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Abstract: In this paper, we explore the impact of four different types of dissimilarity-driven behavior on the evolution of cooperation in the spatial public goods game. While it is commonly assumed that individuals adapt their strategy by imitating one of their more successful neighbors, in reality only very few will be awarded the highest payoffs. Many have equity or equality preferences, and they have to make do with an average or even with a low payoff. To account for this, we divide the population into two categories. One consists of payoff-driven players, while the other consists of dissimilarity-driven players. The later imitate the minority strategy in their group based on four different dissimilaritydriven behaviors. The rule that most effectively promotes cooperation, and this regardless of the multiplication factor of the public goods game, is when individuals adopt the minority strategy only when their payoff is better than that of their neighbors. If the dissimilarity-driven players adopt the minority strategy regardless of the payoffs of others, or if their payoff is the same, the population typically evolves towards a neutral state where cooperators and defectors are equally common. This may be beneficial when the multiplication factor is low, when defectors would otherwise dominate. However, if the dissimilarity-driven players adopt the minority strategy only when their payoff is worse than that of their neighbors, then cooperation is not promoted at all in comparison to the baseline case in the absence of dissimilarity-driven behavior. We explore the pattern formation behind these results, and we discuss their wider implications for the better understanding of cooperative behavior in social groups.
Keywords: theoretical biology, evolution, agent-based modeling, complex system, network science, evolutionary game theory
Published in DKUM: 26.02.2025; Views: 0; Downloads: 3
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Identification of influential invaders in evolutionary populations
Guoli 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: 2
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Action-Based Digital Characterization of a Game Player
Damijan Novak, Domen Verber, Jani Dugonik, Iztok Fister, 2023, original scientific article

Keywords: association rule mining, digital characterization, game agent, game player, real-time strategy games
Published in DKUM: 23.05.2024; Views: 131; Downloads: 8
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