Representing Trust in Cognitive Social Simulations

摘要

Trust plays a critical role in communications, strength of relationships, and information processing at the individual and group levels. Cognitive social simulations show promise in providing an experimental platform for the examination of social phenomena such as trust formation. This work is a novel attempt at trust representation in a cognitive social simulation using reinforcement learning algorithms. Initial algorithm development was completed within a standalone social network simulation and tested using a public commodity game. Evaluation of the contributions and dividends within the public commodity game shows that many of the expected behaviors of human trust formation are present. Initial results show that reinforcement learning can accurately capture the core essentials of human trust formation. Following standalone testing, the trust algorithm was imported into the Cultural Geography model for large-scale test and evaluation.