Egocentric Bias and Doubt in Cognitive Agents

TL;DR

This paper introduces a model for social interactions that incorporates egocentric bias and domain-based self-doubt, allowing agents to adaptively reduce bias and improve cooperation in faction-based systems.

Contribution

It presents a novel approach to modeling egocentric bias with a symmetric distribution and incorporates self-doubt to enable reactive learning in agents.

Findings

Agents in factions outperform individual agents.

Intermediate egocentricity levels optimize agent performance.

Self-doubt reduces egocentricity and enhances cooperation.

Abstract

Modeling social interactions based on individual behavior has always been an area of interest, but prior literature generally presumes rational behavior. Thus, such models may miss out on capturing the effects of biases humans are susceptible to. This work presents a method to model egocentric bias, the real-life tendency to emphasize one's own opinion heavily when presented with multiple opinions. We use a symmetric distribution centered at an agent's own opinion, as opposed to the Bounded Confidence (BC) model used in prior work. We consider a game of iterated interactions where an agent cooperates based on its opinion about an opponent. Our model also includes the concept of domain-based self-doubt, which varies as the interaction succeeds or not. An increase in doubt makes an agent reduce its egocentricity in subsequent interactions, thus enabling the agent to learn reactively. The agent system is modeled with factions not having a single leader, to overcome some of the issues associated with leader-follower factions. We find that agents belonging to factions perform better than individual agents. We observe that an intermediate level of egocentricity helps the agent perform at its best, which concurs with conventional wisdom that neither overconfidence nor low self-esteem brings benefits.