Selfish optimization and collective learning in populations

McAvoy, Alex; Mori, Yoichiro; Plotkin, Joshua B.

doi:10.48550/arxiv.2111.07776

Cited by 1 publication

(1 citation statement)

References 27 publications

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“…Nevertheless, the recent breakthrough in reinforcement (deep) learning of zero-sum games [30], like the Go [31], can lend some insight into the study of non-zerosum games where learning agents, despite being self-serving, can mutually foster cooperation for the greater good under certain conditions [32]. Thus, the classic framework of IPD still has the potential to be used as a primary testbed for synergistically combining artificial intelligence (AI) and game theory in future work [18,33,34], all with an eye towards helping us to enhance global cooperation in many challenging issues confronting our common humanity [35].…”

Section: Discussionmentioning

confidence: 99%

Outlearning Extortioners by Fair-minded Unbending Strategies

Chen¹,

Fu²

2022

Preprint

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Recent theory shows that extortioners taking advantage of the zero-determinant (ZD) strategy can unilaterally claim an unfair share of the payoffs in the Iterated Prisoner's Dilemma. It is thus suggested that against a fixed extortioner, any adapting co-player should be subdued with full cooperation as their best response. In contrast, recent experiments demonstrate that human players often choose not to accede to extortion out of concern for fairness, actually causing extortioners to suffer more loss than themselves. In light of this, here we reveal fairminded strategies that are unbending to extortion such that any payoff-maximizing extortioner ultimately will concede in their own interest by offering a fair split in head-to-head matches. We find and characterize multiple general classes of such unbending strategies, including generous zero-determinant strategies and Win-Stay, Lose-Shift as particular examples. When against fixed unbending players, extortioners are forced with consequentially increasing losses whenever intending to demand more unfair share. Our analysis also pivots to the importance of payoff structure in determining the superiority of zero-determinant strategies and in particular their extortion ability. We show that an extortionate ZD player can be even outperformed by, for example, Win-Stay Lose-Shift, if the total payoff of unilateral cooperation is smaller than that of mutual defection. Unbending strategies can be used to outlearn evolutionary extortioners and catalyze the evolution of Tit-for-Tat-like strategies out of ZD players. Our work has implications for promoting fairness and resisting extortion so as to uphold a just and cooperative society.

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