A co-evolutionary approach to data-driven agent-based modelling: simulating the virtual interaction application experiments.
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Date
2023
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Abstract
The dynamics of social interactions are barely captured by the traditional methods of research in social
psychology, vis-à-vis, interviews, surveyed data and experiments. To capture the dynamics of social
interactions, researchers adopt computer-mediated experiments and agent-based simulations (ABSs).
These methods have been efficiently applied to game theories.
While strategic games such as the prisoner’s dilemma and GO have optimal outcomes, interactive
social exchanges can have obscure and multiple conflicting objectives (fairness, selfishness, group
bias) whose relative importance evolves in interaction. Discovering and understanding the
mechanisms underlying these objectives become even more difficult when there is little or no
information about the interacting individual(s). This study describes this as an information-scarce
interactive social exchange context. This study, therefore, forms part of a larger initiative on
developing efficient simulations of social interaction in an information-scarce interactive social
exchange context.
First, this dissertation develops a context for and justifies the importance of simulation in an
information-scarce interactive social exchange context (Chapter 2). It then performs a literature review
of the studies that have developed a computational model and simulation in this context (Chapter 3).
Next, the dissertation develops a co-evolutionary data-driven model and simulates exchange behaviour
in an information-scarce context (Chapter 4). To benchmark the data-driven model, this dissertation
develops a rule-based model. Furthermore, it creates agents that use the rule-based model, integrates
them into Virtual Interaction APPLication (VIAPPL) and tests their usefulness in predicting and
influencing exchange decisions. Precisely, it measures the agent’s ability in reducing in-group bias
during interaction in an information-scarce context (Chapter 5). Likewise, it creates machine learning
(adaptive) agents that use the data-drivel model, and tests them in a similar experimental context.
These chapters were written independently; thus, their objectives, methods and results are discussed
in each chapter. Finally, the study presents a general conclusion (Chapter 6).
Description
Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.