Multi-Agent, Human-Agent and Beyond: A Survey on Cooperation in Social Dilemmas

TL;DR

This survey reviews recent AI advancements in fostering cooperation among multiple agents, humans, and AI-human interactions in social dilemmas, highlighting new methods, challenges, and future research directions.

Contribution

It provides a comprehensive overview of AI techniques for cooperation in social dilemmas across multi-agent, human-agent, and human-human contexts, and discusses future research avenues.

Findings

AI enhances cooperation strategies among rational agents.

AI algorithms facilitate cooperation with humans and address biases.

Emerging applications leverage AI to improve human cooperation.

Abstract

The study of cooperation within social dilemmas has long been a fundamental topic across various disciplines, including computer science and social science. Recent advancements in Artificial Intelligence (AI) have significantly reshaped this field, offering fresh insights into understanding and enhancing cooperation. This survey examines three key areas at the intersection of AI and cooperation in social dilemmas. First, focusing on multi-agent cooperation, we review the intrinsic and external motivations that support cooperation among rational agents, and the methods employed to develop effective strategies against diverse opponents. Second, looking into human-agent cooperation, we discuss the current AI algorithms for cooperating with humans and the human biases towards AI agents. Third, we review the emergent field of leveraging AI agents to enhance cooperation among humans. We conclude by discussing future research avenues, such as using large language models, establishing unified theoretical frameworks, revisiting existing theories of human cooperation, and exploring multiple real-world applications.