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Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement Learning

Greg d'Eon, Neil Newman, Kevin Leyton-Brown

TL;DR

This work proposes a MARL-based methodology to study iterative combinatorial auctions, balancing tractable modelling with essential features like imperfect information and bidder asymmetry. It demonstrates the approach on clock auctions, using MCCFR and PPO to find near-equilibria and evaluating two drop-based bid-processing rules. The results show that bid-processing design can yield qualitatively different outcomes, especially with multi-type bidders, and that naive myopic heuristics can mislead conclusions. The paper also provides a configurable clock-auction environment and discusses practical design questions, scalability challenges, and theoretical questions for future work.

Abstract

Iterative combinatorial auctions are widely used in high stakes settings such as spectrum auctions. Such auctions can be hard to analyze, making it difficult for bidders to determine how to behave and for designers to optimize auction rules to ensure desirable outcomes such as high revenue or welfare. In this paper, we investigate whether multi-agent reinforcement learning (MARL) algorithms can be used to understand iterative combinatorial auctions, given that these algorithms have recently shown empirical success in several other domains. We find that MARL can indeed benefit auction analysis, but that deploying it effectively is nontrivial. We begin by describing modelling decisions that keep the resulting game tractable without sacrificing important features such as imperfect information or asymmetry between bidders. We also discuss how to navigate pitfalls of various MARL algorithms, how to overcome challenges in verifying convergence, and how to generate and interpret multiple equilibria. We illustrate the promise of our resulting approach by using it to evaluate a specific rule change to a clock auction, finding substantially different auction outcomes due to complex changes in bidders' behavior.

Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement Learning

TL;DR

This work proposes a MARL-based methodology to study iterative combinatorial auctions, balancing tractable modelling with essential features like imperfect information and bidder asymmetry. It demonstrates the approach on clock auctions, using MCCFR and PPO to find near-equilibria and evaluating two drop-based bid-processing rules. The results show that bid-processing design can yield qualitatively different outcomes, especially with multi-type bidders, and that naive myopic heuristics can mislead conclusions. The paper also provides a configurable clock-auction environment and discusses practical design questions, scalability challenges, and theoretical questions for future work.

Abstract

Iterative combinatorial auctions are widely used in high stakes settings such as spectrum auctions. Such auctions can be hard to analyze, making it difficult for bidders to determine how to behave and for designers to optimize auction rules to ensure desirable outcomes such as high revenue or welfare. In this paper, we investigate whether multi-agent reinforcement learning (MARL) algorithms can be used to understand iterative combinatorial auctions, given that these algorithms have recently shown empirical success in several other domains. We find that MARL can indeed benefit auction analysis, but that deploying it effectively is nontrivial. We begin by describing modelling decisions that keep the resulting game tractable without sacrificing important features such as imperfect information or asymmetry between bidders. We also discuss how to navigate pitfalls of various MARL algorithms, how to overcome challenges in verifying convergence, and how to generate and interpret multiple equilibria. We illustrate the promise of our resulting approach by using it to evaluate a specific rule change to a clock auction, finding substantially different auction outcomes due to complex changes in bidders' behavior.
Paper Structure (54 sections, 11 figures, 2 tables)

This paper contains 54 sections, 11 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology: Understanding Iterative Combinatorial Auctions via Multi-Agent Reinforcement Learning
  3. Modelling an Auction
  4. Number of actions.
  5. Auction length.
  6. Number of bidders.
  7. Perfect or imperfect information.
  8. Asymmetry between bidders.
  9. Finding Equilibria
  10. Choosing an algorithm.
  11. Multiple equilibria.
  12. Pure or mixed equilibria.
  13. Avoiding brittle equilibria.
  14. Validating and Interpreting Policies
  15. Assessing convergence.
  16. ...and 39 more sections

Figures (11)

  • Figure 1: The AuctionNet neural network architecture.
  • Figure 2: NashConv of MCCFR ablations, varying secondary rewards and trembling opponents.
  • Figure 3: Auction outcomes using MCCFR and PPO on 2-player games with 1 to 7 types.
  • Figure 4: Auction outcomes under straightforward bidding on 2-player games with 7 types.
  • Figure 5: (a) NashConv runtime distribution; (b) auction outcomes, using MCCFR on 3-player games.
  • ...and 6 more figures