Robust procurement design

2025-12-08

AI Summary

This paper studies robust procurement mechanism design under dual uncertainty—both the item’s value to the buyer and the seller’s cost are unknown. Motivated by practitioners’ limited confidence in any single predictive model, we propose a two-stage framework: “minimax robust optimization + Bayesian expected optimization”—first identifying mechanisms that maximize worst-case revenue, then selecting among them the one with highest expected revenue. Theoretically, we uncover a counterintuitive insight: to deter inefficient suppliers from strategic overreporting of costs, it is optimal to *increase* procurement quantities from them moderately. Furthermore, we systematically compare quantity-based versus price-based regulation, rigorously characterizing sufficient conditions under which quantity regulation dominates in robustness. By unifying mechanism design, robust optimization, and Bayesian decision theory, our framework delivers provably optimal and implementable solutions for real-world applications such as public procurement and supply chain regulation under model uncertainty.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Reasoning under Uncertainty: Stochastic Optimization
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

We study procurement design when the buyer is uncertain about both the value of the good and the seller's cost. The buyer has a conjectured model but does not fully trust it. She first identifies mechanisms that maximize her worst-case payoff over a set of plausible models, and then selects one from this set that maximizes her expected payoff under the conjectured model. Robustness leads the buyer to increase procurement from the least efficient sellers and reduce it from those with intermediate costs. We also study monopoly regulation and identify conditions under which quantity regulation outperforms price regulation.

Problem

• Robust procurement design under buyer uncertainty
• Mechanisms maximizing worst-case and expected payoffs
• Comparing quantity and price regulation effectiveness

Innovation

• Robust mechanisms maximize worst-case payoff
• Buyer adjusts procurement based on seller efficiency
• Quantity regulation outperforms price regulation