Evolutionary Game Analysis of Data Resale Governance in Data Trading

Abstract

Data trading is important for optimizing the allocation of data elements. However, data can be easily copied, disseminated, or resold, leading to disorderly development in the data trading market, and raising the issue of data governance. Data trading involves various participants, while existing research lacks an understanding of participant interactions and strategy adoption, as well as determination of optimal strategies for the participants. To address these gaps and provide insights for the governance of data trading platforms, this paper proposes an evolutionary game model for the governance of data trading involving three parties: data suppliers, demanders, and trading platforms. Our findings reveal that data trading platforms choosing to govern, data suppliers choosing to innovate positively, and data demanders choosing not to resell can be achieved under certain conditions. We also find that an increase in the price of data trading or the number of transactions can weaken the effectiveness of platform governance and make data trading more difficult to govern. Additionally, the incentives for data innovation provided by the trading platform can significantly promote data suppliers to innovate data positively. However, when these incentives are too high, the platform may weaken its level of governance or even move towards non-governance. Increasing penalties for data resale weakens data demanders’ motivation to resell data, and a higher probability of data resale being reported lowers their motivation to do so. By examining the role of different participants in data trading, the model proposes ways to improve the efficiency and robustness of the data market while better protecting the interests of data traders.