A NATURALISTIC COMPUTATIONAL MODEL OF HUMAN BEHAVIOR IN NAVIGATION AND SEARCH TASKS

Planning, navigation, and search are fundamental human cognitive abilities central to spatial problem solving in search and rescue, law enforcement, and military operations. Despite a wealth of literature concerning naturalistic spatial problem solving in animals, literature on naturalistic spatial problem solving in humans is comparatively lacking and generally conducted by separate camps among which there is little crosstalk. Addressing this deficiency will allow us to predict spatial decision making in operational environments, and understand the factors leading to those decisions. The present dissertation is comprised of two related efforts, (1) a set of empirical research studies intended to identify characteristics of planning, execution, and memory in naturalistic spatial problem solving tasks, and (2) a computational modeling effort to develop a model of naturalistic spatial problem solving. The results of the behavioral studies indicate that problem space hierarchical representations are linear in shape, and that human solutions are produced according to multiple optimization criteria. The Mixed Criteria Model presented in this dissertation accounts for global and local human performance in a traditional and naturalistic Traveling Salesman Problem. The results of the empirical and modeling efforts hold implications for basic and applied science in domains such as problem solving, operations research, human-computer interaction, and artificial intelligence

Modeling Memes: A Memetic View of Affordance Learning

This research employed systems social science inquiry to build a synthesis model that would be useful for modeling meme evolution. First, a formal definition of memes was proposed that balanced both ontological adequacy and empirical observability. Based on this definition, a systems model for meme evolution was synthesized from Shannon Information Theory and elements of Bandura\u27s Social Cognitive Learning Theory. Research in perception, social psychology, learning, and communication were incorporated to explain the cognitive and environmental processes guiding meme evolution. By extending the PMFServ cognitive architecture, socio-cognitive agents were created who could simulate social learning of Gibson affordances. The PMFServ agent based model was used to examine two scenarios: a simulation to test for potential memes inside the Stanford Prison Experiment and a simulation of pro-US and anti-US meme competition within the fictional Hamariyah Iraqi village. The Stanford Prison Experiment simulation was designed, calibrated, and tested using the original Stanford Prison Experiment archival data. This scenario was used to study potential memes within a real-life context. The Stanford Prison Experiment simulation was complemented by internal and external validity testing. The Hamariyah Iraqi village was used to analyze meme competition in a fictional village based upon US Marine Corps human terrain data. This simulation demonstrated how the implemented system can infer the personality traits and contextual factors that cause certain agents to adopt pro-US or anti-US memes, using Gaussian mixture clustering analysis and cross-cluster analysis. Finally, this research identified significant gaps in empirical science with respect to studying memes. These roadblocks and their potential solutions are explored in the conclusions of this work

Cultural landscapes and behavioral transformations:An agent-based model for the simulation and discussion of alternative landscape futures in East Lesvos, Greece

Agricultural intensification and abandonment have been identified as two of the more prominent and polarizing drivers of landscape change in Europe. These transitions may induce deterioration in landscape functioning and character, particularly in cultural landscapes demonstrative of evolving human-environment dynamics that have sustained environmental benefits through time. Cultural and behavioral motives are important root influences to such landscape transitions, yet efforts to address landscape degradation are often hampered by a failure to account for the heterogeneous decision-making nature of its agents of change and the inherent complexity of socio-ecological systems. Novel techniques are required to further disentangle responses to multi-level drivers and discuss alternative landscape development trajectories. Agent-based models constructed by means of participatory approaches present increasingly applied tools in this context. This study sought to capture and model the future perspectives emerging from presently occurring farming discourses in the region of Gera (Lesvos, Greece), characterized by persistent abandonment of its traditionally managed olive plantations. We constructed an agent-based model iteratively in collaboration with the local farming community and experts in landscape research. Empirical findings informed the model through the construction of a farmer typology, revealing a heavy reliance of the farming community upon sectorial profitability, prevalent cultural farming motives and emerging landscape initiatives. The model examined the de-coupled role of agricultural profitability and landscapes initiatives in shaping the behavior of land managers, mapping alternative landscape futures over a period of 25 years. Model results illustrate both increased profitability and action by landscape initiatives are required to reverse abandonment trends within the simulated time frame. The hypothesized ability of landscape initiatives to maintain and promote a cultural drive amongst adhering farmers is crucial for securing behavioral transformations towards professionalism. This study confirmed agent-based modelling to be intuitively received by stakeholders who significantly contributed to model structure refinement and the rejection of a status quo scenario

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond