Grupo Interdisciplinar de Sistemas Complejos (GISC), Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés, Madrid, Spain.
PLoS One. 2012;7(4):e35135. doi: 10.1371/journal.pone.0035135. Epub 2012 Apr 18.
By applying a technique previously developed to study ecosystem assembly [Capitán et al., Phys. Rev. Lett. 103, 168101 (2009)] we study the evolutionary stable strategies of iterated 2 × 2 games. We focus on memory-one strategies, whose probability to play a given action depends on the actions of both players in the previous time step. We find the asymptotically stable populations resulting from all possible invasions of any known stable population. The results of this invasion process are interpreted as transitions between different populations that occur with a certain probability. Thus the whole process can be described as a Markov chain whose states are the different stable populations. With this approach we are able to study the whole space of symmetric 2 × 2 games, characterizing the most probable results of evolution for the different classes of games. Our analysis includes quasi-stationary mixed equilibria that are relevant as very long-lived metastable states and is compared to the predictions of a fixation probability analysis. We confirm earlier results on the success of the Pavlov strategy in a wide range of parameters for the iterated Prisoner's Dilemma, but find that as the temptation to defect grows there are many other possible successful strategies. Other regions of the diagram reflect the equilibria structure of the underlying one-shot game, albeit often some non-expected strategies arise as well. We thus provide a thorough analysis of iterated 2 × 2 games from which we are able to extract some general conclusions. Our most relevant finding is that a great deal of the payoff parameter range can still be understood by focusing on win-stay, lose-shift strategies, and that very ambitious ones, aspiring to obtaining always a high payoff, are never evolutionary stable.
应用之前开发的技术来研究生态系统组装[Capitán 等人,Phys. Rev. Lett. 103, 168101 (2009)],我们研究了迭代 2×2 博弈的进化稳定策略。我们专注于记忆一策略,其采取给定行动的概率取决于前一步两个玩家的行动。我们发现所有可能入侵任何已知稳定群体的渐近稳定群体。这种入侵过程的结果可以解释为不同群体之间以一定概率发生的过渡。因此,整个过程可以描述为一个马尔可夫链,其状态是不同的稳定群体。通过这种方法,我们能够研究对称 2×2 博弈的整个空间,为不同类别的博弈刻画进化的最可能结果。我们的分析包括准静态混合平衡,它们作为非常长寿命的亚稳态是相关的,并且与固定概率分析的预测进行了比较。我们确认了在迭代囚徒困境中,帕夫洛夫策略在广泛参数范围内成功的早期结果,但发现随着背叛的诱惑增加,还有许多其他可能成功的策略。图表的其他区域反映了基础单次博弈的均衡结构,尽管经常也会出现一些非预期的策略。因此,我们对迭代 2×2 博弈进行了全面分析,从中我们能够得出一些一般性结论。我们最相关的发现是,通过关注赢留、输移策略,可以理解大量的收益参数范围,而那些雄心勃勃、总是期望获得高收益的策略永远不会是进化稳定的。