Promoting Cooperation in the Public Goods Game using Artificial Intelligent Agents

TL;DR

This paper investigates how AI agents can promote cooperation in public goods games, finding that mimicking human behavior reduces the cooperation threshold and helps resolve social dilemmas.

Contribution

It introduces novel AI strategies, especially mimicking human players, to enhance cooperation in public goods scenarios, surpassing traditional regulatory methods.

Findings

Mimicking human behavior lowers the cooperation threshold.

Mandatory AI cooperation does not significantly improve outcomes.

Evolving AI control over cooperation likelihood has limited impact.

Abstract

The tragedy of the commons illustrates a fundamental social dilemma where individual rational actions lead to collectively undesired outcomes, threatening the sustainability of shared resources. Strategies to escape this dilemma, however, are in short supply. In this study, we explore how artificial intelligence (AI) agents can be leveraged to enhance cooperation in public goods games, moving beyond traditional regulatory approaches to using AI as facilitators of cooperation. We investigate three scenarios: (1) Mandatory Cooperation Policy for AI Agents, where AI agents are institutionally mandated always to cooperate; (2) Player-Controlled Agent Cooperation Policy, where players evolve control over AI agents' likelihood to cooperate; and (3) Agents Mimic Players, where AI agents copy the behavior of players. Using a computational evolutionary model with a population of agents playing public goods games, we find that only when AI agents mimic player behavior does the critical synergy threshold for cooperation decrease, effectively resolving the dilemma. This suggests that we can leverage AI to promote collective well-being in societal dilemmas by designing AI agents to mimic human players.