The Ultimatum Game in complex networks

被引:72
|
作者
Sinatra, R. [1 ,2 ,3 ]
Iranzo, J. [4 ,5 ]
Gomez-Gardenes, J. [1 ,5 ,6 ]
Floria, L. M. [5 ,7 ]
Latora, V. [1 ,2 ,3 ]
Moreno, Y. [8 ]
机构
[1] Scuola Super Catania, Lab Sistemi Complessi, I-95123 Catania, Italy
[2] Catania Univ, Dipartimento Fis & Astron, I-95123 Catania, Italy
[3] Ist Nazl Fis Nucl, I-95123 Catania, Italy
[4] Ctr Astrobiol CSIC INTA, Madrid 28850, Spain
[5] Univ Zaragoza, Inst Biocomputat & Phys Complex Syst BIFI, E-50009 Zaragoza, Spain
[6] Univ Rey Juan Carlos, ESCET, Dept Matemat Aplicada, Madrid 28933, Spain
[7] Univ Zaragoza, Dept Fis Mat Condensada, E-50009 Zaragoza, Spain
[8] Univ Zaragoza, Dept Theoret Phys, E-50009 Zaragoza, Spain
来源
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT | 2009年
关键词
network dynamics; applications to game theory and mathematical economics; socio-economic networks; ALTRUISTIC PUNISHMENT; COOPERATION; EVOLUTION; BEHAVIOR; MODEL;
D O I
10.1088/1742-5468/2009/09/P09012
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
We address the problem of how cooperative (altruistic-like) behavior arises in natural and social systems by analyzing an Ultimatum Game in complex networks. Specifically, players of three types are considered: (a) empathetic, whose aspiration levels, and offers, are equal, (b) pragmatic, who do not distinguish between the different roles and aim to obtain the same benefit, and (c) agents whose aspiration levels, and offers, are independent. We analyze the asymptotic behavior of pure populations with different topologies using two kinds of strategic update rules: natural selection, which relies on replicator dynamics, and social penalty, inspired by the Bak-Sneppen dynamics, in which players are subject to a social selection rule penalizing not only the less fit individuals, butalso their first neighbors. We discuss the emergence of fairness in the different settings and network topologies.
引用
收藏
页数:22
相关论文
共 50 条
  • [41] Adaptive role switching promotes fairness in networked ultimatum game
    Wu, Te
    Fu, Feng
    Zhang, Yanling
    Wang, Long
    SCIENTIFIC REPORTS, 2013, 3
  • [42] Coevolution of spatial ultimatum game and link weight promotes fairness
    Deng, Lili
    Zhang, Xingxing
    Wang, Cheng
    APPLIED MATHEMATICS AND COMPUTATION, 2021, 392
  • [43] Preference and strategy in proposer's prosocial giving in the ultimatum game
    Inaba, Misato
    Inoue, Yumi
    Akutsu, Satoshi
    Takahashi, Nobuyuki
    Yamagishi, Toshio
    PLOS ONE, 2018, 13 (03):
  • [44] Fairness in the multi-proposer-multi-responder ultimatum game
    Krakovska, Hana
    Hanel, Rudolf
    Broom, Mark
    PLOS ONE, 2025, 20 (03):
  • [45] Random allocation of pies promotes the evolution of fairness in the Ultimatum Game
    Wang, Xiaofeng
    Chen, Xiaojie
    Wang, Long
    SCIENTIFIC REPORTS, 2014, 4
  • [46] Testosterone Administration Decreases Generosity in the Ultimatum Game
    Zak, Paul J.
    Kurzban, Robert
    Ahmadi, Sheila
    Swerdloff, Ronald S.
    Park, Jang
    Efremidze, Levan
    Redwine, Karen
    Morgan, Karla
    Matzner, William
    PLOS ONE, 2009, 4 (12):
  • [47] Is the Human Fairness Innate or Learned-Evidence from the Ultimatum Game
    Luo Xinqing
    Xie Junjie
    Liu Jianfeng
    PROCEEDINGS OF THE 2018 4TH INTERNATIONAL CONFERENCE ON HUMANITIES AND SOCIAL SCIENCE RESEARCH (ICHSSR 2018), 2018, 213 : 81 - 88
  • [48] Is costly punishment altruistic? Exploring rejection of unfair offers in the Ultimatum Game in real-world altruists
    Brethel-Haurwitz, Kristin M.
    Stoycos, Sarah A.
    Cardinale, Elise M.
    Huebner, Bryce
    Marsh, Abigail A.
    SCIENTIFIC REPORTS, 2016, 6
  • [49] Are patients with schizophrenia rational maximizers? Evidence from an ultimatum game study
    Csukly, Gabor
    Polgar, Patricia
    Tombor, Laszlo
    Rethelyi, Janos
    Keri, Szabolcs
    PSYCHIATRY RESEARCH, 2011, 187 (1-2) : 11 - 17
  • [50] Accuracy in strategy imitations promotes the evolution of fairness in the spatial ultimatum game
    Szolnoki, Attila
    Perc, Matjaz
    Szabo, Gyoergy
    EPL, 2012, 100 (02)