WRIGHT-FISHER DIFFUSIONS IN STOCHASTIC SPATIAL EVOLUTIONARY GAMES WITH DEATH-BIRTH UPDATING

• 成果类型: Article
• 署名作者: Chen, Yu-Ting
• 署名单位: University of Tennessee System; University of Tennessee Knoxville
• 刊物名称: ANNALS OF APPLIED PROBABILITY
• ISSN/ISSBN: 1050-5164
• DOI: 10.1214/18-AAP1390
• 发表日期: 2018
• 页码: 3418-3490
• 关键词: voter models; COOPERATION; population

摘要

We investigate stochastic spatial evolutionary games with death-birth updating in large finite populations. Within growing spatial structures subject to appropriate conditions, the density processes of a fixed type are proven to converge to the one-dimensional Wright-Fisher diffusions. Convergence in the Wasserstein distance of the laws of the occupation measures also holds. The proofs study the convergences under certain voter models by an equivalence between their laws and the laws of the evolutionary games. In particular, the additional growing dimensions in minimal systems that close the dynamics of the game density processes are cut off in the limit. As another application of this equivalence of laws, we consider a first-derivative test among the major methods for these evolutionary games in a large population of size N. Requiring only the assumption that the stationary probabilities of the corresponding voting kernel are comparable to uniform probabilities, we prove that the test is applicable at least up to weak selection strengths in the usual biological sense [i.e., selection strengths of the order O(1/N)].