Abstract: 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 typically face an exposure problem. Given the gap in 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, bidding optimally with respect to self-confirming price predictions is w.l.o.g. in equilibrium. In other words, Bayes-Nash equilibrium can be fully characterized as a profile of optimal price-prediction strategies with self-confirming predictions. We exhibit practical procedures to compute approximately optimal bids given a probabilistic price predicti! on, and near self-confirming price predictions given a price-prediction strategy. We call the output of our procedures self-confirming price-prediction (SCPP) strategies. An extensive empirical game-theoretic analysis demonstrates that SCPP strategies are effective in simultaneous auction games with both complementary and substitutable preference structures.
Impact: This work (a collaboration between Michigan and Brown) addresses a fundamental issue in automated trading: how to deal with multiple related markets at once. Our solution is justified by game-theoretic analysis, yet is computationally practical given a model of the bidding environment.
Conference: Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, August 15-17, Catalina Island, USA.
Paper URL: http://web.eecs.umich.edu/srg/?page_id=1346
Submitted by Michael Wellman, Professor of Electrical Engineering and Computer Science. firstname.lastname@example.org