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
东皇太憨发布了新的文献求助10
刚刚
刚刚
夏歌蝉完成签到,获得积分10
1秒前
kk关闭了kk文献求助
1秒前
2秒前
汉堡包应助xyj6486采纳,获得10
2秒前
3秒前
3秒前
JamesPei应助李呆采纳,获得10
4秒前
4秒前
cqrneu完成签到,获得积分10
5秒前
裴佳晨完成签到,获得积分10
5秒前
丘比特应助XY采纳,获得10
6秒前
6秒前
7秒前
Akim应助落日余晖采纳,获得10
7秒前
刘俊发布了新的文献求助10
8秒前
8秒前
8秒前
8秒前
9秒前
爆米花应助刻苦的电脑采纳,获得10
9秒前
科研通AI6.1应助DKH采纳,获得10
9秒前
Owen应助mmol采纳,获得10
10秒前
清淮完成签到 ,获得积分10
10秒前
10秒前
小李发布了新的文献求助10
10秒前
lukas发布了新的文献求助10
11秒前
烟花应助长情的凌旋采纳,获得10
11秒前
jiangcy完成签到,获得积分10
12秒前
冷静若雁完成签到,获得积分10
12秒前
12秒前
12秒前
文鞅发布了新的文献求助10
12秒前
李紫悠发布了新的文献求助10
12秒前
13秒前
14秒前
15秒前
小蘑菇应助Chew1q采纳,获得10
15秒前
yan完成签到,获得积分10
15秒前
高分求助中
Adhesion Science: Principles & Practice 1234
Signals, Systems, and Signal Processing 610
Burger's Medicinal Chemistry and Drug Discovery 400
A Step-by-Step Guide to Qualitative Data Coding 2nd Edition 400
Impact of Storage Orientation and Duration on Prefilled Syringe Performance: Break-Loose and Glide Forces, and Injection Time Across Multiple Time Points 360
Programming for Chemical Engineers Using C, C++, and MATLAB 300
Upland Kenya wild flowers and ferns: a flora of the flowers, ferns, grasses, and sedges of highland Kenya 300
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 6667543
求助须知:如何正确求助?哪些是违规求助? 8416963
关于积分的说明 17992820
捐赠科研通 5875291
什么是DOI,文献DOI怎么找? 2976555
邀请新用户注册赠送积分活动 1952477
关于科研通互助平台的介绍 1880081