Searching and Learning by Trial and Error
Searching and Learning by Trial and Error
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通过反复试验进行搜索和学习
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Steven Callander
中科院分区：
文献类型：
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作者：
Steven Callander
我研究了一个试错搜索的动态模型，在这个模型中，代理人并不完全知道选择是如何映射成结果的。智能体通过观察早期智能体的选择和实现的结果来学习映射。关键的新奇之处在于，映射被表示为布朗运动的实现路径。我在这个环境中描述了每个时期的最佳行为，以及实验和学习的轨迹。该模型应用于新产品开发，与众所周知的产品生命周期数据具有相同的特征。（凝胶d81， d83, d92, 126）
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. (JEL D81, D83, D92, L26)
雅科夫列夫扭矩：旧的和新的。
影响因子：
10.6
作者：
Bilder,RobertM
通讯作者：
Bilder,RobertM