  Our paper concerns the computation of Nash equilibria of first-price auctions
with correlated values. While there exist several equilibrium computation
methods for auctions with independent values, the correlation of the bidders'
values introduces significant complications that render existing methods
unsatisfactory in practice. Our contribution is a step towards filling this
gap: inspired by the seminal fictitious play process of Brown and Robinson, we
present a learning heuristic-that we call fictitious bidding (FB)-for
estimating Bayes-Nash equilibria of first-price auctions with correlated
values, and we assess the performance of this heuristic on several relevant
examples.
