Modeling Confirmation Bias Through Egoism and Trust in a Multi Agent System

Permanent memory, and biases that humans have such as confirmation bias, are not modeled in some of the leading strategies in Iterated Prisoner's Dilemma. This reduces their effectiveness at modeling actual human behaviors. As a solution to this problem we have proposed a framework to model egoistic agents using trust. This framework accounts for such flaws in human behavior, and provides a more accurate representation of human nature by modeling confirmation bias through egoism and trust. The agents of this model divide their neighboring agents into two sets, I and Non-I, and the actions taken against a neighbor are influenced by the set it belongs to. This framework can be used to modify existing strategies such as Tit-For-Tat to make them more realistic. Using this framework, we have also proposed a trust based strategy and evaluated it. Our strategy does better than the other strategies in pairwise comparisons, and also when there is a combination of different agents in the environment.