全球智库信息集成服务系统(G2TT): Retrospective Search: Exploration and Ambition on Uncharted Terrain

G2TT

来源类型	Working Paper
规范类型	报告
DOI	10.3386/w29127
来源ID	Working 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
URL	https://www.nber.org/papers/w29127
来源智库	National Bureau of Economic Research (United States)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/586801
推荐引用方式 GB/T 7714	Can Urgun,Leeat Yariv. Retrospective Search: Exploration and Ambition on Uncharted Terrain. 2021.

条目包含的文件
文件名称/大小	资源类型	版本类型	开放类型	使用许可
w29127.pdf（752KB）	智库出版物		限制开放	CC BY-NC-SA	浏览