空间博弈中叛逃者引发的柳青飞行如何影响合作演化

IF 1.6 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Life Pub Date : 2023-03-01 DOI:10.1162/artl_a_00382
Genki Ichinose;Daiki Miyagawa;Erika Chiba;Hiroki Sayama
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引用次数: 0

摘要

个人之间的合作一直是维持社会的关键。然而,自然选择倾向于背叛而不是合作。当个人的流动性允许合作者形成集群(或群体)时,合作就会受到青睐。动物的移动模式有时遵循lsamvy飞行。lsamvy飞行是一种随机行走,但它由许多小动作和一些大动作组成。Antonioni和Tomassini研究了lsamvy飞行在合作中的作用,他们表明lsamvy飞行促进了合作,并结合了由邻居叛逃者引发的有条件移动。然而,对于邻近的叛逃者来说,最优条件是什么,以及这种条件如何随着偷渡的强度而变化,目前还不清楚。在这里,我们开发了一个基于agent的方形格子模型,其中agent根据相邻叛逃者的比例执行lsamvy飞行。我们系统地研究了三个因素之间的合作关系:对叛逃者的敏感性、lsamvy逃亡的强度和人口密度。进化模拟结果表明,中等敏感性最能促进合作。然后,我们发现,当对叛逃者的敏感度高时,最短的动作最适合合作。相反,当灵敏度较低时,较长的动作最有利于合作。因此,lsamvy飞行,短距离和长距离跳跃之间的平衡,促进了任何敏感性的合作,进化模拟证实了这一点。最后,随着种群密度的增大,越高的敏感性越有利于合作进化。我们的研究强调,lsamvy飞行不仅是觅食的最佳策略,也是与其他同伴建立合作关系的最佳策略。
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How Lévy Flights Triggered by the Presence of Defectors Affect Evolution of Cooperation in Spatial Games
Cooperation among individuals has been key to sustaining societies. However, natural selection favors defection over cooperation. Cooperation can be favored when the mobility of individuals allows cooperators to form a cluster (or group). Mobility patterns of animals sometimes follow a Lévy flight. A Lévy flight is a kind of random walk but it is composed of many small movements with a few big movements. The role of Lévy flights for cooperation has been studied by Antonioni and Tomassini, who showed that Lévy flights promoted cooperation combined with conditional movements triggered by neighboring defectors. However, the optimal condition for neighboring defectors and how the condition changes with the intensity of Lévy flights are still unclear. Here, we developed an agent-based model in a square lattice where agents perform Lévy flights depending on the fraction of neighboring defectors. We systematically studied the relationships among three factors for cooperation: sensitivity to defectors, the intensity of Lévy flights, and population density. Results of evolutionary simulations showed that moderate sensitivity most promoted cooperation. Then, we found that the shortest movements were best for cooperation when the sensitivity to defectors was high. In contrast, when the sensitivity was low, longer movements were best for cooperation. Thus, Lévy flights, the balance between short and long jumps, promoted cooperation in any sensitivity, which was confirmed by evolutionary simulations. Finally, as the population density became larger, higher sensitivity was more beneficial for cooperation to evolve. Our study highlights that Lévy flights are an optimal searching strategy not only for foraging but also for constructing cooperative relationships with others.
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来源期刊
Artificial Life
Artificial Life 工程技术-计算机:理论方法
CiteScore
4.70
自引率
7.70%
发文量
38
审稿时长
>12 weeks
期刊介绍: Artificial Life, launched in the fall of 1993, has become the unifying forum for the exchange of scientific information on the study of artificial systems that exhibit the behavioral characteristics of natural living systems, through the synthesis or simulation using computational (software), robotic (hardware), and/or physicochemical (wetware) means. Each issue features cutting-edge research on artificial life that advances the state-of-the-art of our knowledge about various aspects of living systems such as: Artificial chemistry and the origins of life Self-assembly, growth, and development Self-replication and self-repair Systems and synthetic biology Perception, cognition, and behavior Embodiment and enactivism Collective behaviors of swarms Evolutionary and ecological dynamics Open-endedness and creativity Social organization and cultural evolution Societal and technological implications Philosophy and aesthetics Applications to biology, medicine, business, education, or entertainment.
期刊最新文献
Complexity, Artificial Life, and Artificial Intelligence. Neurons as Autoencoders. Evolvability in Artificial Development of Large, Complex Structures and the Principle of Terminal Addition. Investigating the Limits of Familiarity-Based Navigation. Network Bottlenecks and Task Structure Control the Evolution of Interpretable Learning Rules in a Foraging Agent.
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