To Trust or Not to Trust: Evolutionary Dynamics of an Asymmetric N-Player Trust Game

独裁者赛局 模仿 明示信任 进化博弈论 随机博弈 激励 微观经济学 可信赖性 计算信任 亲社会行为 进化动力学 盲目信任 博弈论 计算机科学 业务 经济 声誉 心理学 社会心理学 互联网隐私 公共关系 法学 社会学 政治学 人口学 人口
作者
Ik Soo Lim,Naoki Masuda
出处
期刊:IEEE Transactions on Evolutionary Computation [Institute of Electrical and Electronics Engineers]
卷期号:28 (1): 117-131 被引量:30
标识
DOI:10.1109/tevc.2023.3244537
摘要

Trusting others and reciprocating the received trust with trustworthy actions are fundaments of economic and social interactions. The trust game (TG) is widely used for studying trust and trustworthiness and entails a sequential interaction between two players, an investor and a trustee. It requires at least two strategies or options for an investor (e.g. to trust versus not to trust a trustee). According to the evolutionary game theory, the antisocial strategies (e.g. not to trust) evolve such that the investor and trustee end up with lower payoffs than those that they would get with the prosocial strategies (e.g. to trust). A generalisation of the TG to a multiplayer (i.e. more than two players) TG was recently proposed. However, its outcomes hinge upon two assumptions that various real situations may substantially deviate from: (i) investors are forced to trust trustees and (ii) investors can turn into trustees by imitation and vice versa. We propose an asymmetric multiplayer TG that allows investors not to trust and prohibits the imitation between players of different roles; instead, investors learn from other investors and the same for trustees. We show that the evolutionary game dynamics of the proposed TG qualitatively depends on the nonlinearity of the payoff function and the amount of incentives collected from and distributed to players through an institution. We also show that incentives given to trustees can be useful and sufficient to cost-effectively promote trust and trustworthiness among self-interested players.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
冲俨完成签到 ,获得积分10
刚刚
小熊发布了新的文献求助10
刚刚
yuanyuan发布了新的文献求助10
刚刚
科研通AI6.4应助张睿采纳,获得10
刚刚
无限莫言发布了新的文献求助10
刚刚
1秒前
li完成签到,获得积分10
2秒前
xinxin完成签到,获得积分10
2秒前
sagitar应助神龙大冲冠军采纳,获得60
3秒前
科研通AI6.2应助杨新苗采纳,获得10
3秒前
bikazz完成签到,获得积分10
3秒前
8R60d8应助杨新苗采纳,获得30
3秒前
易迅发布了新的文献求助10
3秒前
Cambridge完成签到,获得积分10
4秒前
月亮不会奔你而来完成签到,获得积分10
5秒前
苹果亦巧发布了新的文献求助10
5秒前
HOME发布了新的文献求助10
5秒前
李欣月发布了新的文献求助10
5秒前
5秒前
桃桃发布了新的文献求助10
6秒前
6秒前
Abyssence发布了新的文献求助10
6秒前
6秒前
wenwen流完成签到,获得积分10
6秒前
水秀完成签到,获得积分10
7秒前
酷波er应助yuanyuan采纳,获得10
8秒前
老牛爱耕田关注了科研通微信公众号
9秒前
9秒前
9秒前
科研通AI6.2应助liyi采纳,获得10
9秒前
搜集达人应助暗弱采纳,获得10
9秒前
孤独的狼完成签到,获得积分10
10秒前
10秒前
10秒前
bai应助bikazz采纳,获得10
11秒前
科研通AI6.4应助西瓜采纳,获得10
12秒前
深情安青应助香香香采纳,获得10
12秒前
12秒前
筝zheng完成签到 ,获得积分10
12秒前
12秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Principles of town planning: translating concepts to applications 1000
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The Effective Clinical Neurologist 3ed 500
The Great Hymn to Šamaš 500
Moody's Ratings Rising AI spending narrows the gap, but US hyperscalers retain edge over Chinese peers 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7695603
求助须知:如何正确求助?哪些是违规求助? 9256102
关于积分的说明 20000825
捐赠科研通 7270082
什么是DOI,文献DOI怎么找? 3292521
关于科研通互助平台的介绍 2448209
邀请新用户注册赠送积分活动 2298160