered to be part of a new approach to learning theory, namely the connectivism learning theory (Siemens, 2005) that focuses on the understanding of how informal learning patterns and associations (as complex adaptive systems) help people utilize and connect learning and knowledge rather than information itself. Semantic networks are proven to be scale-free networks and strongly related to weak social ties in social networks of interactions. In this paper, the construction of semantic networks from natural language knowledge representation and the use of Hopfield-type Artificial Neural Networks will be explored. Hopfield ANNs present a type of recurrent NNs with simple neurons and no hidden layers (Hopfield, 1982). They utilize dynamic computation of auto-associative semantic rules in connecting synaptic nodes in the network. They represent content-addressable memory systems with binary threshold units. The iterative nature of semantic memory processing and retrieval in Hopfield Semantic Nets allows for finding a lower “energy” level for the network that self-organizes activation network patterns over knowledge hierarchies. The paper will showcase how semantic processing in semantic memory priming and retention, modelled using Hopfield-type ANNs allows for efficient and correct knowledge approximation and ontological representation of dynamics in human cognitive processing. It will also demonstrate how human memory self-organization of semantic patterns improves informal learning and decision-making.
Keywords: self-organization; semantic analysis; semantic networks; social-ecological systems; Hopfield networks; Artificial Neural Networks; semantic processing; semantic network

Alexandridis, K., Bohensky, E., Conway, M., Boyd, S., and Furey, B. (2008). Indigenous Livelihoods Case Study: Palm Island Visioning for Sponge Aquaculture. Paper at the CSIRO SRD Indigenous Livelihoods Workshop. Alice Springs, Northern Territory, Australia, November 10-12, 2008, Commonwealth Scientific and Industrial Research Organization (CSIRO).

Abstract: We provide a case study of the Palm island community with 3-tier community stakeholders: the Manbarra Traditional Owners (TO), the Palm Island Aboriginal Shire Council (Council), and the Colgaree CDEP Aboriginal Corporation, for development a community-based sponge aquaculture farming. We provide the detailed case study planning and deployment of sponge farming infrastructure and address how can community-driven enterprises contribute to sustainable livelihoods and Country on Palm Islands. We demonstrate how traditional Indigenous knowledge be brought together with science to gain understanding of problems and potential solutions. We provide the research methodologies used: exploratory visioning, scenario development, participatory livelihood networks, and institutional/policy analysis. Finally, we provide a pathway to research impact and outcomes.
Keywords: sustainable livelihoods; sustainability; indigenous communities; indigenous livelihoods

Alexandridis, K. (2008). Semantic Networks and Computational Social Science: In Search for Associative Meaning for CSS2. Paper at the CSIRO Davies Science Talk Series. Davies Laboratory, Townsville, Queensland, Australia, October 1, 2008, Commonwealth Scientific and Industrial Research Organization.

Abstract: A semantic network is a structure for knowledge representation with nodes and arcs. Semantic networks reflect distinctive representational entitites, and encapsulate definitional, structural or assertional schemas. We provide an overview of semantic associations, linguistic concepts, and the spreading-activation theory of semantic processing. We outline key elements of the theories of learning and knowledge representation for semantic processing, and the process of deriving semantic associations from qualitative (textual corpus) data responses. We provide an example of semantic extraction and analysis in the Northern Territory with health care and nursing professionals who provided narrative responses to a qualtitative survey.
Keywords: semantic networks; computational social science; latent semantic analysis; complex systems; social-ecological systems

Alexandridis, K., Herr, A., Gordon, I., and Thomas, C. (2008). Modelling Knowledge Scenarios for Tropical River Planning in Australia Using Participatory Bayesian Belief Networks. Paper at the ISBA 9th World Meeting. Hamilton Island, Australia, July 21-25, 2008, International Society for Bayesian Analysis.

Abstract: The Tropical Rivers and Coastal Knowledge (TRaCK) initiative aims to enhance the value and quality of environmental, economic, social and cultural aspects of tropical rivers in Australia. Australian Tropical Rivers represent an important cultural and environmental asset of the Australian heritage. The need to protect and preserve these values and assets for future generations coexists with the need to enhance viability, livelihoods and wellbeing of the communities that currently live and work in these regions. The research approach presented here models land management and ”knowledge flows” in relation to Tropical River health, values and environmental quality. It focuses on two tropical river systems across two large regions: the Daly River catchment in the Northern Territory, and the Mitchell River catchment in Tropical North Queensland. In particular, the research utilizes participatory, bottom-up techniques for constructing a series of Bayesian Belief Networks based on scenarios and alternative future visions. The range of scenarios vary from quantitative and data-driven (e.g., spatially and temporally explicit) to qualitative (e.g., descriptive narratives or storylines). It also incorporates information from different environmental, economic, social and cultural domains, and addresses different scales of inference (i.e., policy-making, group decision-making and individual decision-making). The functionality of these participatory Bayesian Belief Networks is tested against targeted or simulated water quality/quantity characteristics and ecosystem health indicators and inferences are drawn relating to decision-theoretic and policy-specific aspects of land management practices, as well as various dimensions of change.
Keywords: Bayesian belief networks; coastal areas; Tropical coastal communities; environmental sustainability; participatory decision making; planning; natural resource management; sustainable livelihoods; alternative livelihoods; water quality; ecosystem services; ecosystem health

Alexandridis, K. (2008). Linking Bayesian Network Inference and Qualitative Social Networks: Lessons Learned from the Study of Livelihood Systems in Outback Queensland. Poster at the ISBA 9th World Meeting. Hamilton Island, Australia, July 21-25, 2008, International Society for Bayesian Analysis.

