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We study dynamic mechanisms for optimizing revenue in repeated auctions, thatare robust to heterogeneous forward-looking and learning behavior of thebuyers. Typically it is assumed that the buyers are either all myopic or areall infinite lookahead, and that buyers understand and trust the mechanism.These assumptions raise the following question: is it possible to designapproximately revenue optimal mechanisms when the buyer pool is heterogeneous?Facing a heterogeneous population of buyers with an unknown mixture of$k$-lookahead buyers, myopic buyers, no-regret-learners and no-policy-regretlearners, we design a simple state-based mechanism that achieves a constantfraction of the optimal achievable revenue.

Dynamic First Price Auctions Robust to Heterogeneous Buyers