空间公共物品博弈中一种新的合作规则:二阶社会学习

IF 5.3 1区 数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Nanrong He , Qiuxiang Lu , Fang Yan , Qiang Wang
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引用次数: 0

摘要

在现实世界中,维持合作往往具有挑战性;然而,个体向同伴学习的学习规则可能会戏剧性地重塑进化动力。在现实中,基于模仿的传统社会学习(TSL)依赖于对近邻的复制,这限制了信息采样的深度。为了克服这个问题,我们引入了一种新的二阶社会学习(SOSL)规则,拓宽了社会信息采样的视野。具体来说,SOSL采用了两步信息采样过程:个体不直接复制成功邻居的策略,而是采用成功邻居通过TSL规则确定为最优的策略。通过对格子结构群体的公共物品博弈进行蒙特卡罗模拟,与TSL规则相比,我们发现SOSL规则显著降低了合作和充分合作的关键协同因素的出现条件。即使在TSL规则下协同因素过低而无法产生合作的情况下,SOSL规则也维持了合作集群,加速了从背叛到广泛合作的转变。此外,通过引入允许个体适应其学习模式的策略偏好协同进化框架,我们发现在适度协同因素下,群体压倒性地收敛于SOSL规则。最后,SOSL规则的合作优势在不同的网络结构中都能保持,即使在有噪声的参数条件下也能保持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel rule for cooperation in the spatial public goods game: Second-order social learning
In real-world contexts, sustaining cooperation is often challenging; however, the learning rule by which individuals learn from their peers may dramatically reshape evolutionary dynamics. In reality, imitation-based traditional social learning (TSL) is inherently constrained by its reliance on copying only immediate neighbors, which limits the depth of information sampling. To overcome this, we introduce a novel second-order social learning (SOSL) rule that broadens the horizon of social information sampling. Specifically, SOSL employs a two-step information sampling process: instead of directly copying the strategy of the successful neighbor, individual adopts the strategy that their successful neighbor has identified as optimal through TSL rule. Using Monte Carlo simulations of the public goods game on a lattice-structured population, compared with the TSL rule, we find that the SOSL rule markedly lowers the conditions of the critical synergy factor for the emergence of cooperation and full cooperation. Even when the synergy factor is too low for cooperation to emerge under the TSL rule, the SOSL rule sustains cooperative clusters and accelerates the transition from defection to widespread cooperation. Furthermore, by introducing a strategy-preference coevolutionary framework that allows individuals to adapt their learning mode, we show that the population converges overwhelmingly to the SOSL rule under moderate synergy factors. Finally, the cooperative advantage of the SOSL rule holds across diverse network structures and persists even under noisy parameter conditions.
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来源期刊
Chaos Solitons & Fractals
Chaos Solitons & Fractals 物理-数学跨学科应用
CiteScore
13.20
自引率
10.30%
发文量
1087
审稿时长
9 months
期刊介绍: Chaos, Solitons & Fractals strives to establish itself as a premier journal in the interdisciplinary realm of Nonlinear Science, Non-equilibrium, and Complex Phenomena. It welcomes submissions covering a broad spectrum of topics within this field, including dynamics, non-equilibrium processes in physics, chemistry, and geophysics, complex matter and networks, mathematical models, computational biology, applications to quantum and mesoscopic phenomena, fluctuations and random processes, self-organization, and social phenomena.
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