Numerical Analysis of Asymmetric First Price Auctions
We develop a powerful and user-friendly program for numerically solving first price auction problems where an arbitrary number of bidders draw independent valuations from heterogenous distributions and the auctioneer imposes a reserve price for the object. The heterogeneity in this model arises both from the specification of ex-ante heterogenous, non-uniform distributions of private values for bidders, as well as the possibility of subsets of these bidders colluding. The technique extends the work of Marshall, Meurer, Richard, and Stromquist (1994), where they applied backward recursive Taylor series expansion techniques to solve two-player asymmetric first price auctions under uniform distributions. The algorithm is also used to numerically investigate whether revenue equivalence between first price and second price auctions in symmetric models extend to the asymmetric case. In particular, we simulate the model under various environments and find evidence that under the assumption of first order stochastic dominance, the first price auction generates higher expected revenue to the seller, while the second price auction is more susceptible to collusive activities. However, when the assumption of first order stochastic dominance is relaxed, and the distributions of private values cross once, the evidence suggests that the second price auction may in some cases generate higher expected revenue to the seller
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