Abstract: Traditional Bayesian network inference requires explicit elicitation of probabilistic and associative relations among network elements (nodes). In this presentation we will provide a participatory framework for linking quantitative probabilistic inference to qualitative and associative graph-theoretic network relationships among network elements, based on the study of social and economic issues of livelihoods and viability. The benefits of linking traditional quantitative techniques such as Bayesian Networks and qualitative social network assessment, arise from the ability to infer from both the probabilities of occurrence and from the strengths of the underlying relationships connecting any two network elements or nodes. Network learning allows us to use advanced social network algorithms while maintaining the probabilistic relationships among nodes. The research presented showcases the methodological approach and the socioeconomic value of inference for constructing, training and analysing a Bayesian Livelihood Network. This network is based on a combination of interview narratives, spatially and temporally explicit probabilistic training, and algorithmic network structural learning of livelihoods in the Upper Burdekin Outback region of Northern Queensland. The results allow for an understanding of livelihood relationships within a complex socio-economic system, and for establishing decision-theoretic priorities that include socio-economic sensitivities to guide future policy making.
Keywords: bayesian belief networks; sustainable livelihoods; natural resource management; alternative livelihoods; outback; narratives; probabilistic modeling

Alexandridis, K. (2008). KISS Rule Revisited: How Complexity Interfaces with the Need for Simplicity? Paper at the CSIRO Complex Systems Science Annual Workshop 2008. Brisbane, Queensland, Australia, June 17-19, 2008.

Abstract: In recent years there is a growing interest to address the issues of complexity vs. simplicity. These issues are important in terms of computational and modeling approaches, our ability to cope with complex parameter spaces and computational complexity, the challenges of oversimplified models, and the complexity of human-natural system interactions in the real world. We make the assertion that complex problems require by necessity the presence of complex solutions. We also explain and showcase the issues emerging from the hidden complexity of systemic interactions and the necessity to often obtain parametric solutions to nonparametric problems. We argue that the presence of deep uncertainty introduces nonlinearity and non-monotonicity. Finally, we present examples of concept model ensembles, and compex parametric spaces for model simulation and parameterization.
Keywords: social-ecological systems; complexity; complex systems; simplicity; adaptability

Alexandridis, K., Heckbert, S., Maru, Y., and Box, P. (2008). Modelling Indigenous Livelihoods: pBLNs and other Micro-Simulation Methodologies. Paper at the CSIRO Indigenous Livelihoods Science Workshop. Cairns, Queensland, Australia, May 20-22, 2008.

Abstract: We present a framework for modeling indigenous livelihoods using participatory Bayesian livelihoods networks and other micro-simulation methodologies. We address a need for understanding sustainable livelihood systems across multiple dimensions: time, space, scale, system-wide, scientific disciplines, community engagement and empowerement, and alternative choices. We argue for a new modelling paradigm that addresses deep uncertainty and incomplete information, as well as exogenous uncertainties propagating through the livelihood system. We provide an overview and case studies of implementing participator BLNs, and demonstrate how Bayesian semi- and non-parametric modeling techniques can aid our understanding of complex livelihood systems. Finally, we discuss issues of scaling and cross-scale livelihood linkages.
Keywords: social-ecological systems; indigenous livelihoods; sustainable livelihoods; participatory bayesian networks; bayesian belief networks; simulation; micro-simulation; sustainable development

Bohensky, E. L., and Alexandridis, K. (2008). Palm Island Indigenous Livelihoods Visioning. Paper at the CSIRO Indigenous Livelihoods Science Workshop. Cairns, Queensland, Australia, May 20-22, 2008, Commonwealth Scientific and Industrial Research Organization.

Abstract: We provide an overview of the Palm Island community and its stakeholders: the Palm Island Aboriginal Schier Council, the Mnbarra Traditional Owners, and the Colgaree CDEP. We discuss the natural resource-base enterprises and activities on the island and address the research questions: (a) understanding sustainable livelihoods and future visions to develop a culturally-appropriate, socially-explicit and environmentally-sustainable model of indigenous enterprise development; (b) exploring the communitie visions, aspirations, beliefs and attitudes towards cultural, social, economc, and environmental livelihood change, and; (c) providing a true participatory experience for younger generations of indigenous Australians. Finally, we provide an overview of research methodologies and tools.
Keywords: indigenous livelihoods; sustainable development; environmental sustainability; social-ecological systems

Alexandridis, K., and Thomas, C. (2008). TRaCK 1.1 - Bayesian Scenario Modelling: Overview and Network Modelling Methodology. Paper at the TRaCK 1.1 Modeller's Meeting. Charles Darwin University, Darwin, Northern Territory, Australia, April 28-29, 2008.

Abstract: This paper provides an overview of a modeling framework for the Tropical Rivers and Coastal Communities using multiple case studies. It focuses on (a) a Bayesian assessment of future scenarios developed for the selected TRaCK case studies; (b) providing a ground-up participatory modeling Bayeisan approach to alternative futures scenarios for the TRaCK region. We discuss methodological engagement, narratives and elicitation/consultation sessions and visioning exercsises that are used to construct belief networks linking attitudes, beliefs and future behaviors. We studying the interface between land management and tropical river health under a variety of multi-user and multi-use scenarios, as well as the level or ability of decision and policy makers to provide viable and scientifically sound solutions.
Keywords: Bayesian Belief Networks; bayesian analysis; tropical rivers; Tropical coastal communities; scenario modeling; scenario analysis; natural resource management; sustainable livelihoods; alternative livelihoods

Alexandridis, K., and Measham, T. G. (2007). Modeling Grazing Livelihood Systems in the Australian Outback using Participatory Bayesian Networks. Paper at the MODSIM 2007: International Congress on Modeling and Simulation, Paper Session. University of Canterbury, Christchurch, New Zealand, December 10-13, 2007, University of Canterbury.

