Value-behavior inconsistency is robust to promote cooperative behavior in structured populations

The evolution of cooperation is a theme commonly studied in biology, psychology, sociology, and economics. Mechanisms that promote cooperative behavior in structured populations have been intensively studied. However, individuals' values, specifically, their opinions have been rarely taken into account so far. Inspired by cognition dissonance theory, we assume that individuals pay the cost of guiltiness if the behavior is defection but the opinion deviates from defection, and pay the cost of regret if the behavior is cooperation but the opinion deviates from cooperation. For all general stochastic evolutionary dynamics on arbitrary static networks with multiple opinions, we prove in the weak selection limit that: (i) value-behavior inconsistency cost promotes cooperative behavior if and only if the average cost of regret is less than that of guiltiness; (ii) individuals with value-behavior consistency are more abundant than that with value-behavior inconsistency. This is in contrast with other mechanisms that are at work for cooperation for one population structure but not others. Furthermore, it is also validated on an empirical network and for non-weak selection intensity. The value-behavior inconsistency is thus a robust mechanism to promote cooperative behavior in structured populations. Our results shed light on the importance of the co-evolutionary dynamics of opinion and behavior, which opens an avenue for cooperation.

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Publication Details

Journal
Chaos An Interdisciplinary Journal of Nonlinear Science
Published
2024-12-01
DOI
https://doi.org/10.1063/5.0242898
Citations
1
Primary Topic
Evolutionary Game Theory and Cooperation
Type
article
Field-Weighted Citation Impact
1.13

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article

Value-behavior inconsistency is robust to promote cooperative behavior in structured populations

Bin Wu, Shuyang Shao
1 citations
Chaos An Interdisciplinary Journal of Nonlinear Science
Evolutionary Game Theory and Cooperation
1.13
article

Value-behavior inconsistency is robust to promote cooperative behavior in structured populations

Bin Wu, Shuyang Shao
article en
1 citations

Abstract

The evolution of cooperation is a theme commonly studied in biology, psychology, sociology, and economics. Mechanisms that promote cooperative behavior in structured populations have been intensively studied. However, individuals' values, specifically, their opinions have been rarely taken into account so far. Inspired by cognition dissonance theory, we assume that individuals pay the cost of guiltiness if the behavior is defection but the opinion deviates from defection, and pay the cost of regret if the behavior is cooperation but the opinion deviates from cooperation. For all general stochastic evolutionary dynamics on arbitrary static networks with multiple opinions, we prove in the weak selection limit that: (i) value-behavior inconsistency cost promotes cooperative behavior if and only if the average cost of regret is less than that of guiltiness; (ii) individuals with value-behavior consistency are more abundant than that with value-behavior inconsistency. This is in contrast with other mechanisms that are at work for cooperation for one population structure but not others. Furthermore, it is also validated on an empirical network and for non-weak selection intensity. The value-behavior inconsistency is thus a robust mechanism to promote cooperative behavior in structured populations. Our results shed light on the importance of the co-evolutionary dynamics of opinion and behavior, which opens an avenue for cooperation.

Chaos An Interdisciplinary Journal of Nonlinear ScienceVol. 34(12)
Beijing University of Posts and Telecommunications (CN)
National Natural Science Foundation of China
Partnerships for the goals
Openalex Percentile: Top 15%
Evolutionary Game Theory and Cooperation
1.13
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Value-behavior inconsistency is robust to promote cooperative behavior in structured populations — Bin Wu, Shuyang Shao · Chaos An Interdisciplinary Journal of Nonlinear Science (2024) | TGRS Research Map | TGRS