[Paper Review] AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games

Kefan Su, Yusen Huo+6 more|arXiv (Cornell University)|Dec 14, 2024

Auction Theory and ApplicationsDecision Sciences3 citations

TL;DR

AuctionNet is a novel benchmark for decision-making in large-scale ad auctions, built from a real-world online advertising platform. It features a simulated environment with deep generative modeling for ad opportunities, customizable GSP-based auctions, and 48 auto-bidding agents, enabling rigorous evaluation of algorithms like reinforcement learning and linear programming on a 500M-record dataset, with validation via the NeurIPS 2024 competition.

ABSTRACT

Decision-making in large-scale games is an essential research area in artificial intelligence (AI) with significant real-world impact. However, the limited access to realistic large-scale game environments has hindered research progress in this area. In this paper, we present AuctionNet, a benchmark for bid decision-making in large-scale ad auctions derived from a real-world online advertising platform. AuctionNet is composed of three parts: an ad auction environment, a pre-generated dataset based on the environment, and performance evaluations of several baseline bid decision-making algorithms. More specifically, the environment effectively replicates the integrity and complexity of real-world ad auctions through the interaction of several modules: the ad opportunity generation module employs deep generative networks to bridge the gap between simulated and real-world data while mitigating the risk of sensitive data exposure; the bidding module implements diverse auto-bidding agents trained with different decision-making algorithms; and the auction module is anchored in the classic Generalized Second Price (GSP) auction but also allows for customization of auction mechanisms as needed. To facilitate research and provide insights into the environment, we have also pre-generated a substantial dataset based on the environment. The dataset contains 10 million ad opportunities, 48 diverse auto-bidding agents, and over 500 million auction records. Performance evaluations of baseline algorithms such as linear programming, reinforcement learning, and generative models for bid decision-making are also presented as a part of AuctionNet. We believe that AuctionNet is applicable not only to research on bid decision-making in ad auctions but also to the general area of decision-making in large-scale games.

Motivation & Objective

• To address the lack of realistic, accessible large-scale game environments for AI research in ad auctions.
• To develop a scalable, privacy-preserving benchmark that mirrors real-world ad auction dynamics.
• To enable systematic evaluation of bid decision-making algorithms under complex, multi-agent, time-varying conditions.
• To support research in reinforcement learning, mechanism design, and operational research through a standardized, reproducible testbed.
• To provide a foundation for future work in strategic decision-making under uncertainty in large-scale systems.

Proposed method

• The ad opportunity generation module uses deep generative networks to synthesize realistic, privacy-preserving data that bridge simulated and real-world distributions.
• The bidding module supports 48 diverse auto-bidding agents trained with different algorithms, including reinforcement learning, linear programming, and generative models.
• The auction module is grounded in the Generalized Second Price (GSP) mechanism but allows for customizable auction rules to support varied research scenarios.
• Temporal patterns in ad values are modeled using position encoding inspired by Transformers and Diffusion Models, enhancing sequence modeling in time-series bidding decisions.
• A value prediction model is trained using a U-Net-like architecture with multi-head attention to forecast ad opportunity values based on contextual features.
• The benchmark includes a pre-generated dataset of 10 million ad opportunities, 48 agents, and over 500 million auction records for reproducible evaluation.

Experimental results

Research questions

• RQ1 How well do standard bid decision-making algorithms such as linear programming and reinforcement learning generalize in a large-scale, realistic ad auction environment?
• RQ2 To what extent can deep generative models effectively simulate real-world ad auction dynamics while preserving data privacy?
• RQ3 How do different auto-bidding strategies perform under varying constraints such as ROI targets and budget limits in a competitive, multi-agent setting?
• RQ4 Can the benchmark support scalable, reproducible evaluation of decision-making algorithms across diverse auction mechanisms?
• RQ5 What insights can be gained from analyzing 500 million auction records in a simulated yet realistic environment?

Key findings

• AuctionNet successfully powered the NeurIPS 2024 Auto-Bidding in Large-Scale Auctions competition, providing a standardized environment to over 1,500 participating teams.
• The benchmark enables high-fidelity simulation of real-world ad auction dynamics, including temporal patterns and multi-agent competition, through deep generative modeling of ad opportunities.
• Performance evaluations show that reinforcement learning and generative modeling approaches achieve competitive results, with clear trade-offs in stability and adaptability across different ROI targets.
• The pre-generated dataset of over 500 million auction records enables robust, reproducible evaluation of bid decision-making algorithms across diverse scenarios.
• The integration of position encoding and multi-head attention mechanisms improves value prediction accuracy, supporting better strategic bidding decisions.
• The benchmark demonstrates strong potential for broader application in reinforcement learning, mechanism design, and operational research beyond ad auctions.