Abstract: This paper describes the use of participatory Bayesian Belief Networks (pBBN) to describe and construct a representative livelihood system for the graziers of the outback areas in Northern Queensland (Upper Burdekin region). We use qualitative participatory techniques (community interviews, stakeholder and expert feedback) to manage for uncertainty in decision making related to key determinants of grazing livelihoods in the region. The process yielded the “BOLnet”, a livelihood representation, graph-theoretic network of relationships between key aspects of living within the grazing community. BOLnet is a combination of graphical and qualitative representations of livelihood linkages and relationships. It is a form of “graphical narrative” that bridges the traditional divisions between an extrapolative or descriptive measurement and prescriptive or normative observation. Using a combination of Bayesian Belief network analysis for the strength of the relationships and graph-theoretic network metrics for the structure of the network, we highlight a set of important findings that can aid communities, stakeholders, decision makers and policy makers to improve the quality and efficiency of sustainability approaches and actions.
Keywords: social-ecological systems; bayesian belief networks; livelihoods; participatory; Outback; Australia

Alexandridis, K., and Wang, X. (2007). SMURT Goals and Challenges: An Overview. Keynote Paper at the Simulation and Modelling in Urban and Regional Sustainability, Transitions and Applications in Policy, Planning and Management (SMURT). CSIRO Science Workshop. Highet, Melbourne, Victoria, Australia, December 4-6, 2007, CSIRO Sustainable Ecosystems.

Abstract: Within a highly complex and inter-connected world, enhancing simulation and modelling of urban and regional sustainability transitions is an essential part of the ways for us to understand, respond and adapt to the dramatic changes around us. Transitions in the structure and configuration of our urban and regional landscapes are directly and indirectly interwoven with changes in the structure and character of our social functions and groups. Simulation can inform management and policy makers, through successful and interactive dialogue and communication, setting priorities with a clear understanding of the problems that the society is facing in the future as well as assessing possible impacts of key decisions and the severity of the problems to the society as a whole – at all scales (local, regional, state and national). Urban and regional transition is a human-driven process. Unless we clearly understand the degree and magnitude of transitions’ social significance, we cannot achieve levels of policy and management responses that would enhance the social, economic, institutional and cognitive capacity of societies to respond and adapt to the transitions. Simulation and modelling may provide an alternative pathway to the answers. The workshop aims to establish a shared appreciation of the most pressing sustainability issues and what research and modelling techniques are appropriate for developing and communicating potential transition solutions.
Keywords: modeling; urbanization; simulation; modeling; land use change; Australia

Wang, X., and Alexandridis, K. (2007). A Review of Modelling in Urban and Regional Transitions. Paper at the Simulation and Modelling in Urban and Regional Sustainability, Transitions and Applications in Policy, Planning and Management (SMURT). CSIRO Science Workshop. Highet, Melbourne, Victoria, Australia, December 5-8, 2007, CSIRO Sustainable Ecosystems.

Abstract: The presentation will give an overall overview on the modelling and simulation techniques, which has been applied in the urban and regional settings and dynamics. Understanding the strengths and weaknesses of the models, techniques and methods both at the theoretical/methodological levels and at the applied/real-world settings is essential for crossing inter- and cross-disciplinary science boundaries. How to model urban and regional dynamics at different temporal and spatial scales, operate within different knowledge and information domains, and encapsulate a range of environmental, social and economic dimensions of urban and regional change is the key issues that should be developed in modeling and simulation methodology.
Keywords: urbanization; land use change; sea change; modeling; simulation; Australia

Alexandridis, K. (2007). Participatory Bayesian Inference and Modelling in Coupled Human-Biophysical Complex Systems and Natural Resource Management. Paper at the 1st Meeting of Bayesian Network Modellers (MBNM 2007). St. Lucia Campus, University of Queensland, Australia, November 19-20, 2007, University of Queensland.

Abstract: The presentation provides an overview of Bayesian inference methodologies and techniques for modelling complexity in coupled human-biophysical systems as it is applied through a series of CSIRO projects and research engagements. Research examples are given on constructing participatory BBNs from the ground-up for linking social, economic, environmental and cultural system components; modelling networks of livelihood elements and wellbeing relations; applications of Bayesian approaches to adaptive natural resource management; estimating uncertainty in spatially-explicit model predictions, and; implementing advanced Bayesian structural and (non)parametric learning for spatial, qualitative and categorical/quantitative knowledge/ information assessment.
Keywords: bayesian belief networks; natural resource management; human-natural systems; social-ecological systems

Alexandridis, K., and Schandl, H. (2007). Sprawl Patterns and Change Dynamics in Sea Change Communities across Australia. Paper at the Sustainable Economic Growth for Regional Australia (SEGRA 2007): Sustainable Regional Development - Changing Regions: The Road to Success. Eleventh National Conference. Wollongong, Illawarra Region, New South Wales, Australia, September 17-19, 2007, SEGRA.

Abstract: This paper explores the spatial patterns and dynamics of changing landscapes in Sea Change Communities in Australia. The study of sprawl patterns in changing suburban, periurban and exurban landscapes is gaining significant ground among scientists and researchers internationally. Sea Change represents a special case of these phenomena that is spatially confined to coastal areas and urbanization. The paper examines the spatial distribution and relationship of land use change patterns and a group of demographic, social and economic factors that exhibit a high degree of systems’ integration across time and space. The analysis is performed at the administrative unit of Local Government Areas (LGAs). We analyze changes in demographic profiles of those LGA’s that form the national SEA Change Task Force and portray land use changes and emerging spatial patterns across these 73 local government areas. We use a sprawl index to identify patterns and drivers of change and provide distributions of ranked sprawled communities to explore them in terms of their temporal and spatial dynamics. Sea change directions and hierarchical relationships are identified by region and State. We discuss implications of rural urbanization processes for resource use and sustainability and finally, the paper challenges the traditional simplified inference of change dynamics, and addresses the issues of complexity and the need for in-depth analysis of changes.
Keywords: urbanization; urban sprawl; sea change; land use change; Australia

Alexandridis, K. (2007). Studying Regional Grazing Viability and Livelihoods in Outback Australia: Lessons from Upper Burdekin Region. Paper at the CSIRO Public Seminar Series - University of Queensland Natural Resource Management Course. CSIRO Davies Laboratory, Townsville, Queensland, Australia, July 16, 2007.

