论文标题
进化网络动力学中突变率的马尔可夫转换
Markovian Switching of Mutation Rates in Evolutionary Network Dynamics
论文作者
论文摘要
复制器 - 突击器动态最初是为了建模语言的演变而得出的，并且由于该模型以如此一般的方式得出，因此已将其应用于多代理网络中的社会行为和决策动力学。对于两种类型的种群，得出突变率的分叉点，在此点以上和下方显示出不同的长期行为。长期行为自然会受到环境噪声的影响，但是迄今为止，并不存在动态解释环境影响的模型。为了说明对民众演变的环境影响，根据连续的马尔可夫链会切换该分叉点以上和下方的突变率。该模型的长期行为得出，表明大多数初始条件都会有利于主导类型的违反直觉结果。
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.
