标题： 学习、多样性与适应性在变化环境中的作用：弱连接的作用 显示英文标题

标题： Learning, Diversity and Adaptation in Changing Environments: The Role of Weak Links

摘要： 适应动态条件需要一定水平的多样性。 如果所有代理都采取当前最佳行动，学习到基础状态已发生变化并应调整行为将更慢。 当代理之间通信迅速时，保持多样性更加困难，因为它们倾向于快速发现并追求最佳行动。 我们通过社会网络上的（贝叶斯）学习模型来探讨这些问题。 代理会从彼此那里快速学习，并可能有激励与通过强链接连接的其他人进行协调。 然而，我们证明，当基础环境变化足够快时，仅由强链接组成的任何网络在长期表现上只会比随机选择略好一些。 相反，结合强链接和弱链接的网络，其中后者的链接仅缓慢传递信息，可以实现更高的长期平均收益。 最好的社会网络是那些将大量代理组合成一个强连通组件，同时仍保持足够数量的小型社区，这些社区做出多样化选择，并通过弱链接与该组件进行交流。 显示英文摘要

摘要： Adaptation to dynamic conditions requires a certain degree of diversity. If all agents take the best current action, learning that the underlying state has changed and behavior should adapt will be slower. Diversity is harder to maintain when there is fast communication between agents, because they tend to find out and pursue the best action rapidly. We explore these issues using a model of (Bayesian) learning over a social network. Agents learn rapidly from and may also have incentives to coordinate with others to whom they are connected via strong links. We show, however, that when the underlying environment changes sufficiently rapidly, any network consisting of just strong links will do only a little better than random choice in the long run. In contrast, networks combining strong and weak links, whereby the latter type of links transmit information only slowly, can achieve much higher long-run average payoffs. The best social networks are those that combine a large fraction of agents into a strongly-connected component, while still maintaining a sufficient number of smaller communities that make diverse choices and communicate with this component via weak links.