Searching and Learning by Trial and Error
研究了一个试错搜索的动态模型，其中代理人通过观察前人选择与结果来学习选择与结果之间的映射关系，并将该映射表示为布朗运动的实现路径，刻画了每期最优行为及实验与学习的轨迹，应用于新产品开发时与产品生命周期数据特征吻合。
I study a dynamic model of trial-and-error search in which agents do not have complete knowledge of how choices are mapped into outcomes. Agents learn about the mapping by observing the choices of earlier agents and the outcomes that are realized. The key novelty is that the mapping is represented as the realized path of a Brownian motion. I characterize for this environment the optimal behavior each period as well as the trajectory of experimentation and learning through time. Applied to new product development, the model shares features of the data with the well-known Product Life Cycle.