Network Science Institute, Northeastern University, Boston, MA, USA.
Department of Philosophy, Tufts University, Medford, MA, USA.
J R Soc Interface. 2018 Mar;15(140). doi: 10.1098/rsif.2017.0835.
An important way to resolve games of conflict (snowdrift, hawk-dove, chicken) involves adopting a convention: a correlated equilibrium that avoids any conflict between aggressive strategies. Dynamic networks allow individuals to resolve conflict via their network connections rather than changing their strategy. Exploring how behavioural strategies coevolve with social networks reveals new dynamics that can help explain the origins and robustness of conventions. Here, we model the emergence of conventions as correlated equilibria in dynamic networks. Our results show that networks have the tendency to break the symmetry between the two conventional solutions in a strongly biased way. Rather than the correlated equilibrium associated with ownership norms (play aggressive at home, not away), we usually see the opposite host-guest norm (play aggressive away, not at home) evolve on dynamic networks, a phenomenon common to human interaction. We also show that learning to avoid conflict can produce realistic network structures in a way different than preferential attachment models.
解决冲突博弈（雪堆、鹰鸽、斗鸡）的一个重要方法涉及采用一种约定：避免任何攻击性策略之间冲突的关联均衡。动态网络允许个人通过他们的网络连接来解决冲突，而不是改变他们的策略。探索行为策略如何与社交网络共同进化，揭示了新的动态，可以帮助解释约定的起源和稳健性。在这里，我们将规范的出现建模为动态网络中的关联均衡。我们的结果表明，网络具有强烈偏向地打破两个规范解决方案之间对称性的趋势。我们通常看到的不是与所有权规范相关的关联均衡（在主场激进，不在客场），而是在动态网络上进化的相反的主客规范（客场激进，不在主场），这是人类互动中的常见现象。我们还表明，通过避免冲突来学习可以以不同于优先连接模型的方式产生现实的网络结构。
