Facilitating cooperation in human-agent hybrid populations through autonomous agents

Summary

Cooperative AI has shown its effectiveness in solving the conundrum of cooperation. Understanding how cooperation emerges in human-agent hybrid populations is a topic of significant interest, particularly in the realm of evolutionary game theory. In this article, we scrutinize how cooperative and defective Autonomous Agents (AAs) influence human cooperation in social dilemma games with a one-shot setting. Focusing on well-mixed populations, we find that cooperative AAs have a limited impact in the prisoner’s dilemma games but facilitate cooperation in the stag hunt games. Surprisingly, defective AAs can promote complete dominance of cooperation in the snowdrift games. As the proportion of AAs increases, both cooperative and defective AAs have the potential to cause human cooperation to disappear. We then extend our investigation to consider the pairwise comparison rule and complex networks, elucidating that imitation strength and population structure are critical for the emergence of human cooperation in human-agent hybrid populations.

Graphical abstract

Highlights

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  Modeling cooperative and defective autonomous agents in social dilemmas

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  In snowdrift/stag hunt games, a minority of autonomous agents drives full cooperation

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  More autonomous agents can disrupt cooperation

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  Autonomous agents at hub nodes wield influence

Abstract

Statistical physics; Computer science; Artificial intelligence