Dynamic analysis of markets

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Abstract/Contents

Abstract
We study learning in a dynamic setting where identical copies of a good are sold over time through a sequence of second price auctions. Each agent in the market has an unknown independent private valuation which determines the distribution of the reward she obtains from the good; for example, in sponsored search settings, advertisers may initially be unsure of the value of a click. Though the induced dynamic game is complex, we simplify analysis of the market using an approximation methodology known as mean field equilibrium (MFE). The methodology assumes that agents optimize only with respect to long run average estimates of the distribution of other players' bids. We show a remarkable fact: in a mean field equilibrium, the agent has an optimal strategy where she bids truthfully according to a conjoint valuation. The conjoint valuation is the sum of her current expected valuation, together with an overbid amount that is exactly the expected marginal benefit to one additional observation about her true private valuation. Under mild conditions on the model, we show that an MFE exists, and that it is a good approximation to a rational agent's behavior as the number of agents increases. Formally, if every agent except one follows the MFE strategy, then the remaining agent's loss on playing the MFE strategy converges to zero as the number of agents in the market increases. We conclude by discussing the implications of the auction format and design on the auctioneer's revenue. In particular, we establish a dynamic version of the revenue equivalence theorem, and discuss optimal selection of reserve prices in dynamic auctions.

Description

Type of resource text
Form electronic; electronic resource; remote
Extent 1 online resource.
Publication date 2012
Issuance monographic
Language English

Creators/Contributors

Associated with Iyer, Krishnamurthy Rengarajan
Associated with Stanford University, Department of Management Science and Engineering
Primary advisor Johari, Ramesh, 1976-
Thesis advisor Johari, Ramesh, 1976-
Thesis advisor Glynn, Peter W
Thesis advisor Van Roy, Benjamin
Advisor Glynn, Peter W
Advisor Van Roy, Benjamin

Subjects

Genre Theses

Bibliographic information

Statement of responsibility Krishnamurthy Iyer.
Note Submitted to the Department of Management Science and Engineering.
Thesis Thesis (Ph.D.)--Stanford University, 2012.
Location electronic resource

Access conditions

Copyright
© 2012 by Krishnamurthy Rengarajan Iyer

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