Information Aggregation in Decentralized Markets

经济学家长期以来一直认为，市场在信息聚合方面发挥着关键作用;个人所知道的会影响他们将支付和收取的价格。 这一过程的运作方式因市场而异。 例如，通过分析拍卖市场，经济学家已经能够为信息聚合建立一个战略基础。然而，在许多市场中，交易不是通过集中拍卖进行的。相反，价格通常是通过少数代理人之间的本地互动来确定的，例如，在分散的市场中，如劳动力和场外证券市场。 该奖项资助经济理论研究，旨在了解分散的市场如何聚集分散在市场参与者中的信息。分散的市场是用搜索框架来建模的。成对的交易者讨价还价，如果他们不能达成协议，就寻找其他交易者。分析表明，分散市场中的信息聚合从根本上不同于拍卖中的信息聚合。PI正在构建和分析这一过程的两个不同模型。第一个模型是一个单一买家的分散设置。买方寻求获得服务、修理或治疗，并联系卖方以获得报价。服务费用取决于买方的个人特点，如维修的复杂性或健康状况。联系的卖家数量是内生的，取决于买家的私人信息。此外，被联系会向卖家透露信息。卖方还通过检查或诊断接收关于买方特征的噪声信息。虽然每个单独的卖方的信息是不完善的，但所有卖方共同拥有足够的信息来确定买方的特征。这些分散的信息是否以及如何被聚集起来，这是要研究的问题。初步结果表明，分散市场中的信息聚合比标准拍卖中的信息聚合要困难得多（从某种意义上说，后来才变得精确）。第二个模型分析了分散式拍卖环境下的信息流，其中有许多买卖双方交易同一种商品。对商品的总需求（稀缺性）是未知的。买家和卖家通过与其他交易者的互动来了解真实的市场状况。与第一个分项目一样，分析的重点是分散信息的传输和汇总。如果将信息汇总，价格反映了商品的真实稀缺性，也就是说，价格反映了商品的真实经济价值。更广泛的影响包括研究生培训和更好地了解就业市场等重要市场的关键特征。

Economists have long argued that markets play a key role in aggregating information; what individuals know affects the prices they will pay and charge. How this process works varies from market to market. For example, by analyzing auction markets economists have been able to develop a strategic foundation for information aggregation. In many markets, however, trading is not conducted through centralized auctions. Instead, prices are often determined through local interactions between a small number of agents, for example, in decentralized markets such as those for labor and over-the-counter securities. This award funds research in economic theory with a goal of understanding how decentralized markets aggregate information that is dispersed among market participants. Decentralized markets are modeled with a search framework. Pairs of traders bargain over prices and search for other traders if they do not reach an agreement. The analysis shows that information aggregation in decentralized markets differs fundamentally from information aggregation in auctions. The PI is building and analyzing two different models of this process.In the first, there is a decentralized setting with a single buyer. The buyer seeks to procure a service, repair or cure, and contacts sellers for price offers. The cost of the service depends on private characteristics of the buyer, such as the complexity of the repair or the health status. The number of sellers contacted is endogenous and depends on the private information of the buyer. Moreover, being contacted reveals information to the seller. Sellers also receive noisy information about the buyer's characteristics through inspection or diagnosis. While each individual seller's information is imperfect, all sellers jointly posses sufficient information to determine the buyer's characteristics. Whether and how this dispersed information is aggregated is the question examined. Preliminary results show that information aggregation in the decentralized market is much harder (in a sense made precise later) than in standard auctions. This continues to be true even when the standard auction setting is extended to permit the buyer to solicit bidders.The second model analyzes the flow of information in decentralized settings with many buyers and sellers who trade a single type of good. Aggregate demand for the good (scarcity) is unknown. Buyers and sellers learn individually about the true market condition by interacting with other traders. As in the first sub-project, the focus of the analysis is on the transmission and aggregation of dispersed information. If information is aggregated, the price reflects the true scarcity of the good, that is, the price reflects the good's true economic value.The broader impacts include graduate student training and a better understanding of key features of important markets like the job market.