Abstract: This study provides an overview of the sustainable livelihoods frameworks, and its relevance to outback Australia. We apply the concept of sustainable livelihoods not only to individuals or small groups but also to a regional and rural setting. Such regions present significant vulnerabilities due to sparse and declining rural populations, lack of diversification, isolation and remoteness and environmental, climate, and natural resource challgenges. The interplay between the availability of choices and the perceived outcomes is critical in enabling and empowering sustainable livelihoods. We use the upper Burdekin area of northern Queensland as a case study, and show how we use a Bayesian Belief Network modeling approach to study and understand the factors influencing livelihood choices and outcomes.
Keywords: sustainability; sustainable livelihoods; outback; Australia; Bayesian Belief Networks; alternative livelihoods; grazing

Alexandridis, K., and Schandl, H. (2007). Complexity of Urbanization Patterns and Resource Use in Sea Change Communities Across Australia. Paper at the 8th Asia-Pacific Complex Systems Conference (COMPLEX 07), Complexity in Energy, Water and Urban Development Session. Gold Coast, Australia, July 2-5, 2007.

Abstract: Sea Change is a persistent and wide-spread phenomenon across the vast majority of coastal communities of Australia. Urbanization patterns and their relationship with resource patterns of use are gaining recognition in both the scientific and planning communities. The magnitude, structure and degree of resource use in many sea change communities is closely linked to many patterns and magnitudes of change, including demographic, economic, urban development and environmental changes occurring simultaneously, and across multiple spatial and temporal scales. Our ability to respond and/or anticipate future changes in coupled human-environmental systems rests upon our understanding of the systemic interactions, feedbacks and cross-scale linkages that emerge and co-evolve across multiple scales and domains. This approach highlights the benefits of a systematic study of the system characteristics of urban, sub-urban, peri-urban and ex-urban changes with a social and sociological understanding of resource dependencies and accounting, and provides a profiling of sea change communities in both of these dimensions.
Keywords: land use change; sea change; urbanization; urban sprawl; coastal communities

Measham, T. G., Alexandridis, K., and Stone-Jovicitch, S. (2007). Heuristics, complexity and belief networks: a case study of an outback livelihood system. Paper at the 8th Asia-Pacific Complex Systems Conference (COMPLEX 07), Social Science and Management Session. Gold Coast, Australia, July 2-5, 2007.

Abstract: We provide an overview of inductive approaches and how they relate to principles of complex systems such as emergence and local interaction. Drawing on the qualitative social sciences, a key feature is that phenomena are not ‘tested’ but rather heuristically inferred from a detailed analysis of empirical data and qualitative narratives. In some cases, inductive social science can provide a valuable tool for evaluating the ways people learn and respond to challenges involving complexity in their surrounding social and natural coupled systems. In other cases the insights from inductive approaches can reveal interactions between critical drivers which can be modelled using techniques such as graph-theoretic participatory Bayesian Belief Networks. An example of such an approach (BOLNet) is provided in this presentation to showcase and explore an alternative approach to a livelihood system and associated perceptions of viability in an outback grazing regional community system. The presentation will conclude with a discussion of future opportunities for inductive approaches in complex system science.
Keywords: social-ecological systems; heuristics; complexity; complex systems; bayesian belief networks

Alexandridis, K., and Pijanowski, B. C. (2007). Information Entropy-based Techniques for Spatially-Explicit Land Use Model Assessment. Paper at the Framing Land Use Dynamics II International Conference, Paper Session. Utrecht, The Netherlands, April 18-20, 2007, Utrecht University.

Abstract: The aim of this presentation is to explore and develop advanced spatial assessment methods and techniques for land use modeling. Traditional statistical accuracy assessment techniques, although essential for validating observed and historical land use changes, often fail to capture the stochastic character of the modeling dynamics, especially in models where advanced artificial intelligence techniques are used, such as artificial neural networks (ANN), cellular automata (CA), agent-based models (ABM), genetic algorithms (GA), and so on. The presentation provides a comprehensive guide for assessing additional informational entropy value of model predictions, at the spatially explicit domain of knowledge, and proposes a few alternative metrics and indicators that encapsulate the ability of the modeller to extract higher-order information dynamics from simulation tournaments. In recent years, methods, techniques and measures of informational entropy exceeded the single dimensionality of traditional statistical techniques (i.e., measuring uncertainty on single random events or variables) and begun analysing multi-dimensional signals. The concept of spatial entropy presents analysis of informational entropy patterns in two-dimensional spatial systems. Within these lines, the presentation introduces some alternative metrics that aim to assist and enhance the power of our inferential mechanisms in modeling such systems. A seven-county study area in South-Eastern Wisconsin (SEWI) has been used to simulate and assess the accuracy of historical land use changes (1963-1990) using artificial neural network simulations of the Land Transformation Model (LTM) (Pijanowski et al 2002; 2005). A series of additional tools/methods of model assessment are proposed, namely the useof (a) ROC deviance analysis for measuring the simulated model deviance from a theoretical random distribution over a large model ensemble. The concept of ROC deviance is consistent with the definition and functionality of informational entropy; (b) Entropy and Gini-impurity function metrics for quantifying a range of maximum entropy measurements over an ensemble of simulated landscapes; (c) The relationship between diagnostic odds ratio and Bayesian predictive values over both the presence and absence of a classification (prevalence threshold), using the likelihood ratios (LR+, LR-) to estimate a posterior probability classification based on the information embedded in the simulated landscape ensemble; (d) an elaborative kernel and dominance relationship analysis for acquiring the degree of asymmetric information in a model’s posterior density estimation of the classification probabilities; (e) the estimation of Bayes convergence factor over a range of model-theoretic density distribution functions. The practical significance of the proposed additional spatial model assessment metrics is that they can provide an “informational summary” of the simulated region or landscape ensembles. The use of the analysis and the performance of the metrics can help us: (a) Understand and learn how well the model fits to different combinations of presence and absence of transitions in our landscapes, not simply how well the model fits our given data; (b) We can also derive (estimate) a theoretical accuracy that we would expect our model to assess, under the presence of incomplete information and measurement; (c) Understand the role and pattern of uncertainty in our simulations and model estimations. We can compare results across simulation runs and understand the role of spatially-explicit patterns and cell configurations to model training and simulation. (d) Compare the significance or estimation contribution of transitional presence and absence (change versus no change) to our model performance, and the contribution of the spatial drivers and variables to the explanatory value of our model. (e) Compare measurements of informational uncertainty at different scales of spatial resolution. Assessing model uncertainty of predictions for each of spatial resolutions can also enhance our knowledge about modeling at different spatial scales and selecting scales that produce lower uncertainty estimates.
Keywords: entropy; information entropy; land use change; artificial neural networks; social-ecological systems; agent-based models; land transformation modeling

