1. Identification of influential invaders in evolutionary populationsGuoli Yang, Tina Perc Benko, Matteo Cavaliere, Jincai Huang, Matjaž Perc, 2019, original scientific article Abstract: The identification of the most influential nodes has been a vibrant subject of research across the whole of network science. Here we map this problem to structured evolutionary populations, where strategies and the interaction network are both subject to change over time based on social inheritance. We study cooperative communities, which cheaters can invade because they avoid the cost of contributions that are associated with cooperation. The question that we seek to answer is at which nodes cheaters invade most successfully. We propose the weighted degree decomposition to identify and rank the most influential invaders. More specifically, we distinguish two kinds of ranking based on the weighted degree decomposition. We show that a ranking strategy based on negative-weighted degree allows to successfully identify the most influential invaders in the case of weak selection, while a ranking strategy based on positive-weighted degree performs better when the selection is strong. Our research thus reveals how to identify the most influential invaders based on statistical measures in dynamically evolving cooperative communities.
Keywords: theoretical biology, evolution, agent-based modeling, complex system, network science, evolutionary game theory
Published in DKUM: 26.02.2025; Views: 0; Downloads: 4
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2. Ranking the invasions of cheaters in structured populationsGuoli Yang, Matteo Cavaliere, Cheng Zhu, Matjaž Perc, 2020, original scientific article 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.
Keywords: cooperation, cheating, network, population, evolution
Published in DKUM: 07.01.2025; Views: 0; Downloads: 4
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3. Strategically positioning cooperators can facilitate the contagion of cooperationGuoli Yang, Matteo Cavaliere, Cheng Zhu, Matjaž Perc, 2021, original scientific article Abstract: The spreading of cooperation in structured population is a challenging problem which can be observed at diferent scales of social and biological organization. Generally, the problem is studied by evaluating the chances that few initial invading cooperators, randomly appearing in a network, can lead to the spreading of cooperation. In this paper we demonstrate that in many scenarios some cooperators are more infuential than others and their initial positions can facilitate the spreading of cooperation. We investigate six diferent ways to add initial cooperators in a network of cheaters, based on diferent network-based measurements. Our research reveals that strategically positioning the initial cooperators in a population of cheaters allows to decrease the number of initial cooperators necessary to successfully seed cooperation. The strategic positioning of initial cooperators can also help to shorten the time necessary for the restoration of cooperation. The optimal ways in which the initial cooperators should be placed is, however, non-trivial in that it depends on the degree of competition, the underlying game, and the network structure. Overall, our results show that, in structured populations, few cooperators, well positioned in strategically chosen places, can spread cooperation faster and easier than a large number of cooperators that are placed badly.
Keywords: cooperation, evolutionary game theory, social physics, collective dynamics, complex system
Published in DKUM: 22.10.2024; Views: 0; Downloads: 3
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