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Weber-Fechner relation and Levy-like searching stemmed from ambiguous experiences

机译:Weber-Fechner关系和类似Levy的搜索源于模棱两可的经历

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Here, we show that an optimized Levy-like walk (mu approximate to 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, i.e., 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. (C) 2015 Elsevier B.V. All rights reserved.
机译:在这里,我们表明,在我们新的基于多主体的,取决于步长的模型中,可以实现优化的类似于Levy的步行(μ大约为2.00)和Weber Fechner定律。 Weber Fechner方程与幂律密切相关。有时使用该方程式以获得观测水平的幂律尾部分布。但是,没有研究报道这两个流行的方程是如何在微观或机械水平上实现的。我们提出了一种基于重值算法的新的随机游走算法,其中代理具有有限的存储容量,即代理仅具有四个最近随机数(限制数)的存储器。使用这些随机数,如果代理经历移动的方向偏差,则代理会更改方向启发式。在本文中,初始限制数取决于代理之间的相互作用。因此,代理改变他们的限制数量并产生关于规则改变事件的时间延迟。我们表明,与孤立觅食相比,坡度值是可变的,即使它们都表明从Weber Fechner方程导出的幂律尾部走动。 (C)2015 Elsevier B.V.保留所有权利。

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