大数据杀熟是否可以避免——基于演化博弈和仿真的分析
Can Big Data-enabled Price Discrimination Against Existing Customers Be Avoided?—Analysis Based on Evolutionary Game and Simulation
万岩 1曾宏 1史书扬 1刘恒宇1
作者信息
- 1. 北京邮电大学 经济管理学院,北京 100876
- 折叠
摘要
以节约公共资源和支持市场自由发展为出发点,建立平台与用户之间的演化博弈模型,探讨用户在可以自由切换平台的情境下,不同均衡点的形成条件及其稳定性.根据理论推导的条件,使用Octave进行数值仿真,以分析影响平台算法杀熟定价的因素,并确定不使用杀熟定价的理想状态所需满足的条件.同时,研究旨在探索除监管外,哪些措施可以帮助实现这一理想状态.研究结果表明,避免大数据杀熟并不一定需要依赖实时监控或罚款等手段,以市场化手段发展多个平台和鼓励平台竞争可能是最直接且可行的方法.
Abstract
An evolutionary game model between platforms and users is constructed from the perspective of conserving public resources and supporting market development.The model explores formation conditions and stability of different equilibrium points under the scenario where users have the freedom to switch platforms.Based on theoretical deduction,numerical simulations using Octave are conducted to analyze the factors influencing platform's algorithmic discrimination pricing and determine the conditions required for an ideal state without such pricing.Additionally,this study aims to explore alternative measures,aside from regulation,that can help achieve this ideal state.The results indicate that avoiding big data-enabled price discrimination against existing customers does not necessarily rely on means such as real-time monitoring or fines;fostering multiple platforms and promoting platform competition may be a more direct and feasible approach.
关键词
大数据杀熟/算法定价/价格歧视/演化博弈/仿真Key words
big data-enabled price discrimination against existing customers/algorithmic pricing/price discrimination/evolutionary game/simulation引用本文复制引用
基金项目
国家自然科学基金面上项目(72374031)
出版年
2024
NETL
NSTL
万方数据