Unbiased third-party bots lead to a tradeoff between cooperation and social payoffs

TL;DR

This study investigates how unbiased third-party AI bots influence cooperation in social dilemmas, revealing they can promote cooperation but also reduce overall social payoffs, especially in structured populations.

Contribution

The paper introduces an evolutionary simulation showing that unbiased third-party bots can enhance cooperation in structured populations but cause a tradeoff with social payoffs.

Findings

Unbiased bots do not alter defective equilibria in well-mixed populations.

Bots promote cooperation more effectively when applying negative influence.

Increasing bots improves cooperation but decreases overall social payoffs.

Abstract

The rise of artificial intelligence (AI) offers new opportunities to influence cooperative dynamics with greater applicability and control. In this paper, we examine the impact of third-party bots--agents that do not directly participate in games but unbiasedly modify the payoffs of normal players engaged in prisoner's dilemma interactions--on the emergence of cooperation. Using an evolutionary simulation model, we demonstrate that unbiased bots are unable to shift the defective equilibrium among normal players in well-mixed populations. However, in structured populations, despite their unbiased actions, the bots spontaneously generate distinct impacts on cooperators and defectors, leading to enhanced cooperation. Notably, bots that apply negative influences are more effective at promoting cooperation than those applying positive ones, as fewer bots are needed to catalyze cooperative behavior among normal players. However, as the number of bots increases, a trade-off emerges: while cooperation is maintained, overall social payoffs decline. These findings highlight the need for careful management of AI's role in social systems, as even well-intentioned bots can have unintended consequences on collective outcomes.