A Reputation Game Simulation: Emergent Social Phenomena from Information Theory

TL;DR

This paper introduces a novel reputation game simulation that models social phenomena emerging from information compression and uncertainty, providing insights into social dynamics and malicious strategies in AI and human interactions.

Contribution

It presents a new approach to information updating in reputation models using compression and uncertainty, leading to emergent phenomena like echo chambers and deception.

Findings

Emergent phenomena such as echo chambers and self-deception observed.

Malicious strategies impact group sociology significantly.

Modeling offers insights into social interactions and counteracting malicious behaviors.

Abstract

Reputation is a central element of social communications, be it with human or artificial intelligence (AI), and as such can be the primary target of malicious communication strategies. There is already a vast amount of literature on trust networks addressing this issue and proposing ways to simulate these networks dynamics using Bayesian principles and involving Theory of Mind models. The main issue for these simulations is usually the amount of information that can be stored and is usually solved by discretising variables and using hard thresholds. Here we propose a novel approach to the way information is updated that accounts for knowledge uncertainty and is closer to reality. In our game, agents use information compression techniques to capture their complex environment and store it in their finite memories. The loss of information that results from this leads to emergent phenomena, such as echo chambers, self-deception, deception symbiosis, and freezing of group opinions. Various malicious strategies of agents are studied for their impact on group sociology, like sycophancy, egocentricity, pathological lying, and aggressiveness. Even though our modeling could be made more complex, our set-up can already provide insights into social interactions and can be used to investigate the effects of various communication strategies and find ways to counteract malicious ones. Eventually this work should help to safeguard the design of non-abusive AI systems.