Evolutionary games revisited in terms of individual interactions

我重温了经典的进化博弈，如鹰鸽博弈，囚徒困境博弈，个体互动方面的合作进化。本文用算法描述了位于连续空间而非格点上的个体之间的机械交互，并利用并行计算实现了模拟这些个体出生、死亡和运动的算法。每种策略的种群动态（鹰派、鸽派、背叛、合作、达特等）通过单线态密度（个体数）和成对密度（由两个个体移位一定距离形成的成对数）分析。

I revisited classical evolutionary games such as Hawk-dove games, Prisoner's dilemma game, evolution of cooperation in terms of individual interactions. Mechanistic interactions among individuals located on a continuous space, not lattice, are algorithmically described and an efficient way to simulate these algorithm of birth, death, and movement, of individuals is implemented by using parallel computing. Population dynamics of each strategy (hawk, dove, defect, cooperate, tit-for-tat, etc.) was analyzed by the singlet density (number of individuals) and the pair density (number of pairs made by two individuals displaced by a certain distance).