ArticleNature communications2021
Aspiration dynamics generate robust predictions in heterogeneous populations.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Behavioral alignment in social networks.National science review · 2025Article
- Evolutionary dynamics of behavioral motivations for cooperation.Nature communications · 2025Article
- Deterministic theory of evolutionary games on temporal networks.Journal of the Royal Society, Interface · 2024Article
- Cooperation among unequal players with aspiration-driven learning.Journal of the Royal Society, Interface · 2024Article
- Imitation dynamics on networks with incomplete information.Nature communications · 2023Article
- Evolutionary dynamics on sequential temporal networks.PLoS computational biology · 2023Article
- Optimization of institutional incentives for cooperation in structured populations.Journal of the Royal Society, Interface · 2023Article
- Evolutionary multiplayer games on graphs with edge diversity.PLoS computational biology · 2019Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Update rules, which describe how individuals adjust their behavior over time, affect the outcome of social interactions. Theoretical studies have shown that evolutionary outcomes are sensitive to model details when update rules are imitation-based but are robust when update rules are self-evaluation based. However, studies of self-evaluation based rules have focused on homogeneous population structures where each individual has the same number of neighbors. Here, we consider heterogeneous population structures represented by weighted networks. Under weak selection, we analytically derive the condition for strategy success, which coincides with the classical condition of risk-dominance. This condition holds for all weighted networks and distributions of aspiration levels, and for individualized ways of self-evaluation. Our findings recover previous results as special cases and demonstrate the universality of the robustness property under self-evaluation based rules. Our work thus sheds light on the intrinsic difference between evolutionary dynamics under self-evaluation based and imitation-based update rules.
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Registered trials
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