Simulation Analysis of Big Data Discriminatory Pricing Behavior from the Perspective of Game Theory - Nan Wang; Yu Chang; Xiao Yu Song (SAGE Open)
With the increasing popularity of big data technology, some enterprises use the massive amount of consumer data collected for the analysis of consumers’ purchasing power and preferences to implement discriminatory pricing and increase profits, with consumer rights infringed upon. Therefore, it is of great significance to study how consumers respond to big data discriminatory pricing (BDDP) behavior by enterprises. This article categorizes consumers into new and old users. In the strategy sets of whether consumers choose to purchase and whether enterprises engage in discriminatory pricing, the costs and benefits of consumer rights protection and enterprise compensation are considered, respectively. A new “government-consumer-enterprise” tripartite game model is proposed, along with an analysis of different behavioral strategy combinations of the three parties. The impact of key parameters on each party is studied through simulation analysis to provide a reference for cracking down on BDDP behavior. The experimental results indicate that increasing government punishment and credibility can effectively suppress the BDDP behavior by enterprises; however, increasing the compensation limit for enterprises will only have a certain effect in the early stage; the higher the evaluation value of products or services by consumers, the less effectiveness it is in suppressing the BDDP behavior by enterprises.