On the Theoretical Foundations of Data Exchange Economies

Hannaneh Akrami, Bhaskar Ray Chaudhury, Jugal Garg, Aniket Murhekar

Abstract: Organizations increasingly seek to share and access datasets to improve their ML models and derive insights. Despite the immense demand for quality data, data exchange and collaboration have not reached their full potential. One of the key reasons is the lack of reciprocity, where some participants perceive their contribution to others to be of higher value than what they receive in return.We propose a general framework for data exchange without payments, focusing on two main properties: (i) reciprocal fairness, which ensures that each agent receives utility proportional to their contribution to the total welfare - contributions are quantifiable using well-established credit-sharing functions such as the Shapley share and proportional share, and (ii) core-stability, which guarantees that no coalition of agents can identify an exchange among themselves which they all unanimously prefer to the current exchange.Surprisingly, we show that fair and stable exchanges exist for all monotone continuous utility functions. Building on this, we investigate the computational complexity of finding approximate fair and stable exchanges. We present a local search algorithm for instances with monotone submodular utility functions and cross-monotone credit-sharing functions, e.g., Shapley share. We prove that this problem belongs to CLS (= PPAD ∩ PLS) under mild assumptions. Our framework opens up several intriguing theoretical avenues for future research on data economics.The full version of our paper can be accessed at https://arxiv.org/abs/2412.01968.

External IDs:dblp:conf/sigecom/AkramiCGM25