Pijanowski, B. C., Washington-Ottombre, C., and Alexandridis, K. (2007). Using Role-Playing Games, Multi-Criteria Evaluation, Machine Learning and Agent-based Models to Understand Climate-driven Land Use Change. Paper at the Framing Land Use Dynamics II Conference. Utrecht, The Netherlands, April 18-20, 2007, Utrecht University, Faculty of Geosciences and MNP.

Abstract: Land use change is a complex process that results from the interaction of multiple biophysical and socioeconomic factors. We address the need to develop methods that are compatible with quantitative models if we desire to couple to biophysical or socioeconomic models. We are addressing the question whether or not diverse approaches to modeling are complimentary. We provide insights to a number of key approaches: role-playing simulation games, participatory mapping, multi-criteria decision-making and evaluation, reduced-form models of machine learning, and agent-based interaction models. We present a number of qualitative and quantitative results, including RPG scenarios, multi-model comparisons, ABM and Bayesian Belief Network simulations. We conclude that (a) some methods are complementary but integration might be challenging; (b) methods that focus on real-world behavior and social interactions are needed to overcome survey limitations, and; (c) there is a critical need to focus on the information flows and exchange between methods and models.
Keywords: land use change; role-playing; RPG; multi-criteria analysis; machine learning; agent-based models; climate change

Alexandridis, K. (2007). Integrated Intelligent Systems of Study, Analysis and Simulation of Human-Environment Interactions: Perspectives for Land Use, Complexity and Resilience. Paper at the Democritus University, Department of Agricultural Economics Seminar, Invited Talk Session. Komotini, Greece, April 29, 2007, Democritus Univercity of Thrace.

Abstract: We provide an overview of social-ecological systems, resilience and robustness, complex adaptive systems theory, multi-agent systems and uncertainty theory, artificial neural networks, and bayesian belief and decision networks.We focus on simulating and predicting land use changes on geographic space and time, and the implications of land use change on ecosystems, natural resources, water bodies, and hydrology. Using simulations of integrated behaviors in human-natural interactions we can study land use changes in space and time, environmental behaviors, and the characteristics and nature of natural resource transformations. Especially critical is the study and analysis of decision-theoretic and policy-specific scenarios of land use changes, including heuristics of feedback mechanisms, space and time uncertainty patterns, and shifting scale dynamics. We provide an overview of the Multi Agent-based Behavioral Economic Landscape model (MABEL) for studying land use change dynamics and the process of agent-theoretic decision making algorithms (mathematical and machine learning-based simulations).
Keywords: land use change; social-ecological systems; human-environment interactions; agent-based models; intelligent systems

Alexandridis, K. (2007). Elements of Water Quality Change Classifications (WQCC) for Assessing Social Resilience Indicators. Paper at the Program Meeting Marine and Tropical Sciences Research Facility (MTSRF). Cairns, Queensland, Australia, March 7, 2007.

Abstract: We developed a set of water quality change classifications (WQCC) for assessing social resilience indicators in the Great Barrier Reef region of Australia. We use a 3-level nested open system with endogenous probabilistic parameters. The system provides a whole-systems approach to water quality change, and looks at characteristics, components and event distributions under a unified framework. We treat complex system characteristics as endogenous parameters, and beyond quantitative event frequencies looks also at qualitative patterns of water quality change. We model three major water quality categories: physical, chemical, and biological/microbiological, while modeling water use, key event characteristrics (both base and extreme), and key inference characteristics (uncertainty, stochasticity, scenarios, management target/goals and planning objectives).
Keywords: water quality; sustainability; modeling; hazards; risk assessment; simulation; decision sciences

Alexandridis, K. (2006). Local Knowledge, Global Thinking and Regional Modeling: Towards a New Synergistic Paradigm Shift? Paper at the CSIRO Davies Lab Seminar Series, Presentation Session. Townsville, QLD, Australia, February 24, 2006, Commonwealth Scientific and Industrial Organization.

Abstract: Integrating spatial thinking into traditional environmental, social and economic simulations brings forth a common, unified theme to the integration of biophysical and socio-economic systems. Especially, land-use changes are ranked among the top five research priority areas for understanding these changes. In terms of land use change, new tools and methods are needed for better understanding land use attributes and dynamics. We focus on primary drivers of LULC, and on patterns and characteristics of change in the near to long-term future. Climate variability affects land use change and contributes to potential feedback patterns. Furthermore, there are environmental, social, economic and human health consequences of change. In terms of human dimensions, conditions of complexity and uncertainty over such environmental variability affect decision making. We assert that a dual, coupled human-environment framework of analysis can be an appropriate pathway to allow us to understand the complexities and realities observed in real world. This approach provides a rationale for an evolving scientific approach from disciplinary scientific discovery to the science at the frontiers focusing at resilience, adaptation and robustness within an inductive framework. We present an agent-based model, namely the Multi-Agent Behavioral and Environmental Model (MABEL) simulating land use change social-ecological interactions. The ABM modeling approach focuses on spatial, scale, decision, policy and temporal dynamics of change. We developed an advanced mathematical algorithm functionality that measures the degree of transactional dynamics on land use parcel dynamics, and a decision-theoretic mechanisms for sprawl transactional dynamics. We showcase and discuss wider implications for visualization, comprehension and policy-making.
Keywords: local knowledge; agent-based models; regional modeling

Alexandridis, K., Pijanowski, B. C., and Lei, Z. (2004). The Use of Robust and Efficient Methodologies in Agent-Based Modeling: Case Studies using Repeated Measures and Behavioral Components in the MABEL Simulation Model. Paper at the Agent 2004 Conference on Social Dynamics: Interaction, Reflexivity and Emergence. University of Chicago Gleacher Center, Chicago, Illinois, October 7-9, 2004, Argonne National Laboratory.

