A Joint Auction Framework with Externalities and Adaptation

2025-12-16

AI Summary

Existing joint advertising mechanisms struggle to integrate with conventional ad frameworks, overlook global externalities, and fail to accommodate volatile bidding behaviors from multiple advertisers—leading to suboptimal auction efficiency. This paper proposes JEANet, the first neural auction framework that incorporates global externalities into automated mechanism design (AMD), enabling dynamic adaptation to heterogeneous bidding patterns while unifying joint and traditional advertising auctions. JEANet jointly optimizes individual rationality (IR) and approximate dominant-strategy incentive compatibility (DSIC) constraints. Extensive experiments on multi-slot joint auctions demonstrate that JEANet significantly improves platform revenue and resource allocation efficiency, outperforming state-of-the-art baselines.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Advertising auctions, pricing, markets, and exchanges
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Recently, joint advertising has gained significant attention as an effective approach to enhancing the efficiency and revenue of advertising slot allocation. Unlike traditional advertising, which allocates advertising slots exclusively to a single advertiser, joint advertising displays advertisements from brands and stores that have established a joint selling relationship within the same advertising slot. However, existing approaches often struggle to accommodate both joint and traditional advertising frameworks, thereby limiting the revenue potential and generalizability of joint advertising. Furthermore, these methods are constrained by two critical limitations: they generally neglect the influence of global externalities, and they fail to address the bidding variability stemming from multi-party advertiser participation. Collectively, these limitations present substantial challenges to the design of joint auction mechanisms. To address these challenges, we propose a Joint Auction Framework incorporating Externalities and Adaptation, and leverage the automated mechanism design (AMD) method through our proposed JEANet to compute joint auction mechanisms that satisfy the conditions of individual rationality (IR) and approximate dominant strategy incentive compatibility (DSIC). As the first AMD method to integrate global externalities into joint auctions, JEANet dynamically adapts to the bidding characteristics of multi-party advertiser and enables unified auctions that integrate both joint and traditional advertising. Extensive experimental results demonstrate that JEANet outperforms state-of-the-art baselines in multi-slot joint auctions.

Problem

• Design joint auction mechanisms for advertising with externalities
• Address bidding variability from multi-party advertiser participation
• Unify joint and traditional advertising in auction frameworks

Innovation

• Integrates global externalities into joint auction design
• Adapts dynamically to multi-party advertiser bidding characteristics
• Unifies joint and traditional advertising in single auction