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Seminar by Krishnamurthy Iyer

Title: Mean Field Equilibria of Dynamic Auctions with Learning
Speaker: Krishnamurthy Iyer, Stanford University

Time and date: 3:30 p.m., Wednesday, April 6 2011
Venue: Room 211, Mechanical Engineering Building


Abstract: Auctions are observed as a market mechanism in a wide range of economic transactions with repeated interactions among the market participants: sponsored search markets run by Google and Yahoo!, online market places by Amazon and eBay, etc. With rise in their popularity, the need to model and analyse them in a dynamic setting has grown. We consider a market model where identical copies of a good are sold 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 the analysis using an approximation methodology known as mean field equilibrium (MFE), where 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 mi ld 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. Joint work with Ramesh Johari and Mukund Sundararajan

 

Speaker's Bio: Krishnamurthy Iyer is a Doctoral student at Management Science and Engineering Department at Stanford University. His academic interests include Game Theory, Learning in Games, Mean Field models and Market Micro-Structure. He holds an M.Tech in CIM and a B.Tech in Mechanical Engineering, both from IIT Bombay.

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