A co-evolutionary approach to data-driven agent-based modelling: simulating the virtual interaction application experiments.

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).