Evolutionary Analysis of the Regulation of Data Abuse in Digital Platforms

Abstract

This study proposes a tripartite evolutionary game model to investigate the interactions among digital platforms, governments, and users to address the negative consequences of data abuse. The paper identifies that the high tax incentives and low penalties set by the government will increase the incentive for data abuse by platforms of different sizes, and the government can try to set up a tax ladder policy for platforms of different sizes and a dynamic penalty amount based on platform revenue. The study also reveals that user participation in supervision can reduce information asymmetry, and decrease the cost of government regulation. However, the single constraint of users is less effective than government regulation or dual user-government regulation. Additionally, the presence of privacy leakage risks prompts digital platforms to adopt compound engines to implement data abuse. Hence, the relevant government regulatory policies should consider the efficiency and cost of data security technology for timely adjustments. This research contributes to understanding the complex relationships among digital platforms, governments, and users and highlights the need for appropriate measures to mitigate the negative effects of data abuse.