标题： 世界中的社会学习 显示英文标题

标题： Social Learning in a Changing World

摘要： 我们研究了一个在动态环境中学习的社会网络模型，描述了一组代理，他们各自试图根据获得的弱信号和社交网络邻居的估计来估计一个随时间变化的底层状态。 我们研究了三种代理行为模型。 在“固定响应”模型中，代理使用固定的线性组合将同伴的信息纳入自己的估计中。 这可以看作是DeGroot模型在动态环境中的扩展。 在“最佳响应”模型中，玩家计算底层状态的最小方差线性估计器。 我们证明，无论初始配置如何，固定响应动力学都会收敛到稳态，并且对于完全图上的最佳响应也是如此。 我们证明，从长远来看，最佳响应动力学可能导致比使用精心选择的固定响应所能实现的方差更高的估计器。 “最终预测”模型是对最佳响应模型的进一步阐述。 虽然该模型仅略微增加了代理所需的计算量，但我们证明，在某些情况下，它大大提高了学习的效率，并且在完全图上实际上在强意义上是最优的。 显示英文摘要

摘要： We study a model of learning on social networks in dynamic environments, describing a group of agents who are each trying to estimate an underlying state that varies over time, given access to weak signals and the estimates of their social network neighbors. We study three models of agent behavior. In the "fixed response" model, agents use a fixed linear combination to incorporate information from their peers into their own estimate. This can be thought of as an extension of the DeGroot model to a dynamic setting. In the "best response" model, players calculate minimum variance linear estimators of the underlying state. We show that regardless of the initial configuration, fixed response dynamics converge to a steady state, and that the same holds for best response on the complete graph. We show that best response dynamics can, in the long term, lead to estimators with higher variance than is achievable using well chosen fixed responses. The "penultimate prediction" model is an elaboration of the best response model. While this model only slightly complicates the computations required of the agents, we show that in some cases it greatly increases the efficiency of learning, and on complete graphs is in fact optimal, in a strong sense.