Weber-Fechner relation and Lévy-like searching stemmed from ambiguous experiences
Weber-Fechner relation and Lévy-like searching stemmed from ambiguous experiences
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韦伯-费希纳关系和 L
DOI：
10.1016/j.physa.2015.06.038
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发表时间：
2015
期刊：
影响因子：
3.3
通讯作者：
Yukio-Pegio Gunji
中科院分区：
文献类型：
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作者：
Tomoko Sakiyama;Yukio-Pegio Gunji
在这里，我们证明了在我们新的基于多智能体的模型中可以实现优化的Lévy样行走（μ m 2.00）和Weber-Fechner定律，该模型取决于步长。Weber-Fechner方程与幂律密切相关。这个方程有时被用来获得观测水平的幂律尾分布。然而，没有研究报道这两个流行的方程是如何实现的微观或机械水平。我们提出了一个新的随机游走算法的基础上的重估算法，其中一个代理有有限的记忆容量，即，一个代理有一个记忆的最近的四个随机数（限制数）。使用这些随机数，如果代理经历移动的方向偏差，则代理改变方向启发式。在本文中，初始限制数的变化取决于代理之间的相互作用。因此，代理人改变他们的限制数量，并产生相对于规则改变事件的时间延迟。我们发现，斜率值是可变的，与孤立的觅食相比，即使都表示幂律尾走韦伯-费希纳方程。
Here, we show that an optimized Lévy-like walk (μ≈ 2.00) and the Weber–Fechner law can be achieved in our new multi-agent based model that depends on step lengths. Weber–Fechner equation is strongly related to power-law. This equation is sometimes used in order to obtain power-law tailed distributions in observational levels. However, no study has reported how these two popular equations were achieved in micro or mechanistic levels. We propose a new random walk algorithm based on a re-valued algorithm, in which an agent has limited memory capacity, ie, an agent has a memory of only four recent random numbers (limitation number). Using these random numbers, the agent alters the directional heuristic if the agent experiences moving directional biases. In this paper, the initial limitation number varies depending on the interaction among agents. Thus, agents change their limitation number and produce time delay in respect to rule change events. We show that slope values are variable compared with isolate foraging even though both indicate power-law tailed walks derived from Weber–Fechner equation.