Incentivizing Data Trading via Profit Reallocation

2026-06-30

AI Summary

This study addresses the low activity in data markets, which stems from upstream data sellers being unable to benefit from downstream resale, thereby dampening their willingness to supply. For the first time, this work incorporates the replicability and resale characteristics of data into a sequential trading model and proposes a profit redistribution mechanism that enables upstream participants to share in the revenues from subsequent resales. By modeling the setting as a sequential game, the authors design an exact polynomial-time algorithm for the discrete case and a fully polynomial-time approximation scheme (FPTAS) for the continuous case to efficiently compute sequential equilibria. Experimental results on synthetic data demonstrate that, compared to a baseline without redistribution, the proposed mechanism increases transaction volume by 120.0% and improves social welfare by 50.4%.

Abstract

Data trading is a central approach to data circulation, yet data markets remain far less active than expected. A primary bottleneck is the lack of effective economic incentives. Existing approaches often treat data as traditional goods, overlooking its inherent replicability and resale potential: buyers can replicate and resell data products, thereby forming transaction chains. Upstream sellers do not benefit from downstream resales and thus have limited incentives to sell. However, the impact of data resale on market performance remains insufficiently understood. To address this gap, we propose a sequential, chain-based data trading model that explicitly captures data resale. The model reflects data flows in settings such as LLM training and strategic decision-making. We integrate this model with a profit reallocation mechanism. By reallocating profits along the transaction chain, this mechanism ensures upstream sellers benefit from downstream resales. We next develop efficient algorithms, including a polynomial-time exact algorithm for the discrete model and an FPTAS for the continuous model, to compute its sequential equilibria. We theoretically show that profit reallocation expands trade and improves social welfare under certain conditions, and empirical results demonstrate that our mechanism increases transaction volume by 120.0\% and social welfare by 50.4\% in synthetic environments, compared with the baseline mechanism that does not reallocate profits.

Problem

• data trading
• economic incentives
• data resale
• transaction chains
• market performance

Innovation

• data trading
• profit reallocation
• transaction chain
• sequential equilibrium
• FPTAS