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  Aspiration dynamics in structured population acts as if in a well-mixed one

Du, J., Wang, L., & Wu, B. (2015). Aspiration dynamics in structured population acts as if in a well-mixed one. Scientific Reports, 5: 8014. doi:10.1038/srep08014.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0024-CB06-B Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0024-CB07-9
Genre: Journal Article

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Du_Wu_Wang_2015.pdf (Publisher version), 972KB
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This work is licensed under a Creative Commons Attribution-NonCommercial- NoDerivs 4.0 International License.

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 Creators:
Du, Jinming, Author
Wang, Long, Author
Wu, Bin1, Author              
Affiliations:
1Research Group Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_1445641              

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Free keywords: Evolutionary theory; Computational biophysics; Statistics
 Abstract: Understanding the evolution of human interactive behaviors is important. Recent experimental results suggest that human cooperation in spatial structured population is not enhanced as predicted in previous works, when payoff-dependent imitation updating rules are used. This constraint opens up an avenue to shed light on how humans update their strategies in real life. Studies via simulations show that, instead of comparison rules, self-evaluation driven updating rules may explain why spatial structure does not alter the evolutionary outcome. Though inspiring, there is a lack of theoretical result to show the existence of such evolutionary updating rule. Here we study the aspiration dynamics, and show that it does not alter the evolutionary outcome in various population structures. Under weak selection, by analytical approximation, we find that the favored strategy in regular graphs is invariant. Further, we show that this is because the criterion under which a strategy is favored is the same as that of a well-mixed population. By simulation, we show that this holds for random networks. Although how humans update their strategies is an open question to be studied, our results provide a theoretical foundation of the updating rules that may capture the real human updating rules.

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Language(s): eng - English
 Dates: 2014-10-012014-12-232015-01-26
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1038/srep08014
 Degree: -

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Title: Scientific Reports
  Abbreviation : Sci. Rep.
Source Genre: Journal
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Publ. Info: London, UK : Nature Publishing Group
Pages: 7 S. Volume / Issue: 5 Sequence Number: 8014 Start / End Page: - Identifier: Other: 2045-2322 (online)
CoNE: /journals/resource/2045-2322