Bidding in Uniform Price Auctions for Value Maximizing Buyers

Negin Golrezaei and Sourav Sahoo

July 2024

We study the problem of bidding in uniform price auctions, a widely used format in Treasury auctions, emissions permit auctions, and energy markets. Although uniform price auctions are non-truthful for bidders with quasilinear utility functions, several empirical findings suggest that this auction format induces truthful bidding from the bidders. We attribute this difference in theory and practice to the assumption of the behavioral model of the bidders. In this pursuit, we study uniform price auctions in a repeated setting from the perspective of a single value-maximizing buyer who aims to maximize their acquired cumulative value across T rounds, subject to per-round return-on-investment (RoI) constraints. For a RoI-constrained and value-maximizing buyer, we study a generalized version of the uniform bidding format, commonly used in practice, which we term as m-uniform bidding. Under m-uniform bidding, the buyer submits m pairs of bid and quantity values (b_i, q_i), demanding q_i units at bid b_i. To characterize the optimal m-uniform bid, we introduce and study the notion of universally feasible (UF) bidding policies, which are robust, meaning that RoI feasibility is obtained regardless of the competitors’ bids. We show that the optimal class of UF bidding policies is essentially a generalization of truthful bidding policies, which depends only on the valuation curve of the bidder and target RoI, irrespective of the bids submitted by competitors. To measure the performance of UF bidding policies against the optimal bidding policy that is not necessarily UF, we introduce a metric called the Price of Universal Feasibility (PoUF) and establish that PoUF is at most 2, irrespective of m, and show that the upper bound is tight. We further compare the generalized m-uniform bidding interface against the classical uniform bidding format under which m = 1, showing the total value under m-uniform bidding increases at most by a factor of m. Numerical simulations on semi-synthetic data demonstrate that UF bidding policies perform significantly better than the derived theoretical bounds, and this, combined with their straightforward characterization, makes them highly appealing in practice.