On the Theoretical Foundations of Data Exchange Economies
Bhaskar Ray Chaudhury
Assistant Professor, Industrial & Enterprise Systems Engineering
University of Illinois at Urbana-Champaign
Abstract: The immense success of ML systems relies heavily on large-scale high-quality data. The high demand for data has led to several paradigms that involve selling, exchanging, and sharing data. This naturally motivates studying economic processes that involve data as an asset. However, data differs from classical economic assets in terms of (i) free duplication i.e., there is no concept of limited supply with data as it can be replicated at zero marginal cost, and (ii) ex-ante unverifiable, i.e., it is difficult to estimate the utility of the data to an agent apriori, without using it. These distinctions cause fundamental differences between economic processes involving data and those involving other assets.
We investigate the parallel of exchange markets (Arrow-Debreu markets) in settings where data is the asset, i.e., where agents in possession of datasets exchange data fairly and voluntarily for mutual benefit without any monetary compensation. This is relevant in settings involving non-profit organizations that are seeking to improve their ML models through data-exchange with other organizations and are not allowed to sell their data for profit. This work proposes a general framework for data-exchange from first principles. We investigate the existence and computation of a data-exchange satisfying the foregoing principles.
Biography: Bhaskar Ray Chaudhury is an Assistant Professor of Operations Research in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign, with a simultaneous affiliation in the Department of Computer Science. His research interests lie at the intersection of economics and computation, focusing on areas such as equilibrium computation, computational social choice, and the integration of economic principles into machine learning frameworks. His contributions to the field have been recognized with awards, including the Best Paper Award for a student-led submission, the Exemplary Theory Paper Award at the ACM Conference on Economics and Computation (EC), and a spotlight presentation at Advances for Neural Information Processing Systems (NeurIPS).