Information Aggregation in Decentralized Markets

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

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.