Rationalizing collective revealed preferences with an application in fair resource allocation
This work addresses the problem of rationalizing collective behavior for fair resource allocation, offering a practical method with theoretical guarantees, though it is an incremental extension of revealed preference theory.
The paper introduces the Constructive Rationalization Method (CRM) to model collective consumption behavior from aggregate data, providing generalization guarantees and privacy preservation. It applies CRM to approximate proportionally fair resource allocations without individual utility information.
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.