| Title: | Ranking the invasions of cheaters in structured populations |
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| Authors: | ID Yang, Guoli (Author)
ID Cavaliere, Matteo (Author)
ID Zhu, Cheng (Author)
ID Perc, Matjaž (Author) |
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| Files: |  Yang-2020-Ranking_the_invasions_of_cheaters_in.pdf (6,72 MB)
MD5: 06367E32F175ED79B7ADDA504BB84C95
 https://doi.org/10.1038/s41598-020-59020-4
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FNM - Faculty of Natural Sciences and Mathematics
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| Abstract: | The identification of the most influential individuals in structured populations is an important research question, with many applications across the social and natural sciences. Here, we study this problem in evolutionary populations on static networks, where invading cheaters can lead to the collapse of cooperation. We propose six strategies to rank the invading cheaters and identify those which mostly facilitate the collapse of cooperation. We demonstrate that the type of successful rankings depend on the selection strength, the underlying game, and the network structure. We show that random ranking has generally little ability to successfully identify invading cheaters, especially for the stag-hunt game in scale-free networks and when the selection strength is strong. The ranking based on degree can successfully identify the most influential invaders when the selection strength is weak, while more structured rankings perform better at strong selection. Scale-free networks and strong selection are generally detrimental to the performance of the random ranking, but they are beneficial for the performance of structured rankings. Our research reveals how to identify the most influential invaders using statistical measures in structured communities, and it demonstrates how their success depends on population structure, selection strength, and on the underlying game dynamics. |
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| Keywords: | cooperation, cheating, network, population, evolution |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 17.12.2019 |
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| Article acceptance date: | 24.01.2020 |
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| Publication date: | 10.02.2020 |
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| Publisher: | Nature Publishing Group |
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| Year of publishing: | 2020 |
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| Number of pages: | Str. 1-13 |
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| Numbering: | Letn. 10, št. članka 2231 |
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| PID: | 20.500.12556/DKUM-90131  |
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| UDC: | 53 |
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| ISSN on article: | 2045-2322 |
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| COBISS.SI-ID: | 25113864 |
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| DOI: | 10.1038/s41598-020-59020-4 |
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| Publication date in DKUM: | 07.01.2025 |
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| Views: | 0 |
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| Downloads: | 4 |
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| Metadata: |    |
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| Categories: | Misc.
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