Adaptive-Price Combinatorial Auctions

This work introduces a novel iterative combinatorial auction that aims to achieve both high efficiency and fast convergence across a wide range of valuation domains. We design the first fully adaptive-price combinatorial auction that gradually extends price expressivity as the rounds progress. We implement our auction design using polynomial prices and show how to detect when the current price structure is insufficient to clear the market, and how to expand the polynomial structure to guarantee progress. An experimental evaluation confirms that our auction is competitive with bundle-price auctions in regimes where these excel, namely multi-minded valuations, but also performs well in regimes favorable to linear prices, such as valuations with pairwise synergy.