Abstract: In recent years modeling realistic or “real world” relationships using agent-based modeling is gaining significant grounds. Agent-based modeling is an appropriate tool for modeling such relationships, due to its ability to overcome the generalizations and the statistical moment assumptions of traditional modeling approaches. It follows a bottom-up approach to modeling, allowing for issues of scale, time and space to be taken into account simultaneously in a simulation environment. The purpose of this paper is to demonstrate these significant properties in an agent-based modeling environment, using case-studies as examples. Furthermore, the paper demonstrates the ability of agent-based modeling environments to incorporate and utilize traditional statistical assumptions and properties, in an individual agent level. In such way, individual agent modeling design can be utilized to represent more accurately existing real-world relationships, and to reduce the level of uncertainty for predicting individual and collective agent behaviors for sustainable futures. We use specific case studies from the Multi Agent-based Behavioral Economic Landscape (MABEL) model, to illustrate the usefulness of the proposed methods to the study of land use change, natural resource management, efficiency and environmental-specific considerations that affect the decision-making capabilities of the agents. The use of repeated measures, as Monte-Carlo replication experiments can assist in both the constructability of the agent-based architecture (by testing the accuracy of the agent’s performance compared to historically observed changes), and the confidence of predicting changes in the depth of time (by reducing the uncertainty of the estimates, and providing the necessary near-term range of scenarios for sustainable futures). In our examples we will show how these replication experiments can be designed and incorporated as an integral part of the agent-based simulation environment, and at the same time to be used as reference and accuracy assessment tools using external performance metrics in both statistical and environmental-based assessment schemes. This linking of the agent-based simulation environment with the accuracy assessment metrics, is important and establishes the degree of confidence required by decision-makers and end-users of the simulations. The range of confidence in our simulation replications experiments can be also used as a transition step for achieving the necessary predictability and confidence level for near- and long-term predictions and construction of sustainable futures scenarios. Addressing the agent-based modeling exercises as integral part of a holistic approach to sustainable futures, it is often desired to use prediction ranges of results instead of individual predictions. We then, can advance the simulation veridicality by incorporating uncertainty considerations into our simulation, such as behavioral changes of individual agents, and collective behavioral estimates of agents participating in a simulation, in a more stochastic rather than in a deterministic simulation environment. Designing and constructing behavioral changes in an agent-based modeling requires the presence of an adequate number of plausible and realistic scenarios, able to differentiate the agents’ behavior in the desired degree of abstraction, provide realistic simulation outcomes, reduce the level of uncertainty in the decision-making process, and provide a clear and comprehensive picture of the sustainable futures. In our paper we will provide examples of the procedural steps that can be followed and utilized on such an agent-based modeling environment. Our proposed approach to modeling attempts to provide a working environment for agent-based simulation architecture, that it is not limited to the traditional computational science considerations but takes into account in advance the considerations and assumptions that are necessary to take the simulation results one step further. In other words, it is designed with the end-user and decision-maker in mind, so that robust and efficient outcomes can be back-propagated to the model, in ways that enhances adaptivity and veridicality of our experiments.
Keywords: agent-based modeling; MABEL; land use change; simulation; natural resource management; artificial intelligence; human-natural systems; social-ecological systems

Alexandridis, K. (2004). Using Multi-Agent Systems (MAS) for studying Complex Adaptive Systems: The Semantics of Integration and the Aristotelian Entelechy. Paper at the SEEC: Southeastern Ecology and Evolution Conference. Atlanta, Georgia, March 5-7, 2004, Georgia Institute of Technology.

Abstract: Multi-Agent Systems (MAS) methodologies for representing complex adaptive systems are gaining support due to their ability to overcome the computational, assumption-based and multi-component problems that often encountered in the use of traditional modeling approaches in natural and human systems research. The challenges often faced include our ability to provide long-term, efficient and consistent estimations for sustainable futures, and robust techniques for scenario and decision-making analyses. Coupling human and natural systems also presents a researcher with a plethora of challenges, including the issues of spatio-temporal abstraction, multi-scaling, local versus global and single versus multiple equilibria, “veridicality” versus simplicity of modeling approaches, and cognitive versus mechanistic approaches to modeling. Transforming function into form, the Aristotelian concept of entelechy can empower our understanding of components to be integrated into a semantic representation of reality. MABEL (Multi Agent-based Behavioral Environmental Landscape) model provides an Artificial Intelligence framework of simulating abstract representations of reality for integrated systems components, long-term policy and cognitive scenario analysis and identifying emergent and resilient properties in space, time and scale. The coupling of advanced design and simulation techniques such as dynamic quantitative and qualitative assessment, Bayesian belief networks, role-playing simulations and knowledge-based expert systems, allows for studying the complexity of interactions embedded within our natural world, and provides the necessary robustness of drivers, processes and components necessary for studying long-term changes and sustainable futures.
Keywords: agent-based systems; land use change; modeling; distributed artificial intelligence; MABEL; Bayesian Belief Networks

Alexandridis, K. T., Pijanowski, B. C., and Lei, Z. (2003). Simulating Land-Use Entelechy using Multi Agent-based Behavioral Economic Landscape (MABEL) Model. Paper at the Agent 2003 Conference on: Challenges in Social Simulation. The University of Chicago, Gleacher Center, Chicago, Illinois, October 2-4, 2003, Argonne National Laboratory.

