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The evolution of fairness in the coevolutionary ultimatum games

Author

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  • Miyaji, Kohei
  • Wang, Zhen
  • Tanimoto, Jun
  • Hagishima, Aya
  • Kokubo, Satoshi

Abstract

In the ultimatum games, two players are required to split a certain sum of money. Through the responder accepting the offer of proposer, the money will be shared and the fairness is built finally. Here, we figure out five coevolutionary protocols, where strategy (offering rate p and threshold for accepting an offer q) and underlying network topology can coevolve, to demonstrate how the link severing scenario affects the evolution of fairness. We show that the equilibrium of the games is significantly influenced by these coevolutionary protocols. The deterministic rules lead to overly lavish or overly generous result that is inconsistent with the outcome of human behavior experiment. However, the probabilistic rules produce fair division, similar to the realistic case. Moreover, we also introduce an amplitude parameter b to verify the plausibility of assumed link severing protocols. By means of enhancing b we analytically exhibit that preferable performance can be obtained in the game, since the total amount of agents increases as well. Last, we further support our conclusion by showing the so-called unrealistic severing events under these coevolution scenarios. We thus present a viable way of understanding the ubiquitous fairness in nature and hope that it will inspire further studies to resolve social division.

Suggested Citation

  • Miyaji, Kohei & Wang, Zhen & Tanimoto, Jun & Hagishima, Aya & Kokubo, Satoshi, 2013. "The evolution of fairness in the coevolutionary ultimatum games," Chaos, Solitons & Fractals, Elsevier, vol. 56(C), pages 13-18.
  • Handle: RePEc:eee:chsofr:v:56:y:2013:i:c:p:13-18
    DOI: 10.1016/j.chaos.2013.05.007
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    Cited by:

    1. Deng, Lili & Wang, Hongsi & Wang, Rugen & Xu, Ronghua & Wang, Cheng, 2024. "The adaptive adjustment of node weights based on reputation and memory promotes fairness," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    2. Deng, Lili & Zhang, Xingxing & Wang, Cheng, 2021. "Coevolution of spatial ultimatum game and link weight promotes fairness," Applied Mathematics and Computation, Elsevier, vol. 392(C).
    3. Alberto Antonioni & Maria Paula Cacault & Rafael Lalive & Marco Tomassini, 2014. "Know Thy Neighbor: Costly Information Can Hurt Cooperation in Dynamic Networks," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-8, October.
    4. Jia Li & Xiaolin Zhou, 2014. "Sex, Attractiveness, and Third-Party Punishment in Fairness Consideration," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-6, April.
    5. Jianguo Ren & Yonghong Xu, 2014. "Modelling the Effects of Selection Temperature and Mutation on the Prisoner’s Dilemma Game on a Complete Oriented Star," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-9, October.
    6. Zhao, Yakun & Xiong, Tianyu & Zheng, Lei & Li, Yumeng & Chen, Xiaojie, 2020. "The effect of similarity on the evolution of fairness in the ultimatum game," Chaos, Solitons & Fractals, Elsevier, vol. 131(C).
    7. Ramzi Suleiman, 2017. "Economic Harmony: An Epistemic Theory of Economic Interactions," Games, MDPI, vol. 8(1), pages 1-15, January.
    8. Valerio Capraro & Conor Smyth & Kalliopi Mylona & Graham A Niblo, 2014. "Benevolent Characteristics Promote Cooperative Behaviour among Humans," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-6, August.

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