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

TL;DR

This paper introduces the concept of self-confirming prices in simultaneous auctions, providing a framework for equilibrium analysis and practical bidding strategies that improve decision-making under uncertainty.

Contribution

It formalizes self-confirming prices within a Bayesian game framework and offers methods to compute near-optimal bids and predictions, advancing auction strategy research.

Findings

Self-confirming strategies are effective in various auction settings.

Equilibrium characterized by self-confirming prices improves bidding decisions.

Practical algorithms for computing predictions and bids are developed.

Abstract

Bidding in simultaneous auctions is challenging because an agent's value for a good in one auction may depend on the uncertain outcome of other auctions: the so-called exposure problem. Given the gap in understanding of general simultaneous auction games, previous works have tackled this problem with heuristic strategies that employ probabilistic price predictions. We define 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 predictions. We exhibit practical procedures to compute approximately optimal bids given a probabilistic price prediction, and near self-confirming price predictions given a price-prediction strategy. An extensive empirical game-theoretic analysis demonstrates that self-confirming price prediction strategies are effective in simultaneous auction games with both complementary and substitutable preference structures.