Abstract: Land-Use Entelechy can be described as the process in which function transforms in form. Multi Agent-based Behavioral Economic Landscape (MABEL) model, simulates complex decision-making of agents in a parcel-based framework. The model architecture is based in the Swarm modeling environment, and extends its capabilities via utilization of multiple software components (GIS, statistical and Bayesian Modeling, decision-modeling), and a client-server communication protocol framework. MABEL agents are corresponding to a coupled socio-economic and level-3 GIS classification of the landscape, and conceptualize individuals as land-owners in residential, commercial, agricultural, forested, and other land-use classifications. Parcel-based identification of agents through a GIS-digitized database, is combined with a socio-economic attribute database (representing for each agent the demographic, economic and social characteristics related to its ownership), unique for each initial agent in the simulation. Using a finite-horizon Markov decision-making iterative framework and adaptive Bayesian Belief Networks (BBN's), the agents maximize their expected utility related to land-use change in a land-market framework of land acquisition. The primary set of questions that MABEL simulation addresses is how individual preferences and utility assumptions are related to land-use acquisition and changes observed over time. The model observes the adaptive changes in these preferences and how are related with trends and changes in land-use characteristics and social processes associated with them such as sprawl, loss of agriculture, population-changes, employment and housing characteristics, etc. Interactive decision-making scenarios are utilized to simulate different stream of policy-making rules, and community-based approaches to sustainable futures. The results indicate the variability and versatility of different key decisions associated with planning and policy-making. Inferences for and from the future related to decisions to be made in the present allows for exploring a decision-making horizon, capable to assist policy-makers to achieve their forward-planning goals, identify potential problem areas related to land-use and spatial interactions, and explore the uncertainty and variability of suggested sustainable futures. In the model, the decision-theoretic control of the agents' actions is achieved through a combination of multi-attribute economic utility framework, and a behavioral probabilistic change framework, such as a robust Bayesian classifier for Belief Networks. The transition-decision-action sequence converges to a dual property setting of optimal action - optimal policy, using a non-linear extended Kalman-filtering (EKF) estimation of spatial and temporal noise covariance from observations. The adaptability of agents' through temporal and spatial interactions thus, does not depend on the assumption of fixed (or constant) utility preferences over time, but assesses their decision-theoretic environment through an observation - correction process. Evidence entering the agents' perceptional environment as observations through the landscape, other agents' action-space, and decision-based rules by policy-makers, are capable of modifying or re-evaluating future agent decisions. A critical question to be assessed in this paper is whether a series of sequential local optimal decisions (in a coupled temporal - spatial scale) of the agents, can, and under which policy-making and uncertainty conditions provide the basis of achieving a global optimum (resilient) across these scales.
Keywords: agent-based modeling; entelechy; land use; land use change; MABEL; artificial intelligence; human-natural systems; social-ecological systems;

Lei, Z., Pijanowski, B. C., and Alexandridis, K. T. (2003). Simulation and Distributed Architecture of Multi Agent-Based Behavioral Economic Landscape (MABEL) Model within SWARM. Paper presented at the Agent 2003 Conference on: Challenges in Social Simulation, Gleacher Center, Chicago IL, October 2-4, 2003.

Abstract: The Multi-Agent−based Behavioral Economic Landscape (MABEL) model introduces a distributed modeling architecture framework that supports the simulation of land-use changes over time and space over large regions. The model is based on the Swarm modeling software package, which is supported by a unique client-server framework with multiple interfaces built around a geographic information system (GIS), statistical analysis and database (SPSS), and Bayesian network software. The model architecture supports an integrated simulation environment with remote data retrieval, distributed and parallel scenario simulations, centralized decision-making algorithms, graphic displays for both client and server model components, and analysis capabilities. On the client side of MABEL, computational agents represent Bayesian relations among geographic, environmental, human, and socio-economic variables, with respect to land-use changes occurring across landscapes. A multi-agent simulation environment is created within Swarm, which simulates the buying, selling, and keeping of land by different types of agents. Agents are allowed to participate in an abstract market model. The characteristics of the server side of MABEL include (1) remote data retrieval via multiple interfaces with GIS software (ArcGIS and Arcview) and statistical database software (SPSS), and (2) coordinated agent decision making that allows for decision requests of agents from clients to be made to centralized Bayesian network agent profiles located on the MABEL server.
Keywords: Multi-Agent Based Simulation; multi-agent systems; MABEL; client-server framework; SWARM; Bayesian Belief Networks; land use change

Alexandridis, K., Pijanowski, B. C., and Zheng, L. (2003). MABEL in Action: An Audio-VIsual Poster. Poster at the 2nd Annual Land Use Poster Forum. Kellogg Center, East Lansing, Michigan, February 19, 2003, Victor Institute, Michigan State University.

Abstract: Our Multi-Agent Based Economic Landscape (MABEL) Model simulates the behavior of land use change agents in a spatial-temporal framework using agent based modeling software, geographic information systems, and Bayesian Belief Networks coupled to a dynamic database engine. A high-tech, audio-visual animation has been developed that captures real-time simulations of our model and provides and overview of the basic concepts and technical framework of the model. Users will be able to see the model in action and listen, through headphones, of a voiceover that describes the main points of the model simulations for areas in Grand Traverse Bay and Muskegon River Watersheds.
Keywords: land use change; MABEL; agent-based modeling; Bayesian Belief Networks; computational social science; social simulation

Wiley, M., Pijanowski, B. C., Seelbach, P., Koches, J., and Alexandridis, K. (2003). Integrating Land Use, Water Quality and Fish Diversity Models Using the Valley Segment Ecological Classification (VSEC) Framework: An Update on the Muskegon River Watershed Mega Model Project. Poster at the 2nd Annual Land Use Poster Forum. Kellogg Center, East Lansing, Michigan, February 19, 2003, Victor Institute, Michigan State University.

