arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.00900math.PRq-bio.PE
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进化网络动力学中变异率的马尔可夫转换

Markovian Switching of Mutation Rates in Evolutionary Network Dynamics

Andrew Vlasic

摘要:

复制-变异动力学最初是为了模拟语言的进化而产生的,由于该模型是以这样一种普遍的方式产生的,因此它已被应用于多智能体网络中的社会行为和决策的动力学。对于两种类型的种群,得到了突变率的一个歧点,在该分歧点之上和之下表现出不同的长期行为。长期运行的行为自然会受到来自环境的噪声的影响,然而,迄今为止,还没有一个模型,动态地考虑环境的影响。为了解释环境对种群进化的影响,根据连续时间马尔可夫链切换高于和低于该歧点的突变率。该模型的长期行为的推导,显示出一个违反直觉的结果,大多数的初始条件将有利于支配类型。

英文摘要:

The replicator-mutator dynamic was originally derived to model the evolution of language, and since the model was derived in such a general manner, it has been applied to the dynamics of social behavior and decision making in multi-agent networks. For the two type population, a bifurcation point of the mutation rate is derived, displaying different long-run behaviors above and below this point. The long-run behavior would naturally be subjected to noise from the environment, however, to date there does not exist a model that dynamically accounts for the effects of the environment. To account for the environmental impacts on the evolution of the populace, mutation rates above and below this bifurcation point are switched according to a continuous-time Markov chain. The long-run behaviors of this model are derived, showing a counterintuitive result that the majority of initial conditions will favor the dominated type.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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