Prices, Bids, Values: One ML-Powered Combinatorial Auction to Rule Them All

2024-10-08 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Market Design & Auction Theory, Economic Analysis & Policy · Depth: Expert, extended

Summary

MLHCA, a novel Machine Learning-powered Hybrid Combinatorial Auction, significantly improves efficiency and convergence speed in iterative combinatorial auctions (ICAs) by integrating both demand queries (DQs) and value queries (VQs). The system addresses the exponential growth of bundle space in CAs by using ML-based preference elicitation. Unlike previous state-of-the-art (SOTA) algorithms that rely solely on VQs or DQs, MLHCA combines them, starting with 40 DQs and transitioning to 60 VQs. This hybrid approach reduces efficiency loss by up to a factor of 10 compared to the previous SOTA, BOCA, and outperforms ML-CCA using 30% fewer queries in the most challenging domain (MRVM). MLHCA's efficiency gains translate to welfare improvements exceeding $50 million USD in a single auction instance, while also reducing bidder cognitive load.

Key takeaway

For auction designers and machine learning engineers developing iterative combinatorial auctions, MLHCA demonstrates that a hybrid query strategy significantly outperforms single-query approaches. You should prioritize an initial phase of demand queries for broad preference discovery, followed by targeted value queries for precise value elicitation. Incorporating a "bridge bid" VQ is crucial to prevent efficiency degradation when switching query types, ensuring robust performance and substantial welfare gains in real-world applications.

Key insights

Combining demand and value queries in iterative combinatorial auctions significantly boosts efficiency and learning performance.

Principles

• DQs are more effective for initial preference discovery.
• VQs provide precise, local information for refinement.
• A "bridge bid" VQ prevents efficiency drops when transitioning from DQs to VQs.

Method

MLHCA employs a two-stage training algorithm: first, an mMVNN is trained on DQ responses using a utility-maximizing loss, then on VQ responses with a standard regression loss, provably integrating full information from both query types.

In practice

• Start auctions with DQs for broad preference exploration.
• Transition to targeted VQs for fine-grained value elicitation.
• Implement a "bridge bid" VQ to maintain efficiency during query type transitions.

Topics

• Iterative Combinatorial Auctions
• Machine Learning-powered Auctions
• Demand Queries
• Value Queries
• MLHCA Mechanism Design

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

Related on AIssential

• Constrained user-item allocation for e-commerce marketing campaigns — Constrained spectral biclustering effectively auto-targets e-commerce campaigns by jointly optimizing user-item allocation for quality and fairness.
• Constrained Auto-Bidding via Generative Response Modeling — GRM improves auto-bidding by predicting future responses, enabling precise constraint enforcement and better performance in dynamic auctions.
• HMACE: Heterogeneous Multi-Agent Collaborative Evolution for Combinatorial Optimization — Heterogeneous multi-agent collaboration with specialized roles and memory-guided reflection enhances heuristic discovery for COPs.
• MINTS: Minimalist Thompson Sampling — MINTS simplifies Bayesian decision-making by focusing priors and profiling nuisance parameters to handle structural constraints effectively.
• Batched Single-Index Global Multi-Armed Bandits with Covariates — BIDS is a novel batched multi-armed bandit algorithm that uses single-index regression to efficiently manage covariates and related arm rewards.
• A New Lower Bound for the Random Offerer Mechanism in Bilateral Trade using AI-Guided Evolutionary Search — AI-guided search found a new lower bound for the Random-Offerer mechanism's worst-case efficiency.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.