一场人生游戏走向了一个临界点

IF 0.7 4区 数学 Q4 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Advances in Complex Systems Pub Date : 2023-06-15 DOI:10.25088/complexsystems.32.1.57
Tomoko Sakiyama
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引用次数: 0

摘要

生命游戏(GoL)产生了复杂的生命模式,已经被用来描述生物系统的自组织临界性和无标度特性。本文提出了两个新的GoL模型。一个模型允许每个单元在使用参数调优与其他本地单元交互后更新状态更新的时间。因此,单个细胞将它们的行为从间歇状态更新替换为连续状态更新。如果选择适当的参数,系统会不可预测地接近一个临界点,有时会接近存活种群的灭绝。此事件以幂律尾时间间隔发生,并呈现同步行为,因为单个单元修改其状态更新间隔并创建时间连续性。另一个模型是相同的,除了系统在没有任何参数调整的情况下不可预测地发展。在第二个模型中,单个细胞的行为不是由一个固定的参数来调节,而是由周围的情况来调节。我们发现第二个模型中的GoL系统与第一个模型中的行为方式相似,这表明该模型自主地向临界点转移。
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A Game of Life Shifted toward a Critical Point
The Game of Life (GoL), which produces complex patterns of life, has been employed to describe biological systems through self-organized criticality and scale-free properties. This paper develops two novel GoL models. One model allows each cell to update the time for the state update following interactions with other local cells using parameter tuning. Thus, individual cells replace their behaviors from intermittent state updates with continuous ones. The system evolves unpredictably close to a critical point and occasionally close to extinction for the alive population if an adequate parameter is chosen. This event occurs with a power-law tailed time interval and presents synchronous behaviors, since individual cells modify their state-update intervals and create time continuity. The other model is the same except that the system evolves unpredictably without any parameter tuning. In the second model, actions of individual cells are tuned not by a fixed parameter but by the surrounding situation. We found that the GoL system in the second model behaved in a similar manner in the first model, which suggests that that model shifts toward a critical point autonomously.
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来源期刊
Advances in Complex Systems
Advances in Complex Systems 综合性期刊-数学跨学科应用
CiteScore
1.40
自引率
0.00%
发文量
121
审稿时长
6-12 weeks
期刊介绍: Advances in Complex Systems aims to provide a unique medium of communication for multidisciplinary approaches, either empirical or theoretical, to the study of complex systems. The latter are seen as systems comprised of multiple interacting components, or agents. Nonlinear feedback processes, stochastic influences, specific conditions for the supply of energy, matter, or information may lead to the emergence of new system qualities on the macroscopic scale that cannot be reduced to the dynamics of the agents. Quantitative approaches to the dynamics of complex systems have to consider a broad range of concepts, from analytical tools, statistical methods and computer simulations to distributed problem solving, learning and adaptation. This is an interdisciplinary enterprise.
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