Self-Confirming Price Prediction Strategies for Simultaneous One-Shot Auctions

Bidding in simultaneous auctions is challenging because an agent’s value for a good in one auction may depend on the outcome of other auctions; that is, bidders face an exposure problem. Given the gap in our understanding (e.g., lack of game-theoretic solutions) of general simultaneous auction games, previous works have tackled the problem of how to bid in these games with heuristic strategies that employ probabilistic price predictions—so-called price-prediction strategies. We introduce a concept of self-confirming prices, and show that within an independent private value model, Bayes-Nash equilibrium can be fully characterized as a profile of optimal price-prediction strategies with self-confirming prices. We exhibit practical procedures to compute near-self-confirming price predictions given a priceprediction strategy, and near-optimal bids given a probabilistic price prediction. We call the output of our procedures self-confirming price-prediction (SCPP) strategies, and produce one such strategy that outperforms all previously studied bidding heuristics for this setting. An extensive empirical gametheoretic analysis demonstrates that SCPP strategies are effective in simultaneous auctions with both complementary and substitutable preference structures.