Probabilistic fair behaviors spark its boost in the Ultimatum game: the strength of good Samaritans

Author:

Zheng Guozhong,Zhang Jiqiang,Liang Rizhou,Ma Lin,Chen LiORCID

Abstract

Abstract Behavioral experiments on the Ultimatum game have shown that we human beings have a remarkable preference in fair play, contradicting the predictions by the game theory. Most of the existing models seeking for explanations, however, strictly follow the assumption of Homo economicus in orthodox economics that people are self-interested and fully rational to maximize their earnings. Here we relax this assumption by allowing that people probabilistically choose to be ‘good Samaritans’, acting as fair players from time to time. For well-mixed and homogeneously structured populations, we numerically show that as this probability increases the level of fairness undergoes from the low scenario abruptly to the full fairness state, where occasional fair behaviors ( 5 % ) are sufficient to drive the whole population to behave in the half–half split manner. We also develop a mean-field theory, which correctly reproduces the first-order phase transition and points out the reason. Heterogeneously structured populations, however, display continuous fairness transition; surprisingly, very few hub nodes acting as fair players are able to entrain the whole population to the full fairness state. Our results thus reveal the unexpected strength of ‘good Samaritans’, which may constitute a new explanation for the emergence of fairness in our society.

Funder

National Natural Science Foundation of China

Central University Basic Research Fund of China

Publisher

IOP Publishing

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Information Systems

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3