Oral Defense of Doctoral Dissertation – Computational Social Science – Polity Cycling in Great Zimbabwe via Agent-Based Modeling: The Effects of Timing and Magnitude of External Factors – Gary Keith Bogle
Oral Defense of Doctoral Dissertation
Doctor of Philosophy in Computational Social Science
Department of Computational and Data Sciences
College of Science
George Mason University
Gary Keith Bogle
Bachelor of Arts, University of California, Davis, 1990
Master of Arts, University of Illinois at Urbana-Champaign, 1995
Master of Science, Marymount University, 2003
Polity Cycling in Great Zimbabwe via Agent-Based Modeling:
The Effects of Timing and Magnitude of External Factors
Thursday, April 11, 2019, 1:00 p.m.
Research Hall, Room 92
All are invited to attend.
Claudio Cioffi-Revilla, Chair
This research explores polity cycling at the site of Great Zimbabwe. It rests on laying out the possibilities that may explain what is seen in the archaeological record in terms of modeling what external factors, operating at specific times and magnitudes. What can cause a rapid rise and decline in the polity? This is explored in terms of attachment that individuals feel towards the small groups of which they are a part of, and the change in this attachment in response to their own resources and the history of success that the group enjoys in conducting collective action. The model presented in this research is based on the Canonical Theory of politogenesis. It is implemented using an agent-based model as this type of model excels at generating macro-level behavior from micro-level decisions. The results of this research cover the relationship between environmental inputs and the pattern of growth and decline of groups, the differences in group fealty and resources between successful groups and unsuccessful groups, the change in the number of groups throughout the simulation and the relationship between the probability of success in collective action and the success of the groups themselves. The input parameters to the model presented here are the collective action frequency (CAF) and environmental effect multiplier. The results show that a prehistoric polity can be modeled to demonstrate a sharp rise and fall in community groups and that the rise and fall emerges from the individual decision-making. Different sets of input parameters represent different environmental conditions, from the stable and predictable to less stable to quite unpredictable. Regardless of the environmental variability, the overall value of fealty experienced by community members moves in a similar fashion for all input sets. However, the more stable environment of Set A means the overall feelings of attachment to leadership do not fall as fast as they do in the more variable environments. In all, there is a two-stage process in which members in the community are sorted in to the surviving groups. Success in collective action leads to overall group success. The significance of this research is that it provides a basis for understanding that, while the archaeological record is incomplete, what happened in Great Zimbabwe lies within what has happened in other areas. What seems at first glance to be unusual can be explained through expected environmental and social factors that affect prehistoric societies on other continents. Furthermore, this research provides the basis for further quantifying the analysis of prehistoric societies by providing a model of laying out external factors along the lines of collective action frequencies and environmental effect multipliers.