Inferring strategies from observations in long iterated Prisoner's dilemma experiments

被引:7
|
作者
Montero-Porras, Eladio [1 ]
Grujic, Jelena [1 ]
Domingos, Elias Fernandez [1 ,2 ]
Lenaerts, Tom [1 ,2 ,3 ,4 ]
机构
[1] Vrije Univ Brussel, Artificial Intelligence Lab, B-1050 Brussels, Belgium
[2] Univ Libre Bruxelles, Machine Learning Grp, B-1050 Brussels, Belgium
[3] Univ Calif Berkeley, Ctr Human Compatible AI, Berkeley, CA 94702 USA
[4] Vrije Univ Brussel, FARI Inst, Univ Libre Bruxelles, B-1050 Brussels, Belgium
关键词
TIT-FOR-TAT; EVOLUTION; COOPERATION; BEHAVIOR;
D O I
10.1038/s41598-022-11654-2
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
While many theoretical studies have revealed the strategies that could lead to and maintain cooperation in the Iterated Prisoner's dilemma, less is known about what human participants actually do in this game and how strategies change when being confronted with anonymous partners in each round. Previous attempts used short experiments, made different assumptions of possible strategies, and led to very different conclusions. We present here two long treatments that differ in the partner matching strategy used, i.e. fixed or shuffled partners. Here we use unsupervised methods to cluster the players based on their actions and then Hidden Markov Model to infer what the memory-one strategies are in each cluster. Analysis of the inferred strategies reveals that fixed partner interaction leads to behavioral self-organization. Shuffled partners generate subgroups of memory-one strategies that remain entangled, apparently blocking the self-selection process that leads to fully cooperating participants in the fixed partner treatment. Analyzing the latter in more detail shows that AllC, AllD, TFT- and WSLS-like behavior can be observed. This study also reveals that long treatments are needed as experiments with less than 25 rounds capture mostly the learning phase participants go through in these kinds of experiments.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Inferring strategies from observations in long iterated Prisoner’s dilemma experiments
    Eladio Montero-Porras
    Jelena Grujić
    Elias Fernández Domingos
    Tom Lenaerts
    Scientific Reports, 12
  • [2] Stationary strategies in iterated prisoner's dilemma
    Levchenkov V.S.
    Levchenkova L.G.
    Computational Mathematics and Modeling, 2006, 17 (3) : 254 - 273
  • [3] Shopkeeper Strategies in the Iterated Prisoner's Dilemma
    Ashlock, Daniel
    Kuusela, Christopher
    Cojocaru, Monica
    2011 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2011, : 1063 - 1070
  • [4] Invincible Strategies of Iterated Prisoner's Dilemma
    Wang, Shiheng
    Lin, Fangzhen
    AAMAS '19: PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS, 2019, : 2256 - 2258
  • [5] The Competitions of Forgiving Strategies in the Iterated Prisoner's Dilemma
    Binmad, Ruchdee
    Li, Mingchu
    Deonauth, Nakema
    Hungsapruek, Theerawat
    Limwudhikraijirath, Aree
    2018 IEEE INTERNATIONAL CONFERENCE ON AGENTS (ICA), 2018, : 39 - 43
  • [6] Properties of winning Iterated Prisoner's Dilemma strategies
    Glynatsi, Nikoleta E.
    Knight, Vincent
    Harper, Marc
    PLOS COMPUTATIONAL BIOLOGY, 2024, 20 (12)
  • [7] New Winning Strategies for the Iterated Prisoner's Dilemma
    Mathieu, Philippe
    Delahaye, Jean-Paul
    JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION, 2017, 20 (04):
  • [8] Partners or rivals? Strategies for the iterated prisoner's dilemma
    Hilbe, Christian
    Traulsen, Arne
    Sigmund, Karl
    GAMES AND ECONOMIC BEHAVIOR, 2015, 92 : 41 - 52
  • [9] New Winning Strategies for the Iterated Prisoner's Dilemma
    Mathieu, Philippe
    Delahaye, Jean-Paul
    PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS & MULTIAGENT SYSTEMS (AAMAS'15), 2015, : 1665 - 1666
  • [10] Fault-tolerant strategies in the Iterated Prisoner's Dilemma
    Pelc, Andrzej
    INFORMATION PROCESSING LETTERS, 2010, 110 (10) : 389 - 395