An Exploration of the Challenges of Cross-border Data Flow for International Investment Law by Counting and Fuzzy Numerical Analysis Algorithms

Abstract

Abstract In the context of globalization, cross-border data circulation has brought many challenges to international investment law. Based on fuzzy theory, this paper constructs the affiliation function of cross-border data flow for the fuzzy set of international investment law based on fuzzy theory statistical experiment strategy, then establishes the fuzzy numerical analysis method, and uses the entropy weight method to strengthen the connection between the weights and the initial data for the problem that the index weights are not objective enough. The entropy-weight-based fuzzy numerical analysis method will then study the privacy risk, management risk, and strategic risk brought by cross-border data growth to international economic cooperation. From 2010 to 2019, the privacy risk of cross-border flowing data increased by 32.16% on average, the management risk increased by 37.84% on average, and the strategic risk increased by 167.31% on average. Although none of these 3 risk assessments have yet exceeded 50%, they still challenge major economies to balance the regulation of cross-border data security and promote capital cooperation and circulation. Faced with the challenges brought by cross-border data flow, countries should start by improving international investment agreements and enhancing international consensus, take advantage of the advantages of large data resource countries, and promote the establishment of new rules for cross-border data flow.