Search Theory in Economics

这个项目扩展了搜索理论的前沿，揭示了三个经济问题。第一个问题是，我们应该如何预期大学的拒绝率会随着口径的不同而变化，并询问它们是否是衡量优秀程度的信息性指标。初步研究表明，在最好的大学里，拒绝率可能是与质量相反的指标。第二个应用探讨了最优清算的经济学，例如在20世纪90年代与决议信托公司发生的事情，或者在2008年著名的雷曼兄弟破产之前。早期的工作揭示了，在利率较低的情况下，最优抛售策略的执行速度要慢得多，从而正式确立了艾伦·格林斯潘(Alan Greenspan)公布的一个见解。第三部分对网络搜索的经济学进行了理论分析，比如在亚马逊这样的网站上；早期的发现之一是，卖家不必发现制造一个信息量最大的搜索引擎是最有利可图的。该项目追求搜索理论中的两个动态规划项目，以及一个定向搜索模型。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。随着期刊或大学排名的提高，它的标准也会提高，申请者也会有所提高。但拒绝率一定会上升并不明显，事实上，根据完整的信息，初步结果表明并非如此。这两个动态编程练习探索了两个固有的非静态搜索模型。第一个问题是，面对愿意以特定价格购买部分资产的买家的定期到来，人们如何以最佳方式解除一项资产的大量头寸。在许多关键结果中，最重要的是“递减收益到可选性”，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的选项中进行最佳搜索。这是在网络搜索中出现的，这个模型足够容易处理，可以产生许多新的可用的比较静态。

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.