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 free duplication i.e., there is no concept of limited supply with data as it can be replicated at zero marginal cost, and 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., a setting where agents in possession of datasets exchange data amongst each other 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 own data for profit. In this work, we propose a general framework for data-exchange from first principles. We investigate the existence and computation of a data-exchange satisfying the foregoing principles.