Endogenous regulation of selection pressure: A minimal game-theoretical model

IF 2.1 4区 生物学 Q2 BIOLOGY
Biosystems Pub Date : 2026-01-01 Epub Date: 2025-12-02 DOI:10.1016/j.biosystems.2025.105666
Bruno Gašperov, Branko Šter
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引用次数: 0

Abstract

Selection pressure, determining which individuals survive and reproduce, is traditionally regarded as an exogenous property of the environment. However, growing evidence from the Extended Evolutionary Synthesis (EES) suggests that the strength of selection can, to some extent, be modulated by the organisms themselves. In this paper, we propose a minimal game-theoretical model that formalizes the concept of endogenous selection pressure. In the model, individuals collectively determine the intensity of selection through their actions: each agent “votes” to either intensify or relax selection depending on its relative fitness rank. The aggregate outcome of these decisions defines the number of reproducers, k, which is inversely related to the overall strength of selection. We analytically derive all pure strategy Nash equilibria of the game and examine their properties. By internalizing selection as a population-level emergent variable, the framework provides a tractable formalization of key principles from the EES and offers a foundation for exploring evolutionary dynamics that arise under the presence of endogenous selection pressure.

Abstract Image

选择压力的内源性调节:一个最小博弈论模型。
决定个体生存和繁殖的选择压力,传统上被认为是环境的外生特性。然而,来自扩展进化综合(EES)的越来越多的证据表明,在某种程度上,选择的强度可以由生物体本身调节。在本文中,我们提出了一个最小博弈论模型,该模型形式化了内生选择压力的概念。在该模型中,个体通过他们的行为共同决定选择的强度:每个个体根据其相对适合度等级“投票”加强或放松选择。这些决策的总结果决定了繁殖者的数量k,这与选择的总体强度成反比。我们解析地导出了博弈的所有纯策略纳什均衡,并检验了它们的性质。通过将选择内化为种群水平的突现变量,该框架为EES的关键原则提供了一个易于处理的形式化框架,并为探索内源性选择压力下产生的进化动力学提供了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biosystems
Biosystems 生物-生物学
CiteScore
3.70
自引率
18.80%
发文量
129
审稿时长
34 days
期刊介绍: BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.
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