Evolutionary instability of selfish learning in repeated games

被引:9
|
作者
McAvoy, Alex [1 ,2 ]
Kates-Harbeck, Julian [3 ]
Chatterjee, Krishnendu [4 ]
Hilbe, Christian [5 ]
机构
[1] Univ Penn, Dept Math, Philadelphia, PA 19104 USA
[2] Univ Penn, Ctr Math Biol, Philadelphia, PA 19104 USA
[3] Harvard Univ, Dept Phys, Cambridge, MA USA
[4] IST Austria, Klosterneuburg, Austria
[5] Max Planck Inst Evolutionary Biol, Max Planck Res Grp Dynam Social Behav, Plon, Germany
来源
PNAS NEXUS | 2022年 / 1卷 / 04期
基金
欧洲研究理事会;
关键词
INFINITELY REPEATED GAMES; SOCIAL PREFERENCES; RANDOM SEARCH; COOPERATION; DYNAMICS; STRATEGIES; FAIRNESS; EFFICIENCY; EXTORTION; ANSWER;
D O I
10.1093/pnasnexus/pgac141
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Across many domains of interaction, both natural and artificial, individuals use past experience to shape future behaviors. The results of such learning processes depend on what individuals wish to maximize. A natural objective is one's own success. However, when two such "selfish" learners interact with each other, the outcome can be detrimental to both, especially when there are conflicts of interest. Here, we explore how a learner can align incentives with a selfish opponent. Moreover, we consider the dynamics that arise when learning rules themselves are subject to evolutionary pressure. By combining extensive simulations and analytical techniques, we demonstrate that selfish learning is unstable in most classical two-player repeated games. If evolution operates on the level of long-run payoffs, selection instead favors learning rules that incorporate social (other-regarding) preferences. To further corroborate these results, we analyze data from a repeated prisoner's dilemma experiment. We find that selfish learning is insufficient to explain human behavior when there is a trade-off between payoff maximization and fairness.
引用
收藏
页数:15
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