Computational evolution of social norms in well-mixed and group-structured populations

群(周期表) 心理学 化学 有机化学
作者
Yohsuke Murase,Christian Hilbe
出处
期刊:Proceedings of the National Academy of Sciences of the United States of America [National Academy of Sciences]
卷期号:121 (33)
标识
DOI:10.1073/pnas.2406885121
摘要

Models of indirect reciprocity study how social norms promote cooperation. In these models, cooperative individuals build up a positive reputation, which in turn helps them in their future interactions. The exact reputational benefits of cooperation depend on the norm in place, which may change over time. Previous research focused on the stability of social norms. Much less is known about how social norms initially evolve when competing with many others. A comprehensive evolutionary analysis, however, has been difficult. Even among the comparably simple space of so-called third-order norms, there are thousands of possibilities, each one inducing its own reputation dynamics. To address this challenge, we use large-scale computer simulations. We study the reputation dynamics of each third-order norm and all evolutionary transitions between them. In contrast to established work with only a handful of norms, we find that cooperation is hard to maintain in well-mixed populations. However, within group-structured populations, cooperation can emerge. The most successful norm in our simulations is particularly simple. It regards cooperation as universally positive, and defection as usually negative-unless defection takes the form of justified punishment. This research sheds light on the complex interplay of social norms, their induced reputation dynamics, and population structure.

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
斯文败类应助arpeggione采纳,获得10
刚刚
1秒前
1秒前
yexushifeng完成签到,获得积分10
1秒前
1秒前
赘婿应助风趣的梦易采纳,获得10
1秒前
2秒前
2秒前
2秒前
2秒前
科研通AI2S应助科研通管家采纳,获得10
2秒前
汉堡包应助科研通管家采纳,获得10
2秒前
2秒前
xzy998应助科研通管家采纳,获得50
2秒前
科研通AI2S应助科研通管家采纳,获得10
2秒前
2秒前
仁爱糖豆完成签到 ,获得积分10
2秒前
耶耶小豆包完成签到,获得积分10
3秒前
yxh020807发布了新的文献求助10
3秒前
3秒前
懒懒羊完成签到,获得积分10
4秒前
6秒前
CodeCraft应助度华容采纳,获得10
6秒前
自由意志完成签到,获得积分10
7秒前
7秒前
7秒前
8秒前
Guko发布了新的文献求助10
9秒前
对对对发布了新的文献求助10
10秒前
愚人发布了新的文献求助10
10秒前
潘潘啊完成签到,获得积分20
10秒前
kuromi完成签到,获得积分10
10秒前
10秒前
11秒前
微醺小王发布了新的文献求助10
11秒前
壮观手套发布了新的文献求助10
12秒前
嘻嘻哈哈发布了新的文献求助30
12秒前
wsw111发布了新的文献求助10
12秒前
机智的砖家完成签到,获得积分10
12秒前
英姑应助Kn采纳,获得10
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
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Picture this! Including first nations fiction picture books in school library collections 1500
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
Rheumatoid arthritis drugs market analysis North America, Europe, Asia, Rest of world (ROW)-US, UK, Germany, France, China-size and Forecast 2024-2028 500
17α-Methyltestosterone Immersion Induces Sex Reversal in Female Mandarin Fish (Siniperca Chuatsi) 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 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6364905
求助须知:如何正确求助?哪些是违规求助? 8178927
关于积分的说明 17239565
捐赠科研通 5420001
什么是DOI,文献DOI怎么找? 2867850
邀请新用户注册赠送积分活动 1844885
关于科研通互助平台的介绍 1692352