Alexandre Champagne-Ruel, Sascha Zakaib-Bernier, Paul Charbonneau
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引用次数: 0
Abstract
Diffusion plays an important role in a wide variety of phenomena, from
bacterial quorum sensing to the dynamics of traffic flow. While it generally
tends to level out gradients and inhomogeneities, diffusion has nonetheless
been shown to promote pattern formation in certain classes of systems.
Formation of stable structures often serves as a key factor in promoting the
emergence and persistence of cooperative behavior in otherwise competitive
environments, however an in-depth analysis on the impact of diffusion on such
systems is lacking. We therefore investigate the effects of diffusion on
cooperative behavior using a cellular automaton (CA) model of the noisy spatial
iterated prisoner's dilemma (IPD), physical extension and stochasticity being
unavoidable characteristics of several natural phenomena. We further derive a
mean-field (MF) model that captures the 3-species predation dynamics from the
CA model and highlight how pattern formation arises in this new model, then
characterize how including diffusion by interchange similarly enables the
emergence of large scale structures in the CA model as well. We investigate how
these emerging patterns favors cooperative behavior for parameter space regions
where IPD error rates classically forbid such dynamics. We thus demonstrate how
the coupling of diffusion with non-linear dynamics can, counter-intuitively,
promote large scale structure formation and in return establish new grounds for
cooperation to take hold in stochastic spatial systems.
从细菌的定量感应到交通流的动力学,扩散在各种现象中都扮演着重要角色。虽然扩散通常会消除梯度和不均匀性,但在某些类别的系统中,扩散仍被证明能促进模式的形成。稳定结构的形成通常是促进竞争性环境中合作行为的出现和持续的关键因素,但目前还缺乏关于扩散对此类系统影响的深入分析。因此,我们利用噪声空间囚徒困境(IPD)的细胞自动机(CA)模型来研究扩散对合作行为的影响,物理扩展和随机性是一些自然现象不可避免的特征。我们进一步推导出一个平均场(MF)模型,该模型捕捉到了 CA 模型中的 3 种捕食动态,并强调了在这个新模型中模式是如何形成的,然后描述了在 CA 模型中,通过交换扩散也能产生大规模结构。我们研究了这些新出现的模式如何有利于参数空间区域的合作行为,而这些区域的 IPD 误差率通常是禁止这种动态的。因此,我们证明了扩散与非线性动力学的耦合是如何反直觉地促进大尺度结构的形成,并反过来为随机空间系统中的合作建立新的基础。