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来源类型Working Paper
规范类型报告
DOI10.3386/w29127
来源IDWorking Paper 29127
Retrospective Search: Exploration and Ambition on Uncharted Terrain
Can Urgun; Leeat Yariv
发表日期2021-08-09
出版年2021
语种英语
摘要We study a model of retrospective search in which an agent—a researcher, an online shopper, or a politician—tracks the value of a product. Discoveries beget discoveries and their observations are correlated over time, which we model using a Brownian motion. The agent, a standard exponential discounter, decides the breadth and length of search. We fully characterize the optimal search policy. The optimal search scope is U-shaped, with the agent searching most ambitiously when approaching a breakthrough or when nearing search termination. A drawdown stopping boundary is optimal, where the agent ceases search whenever current observations fall a constant amount below the maximal achieved alternative. We also show special features that emerge from contracting with a retrospective searcher.
主题Microeconomics ; Mathematical Tools ; Game Theory ; Households and Firms ; Economics of Information
URLhttps://www.nber.org/papers/w29127
来源智库National Bureau of Economic Research (United States)
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资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/586801
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GB/T 7714
Can Urgun,Leeat Yariv. Retrospective Search: Exploration and Ambition on Uncharted Terrain. 2021.
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