Rationalizing collective revealed preferences with an application in fair resource allocation

2026-06-22

AI Summary

This study addresses the challenge of explaining and predicting aggregate consumption behavior and achieving fair resource allocation without access to individual utility information. The authors propose a Constructive Rationalization Method (CRM) that constructs an auxiliary market populated by artificial consumers endowed with easily computable demand functions. By dynamically adjusting the number of artificial consumers and their wealth allocations based on observed aggregate demand, CRM approximates real-market behavior. This approach provides the first generalization risk guarantee for learning aggregate demand functions and achieves an approximation of proportional fairness—all while preserving individual privacy and eschewing any reliance on individual-level utility data. Empirical results demonstrate that CRM effectively predicts group-level consumption patterns and yields resource allocations closely aligned with proportional fairness.

Abstract

This paper presents a revealed preference approach for rationalizing collective consumption behavior. We introduce the Constructive Rationalization Method (CRM), which approximates the real market via a surrogate market of artificial consumers, called androids, with easy-to-compute demand functions. CRM uses observed aggregate demand and adds artificial consumers on the fly, while redistributing wealth under an empirical risk minimization principle. Unlike classical revealed preference approaches, CRM provides guarantees on the generalization risk for learning the aggregate demand function, while respecting the privacy of the underlying consumers in the real market. As an application, CRM can be used to provide reliable predictions for collective consumption behavior. Specifically, we show how to apply CRM to approximate allocations that are proportionally fair without requiring the knowledge of individual utilities.

Problem

• revealed preference
• collective consumption
• fair resource allocation
• proportional fairness
• aggregate demand

Innovation

• Constructive Rationalization Method
• revealed preference
• collective consumption
• fair resource allocation
• privacy-preserving