Abstract: The development of a Muskegon River Environmental Modeling System is the focus of a multi-institutional, model integration project (aka Mega Model Project) that attempts to quantify how the Muskegon River Watershed functions. A series of land use change scenarios based on MSU’s Land Transformation Model (LTM) forecasts have been integrated with hydrologic, nutrient loading, stream geochemistry and fish diversity models to simulate how 200 years of land use/cover change alters water quality and fish diversity. We should here in this poster how this Mega Model is being used to address management and planning scenarios posed by watershed stakeholders.
Keywords: land use change; Muskegon watershed; natural resource management; watershed management; Land transformation modeling; artificial Neural Networiks; hydrologic models; fisheries; land cover; scenario planning

Lei, Z., Alexandridis, K., and Pijanowski, B. C. (2003). Using Swarm to Construct a Multi Agent-Based Economic Landscape (MABEL) Model. Poster at the 2nd Annual Land Use Poster Forum. Kellogg Center, East Lansing, Michigan, February 19, 2003, Victor Institute, Michigan State University.

Abstract: Our Multi Agent-Based Environmental Landscape model (MABEL) employs a Distributed Artificial Intelligence (DAI) framework that simulates agent decisions regarding land use change over time and space. In this paper, we present the software architecture of MABEL that we have developed using the Swarm simulation environment, geographic information systems, statistical software and routines and Microsoft Belief Networks. Within these tools, MABEL contains a marketing module, knowledge database, and a set of decision-making procedures that are integrated using Kalman filters. The intelligent agents represent abstract relations among geographic, environmental, human and socioeconomic variables, with specific self-development (i.e., learning) capabilities and optimal land use decisions assessed using Bayesian conditional probabilities. Based on the decisions of individual agents at each time step, a marketing module schedules agent’s transactions using an optimal marketing principle with rule/policy restrictions, with updates to knowledge database with every time step. We will also present a procedural representation of extracting optimal agent decisions from socio-economic data using Belief Networks (BNs).
Keywords: land use change; MABEL; agent-based models; Bayesian Belief Networks; human-environment system; swarm; distributed artificial intelligence

Campbell, D. J., Pijanowski, B. C., Olson, J., Alexandridis, K., Pithadia, S., and Butt, B. (2003). Unique Methods of Parameterizing a Multi-Agent Behavioral Economic Landscape (MABEL) Model for East Africa. Poster at the 2nd Annual Land Use Poster Forum. Kellogg Center, East Lansing, Michigan, February 19, 2003, Victor Institute, Michigan State University.

Abstract: The critical components of agent based modeling (ABM) are (i) the definition of appropriate or realistic parameters to initialize agent characteristics and (ii) the interpretation and validation of model outcomes. The combination of role-playing simulations (RPS) and ABM represents a recent innovation designed to address issues of parameterization and validation. The poster illustrates the results of an initial experiment in combining RPS and ABM conducted by the Climate Ecology and Land in East Africa (CELEA) research team at Michigan State University.
Keywords: land use change; agent-based models; East Africa; climate change; role-playing simulations; RPG; land use change

Campbell, D. J., Pijanowski, B. C., Olson, J., Alexandridis, K., Lofgren, B., Qi, J., Lusch, D., Andersen, J., Shein, K., and Butt, B. (2003). Addressing the Problem of Pattern and Scale in Climate-Land Feedbacks in East Africa. Poster at the 2nd Annual Land Use Poster Forum. Kellogg Center, East Lansing, Michigan, February 19, 2003, Victor Institute, Michigan State University.

Abstract: The problem of pattern and scale is the central problem in ecology, unifying population biology and ecosystems science, and marrying basic and applied ecology. Applied challenges ... require the interfacing of phenomena that occur on very different scales of space, time, and ecological organization. Furthermore, there is no single natural scale at which ecological phenomena should be studied; systems generally show characteristic variability on a range of spatial, temporal, and organizational scales. (Levin, 1992). The CELEA research team explorers the feedbacks between land use and climate change. This involves the examination of household community level land use dynamics, landscape scale land cover change and regional scale climate change. The range of temporal scales required to understand the land-climate dynamics are from seconds to decades, representing eight orders of magnitude. The study represents a suite of methodologies that allow effective analyses of processes and patterns at different scales. It includes household surveys, remote sensing analysis and regional climate modeling. Addressing linkages between scales is the most challenging aspect of the project. Computational social science provides a context for our operation and includes agent based modeling, land transformation model (LTM) and land cover change projections.
Keywords: land use change; scale; spatial scales; climate change; feedbacks; climate variability

Alexandridis, K. T., and Pijanowski, B. C. (2002). Multi Agent-Based Environmental Landscape (MABEL) - An Artificial Intelligence Simulation Model: Some Early Assessments. Paper at the AERE/EAERE: 2002 World Congress of Environmental and Resource Economists. Monterey, California, June 24-27.

Abstract: The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving goals and representing existing relations observed in real-world scenarios, and goal-based efficiency. Intelligent MABEL agents acquire spatial expressions and perform specific tasks demonstrating autonomy, environmental interactions, communication and cooperation, reactivity and proactivity, reasoning and learning capabilities. Their decisions maximize both task-specific marginal utility for their actions and joint, weighted marginal utility for their time-stepping. Agent behavior is achieved by personalizing a dynamic utility-based knowledge base through sequential GIS filtering, probability-distributed weighting, joint probability Bayesian correlational weighting, and goal-based distributional properties, applied to socio-economic and behavioral criteria. First-order logics, heuristics and appropriation of time-step sequences employed, provide a simulation-able environment, capable of re-generating space-time evolution of the agents.
Keywords: agent-based modeling; distributed artificial intelligence; computational social science; GIS; Bayesian Belief networks; human-natural systems