Search Theory in Economics

该项目扩展了搜索理论的前沿，以阐明三个经济问题。第一个问题是，我们应该如何预期大学的拒绝率会因口径而异，并询问它们是否是优秀的信息衡量标准。初步研究表明，拒绝率可能是最好的大学的反向质量指标。第二个应用程序探讨了最优清算的经济学，例如20世纪90年代的重组信托公司或2008年雷曼兄弟破产之前的情况。早期的研究揭示了最优销售策略如何在较低利率下进行得更慢，从而将艾伦·格林斯潘（Alan Greenspan）的观点正式化。第三个开发了一个网络搜索经济学的理论分析，如发生在像亚马逊这样的网站;在早期的研究结果是，卖方不需要找到它最有利可图的，使最大限度地提供信息的搜索引擎。该项目追求两个搜索理论的动态规划项目，和一个有向搜索模型。第一个项目将拍卖理论的见解融入到一个定向搜索问题中，该问题的动机是期刊提交和大学申请过程。随着期刊或学院排名的上升，其标准也会提高，申请者也会有所改善。但拒绝率一定会上升并不明显，事实上，在信息完整的情况下，早期结果表明拒绝率不会上升。这两个动态规划练习探索了两个固有的非平稳搜索模型。第一个问题是，当面对定期到来的愿意以特定价格购买部分资产的买家时，如何以最佳方式解除资产中的大量头寸。在许多关键结果中，最重要的是“递减的期权收益”，即价值函数在位置上是凹的，但边际价值是凸的。第二个动态规划练习探索如何在许多排名选项中进行最佳搜索。这出现在网络搜索中，这个模型足够容易处理，可以产生许多新的可用的比较统计。

This project extends the frontier of search theory to shed light on three economic problems. The first asks how we should expect rejection rates at colleges to vary with caliber, and inquires whether they are informative measures of excellence. Preliminary research suggests that rejection rates might be reverse quality measures at the very best colleges. The second application explores the economics of optimal liquidation, such as transpired with the Resolution Trust Corporation in the 1990s or prior to the famous bankruptcy of Lehman-Brothers in 2008. Early work reveals, among other things, how an optimal selling strategy proceeds far more slowly at lower interest rates, formalizing an insight publicized by Alan Greenspan. The third develops a theoretical analysis of the economics of web search, such as occurs in web sites like Amazon; among early findings are that the seller need not find it most profitable to make a maximally informative search engine. The project pursues two dynamic programming projects in search theory, and one directed search model. The first project incorporates insights from auction theory into a directed search problem that is motivated by the journal submission and college application processes. As the rank of the journal or college rises, its standards rise and its applicants improve. But it is not obvious that rejection rates must rise and in fact, with complete information, early results suggest that they do not. The two dynamic programming exercises explore two inherently nonstationary search models. The first is how does one optimally unwind a large position in an asset when facing periodic arrivals of buyers willing to buy portion at a specified price. Among many key results, most important is the "decreasing returns to optionality" that the value function is concave in the position - but the marginal value is convex. The second dynamic programming exercise explores how one optimally searches among many ranked options. This arises in web search, and this model is tractable enough to produce many novel usable comparative statics.