摘要
在数字经济领域,数字商务企业采用算法定价会明显提高合谋的可能性和可实施性,具有较大的价格合谋风险,因而成为反垄断法关注的重点。学理上,尚待明确的问题有:算法定价促进合谋的内在机理和类型化机制;如何创新反垄断执法体制以有效规制自主学习算法;在反垄断事后执法无效情况下,是否需要以及如何实行事前规制等。研究表明:算法合谋的反垄断规制宜坚持分类治理原则,采取事后反垄断禁止为主并辅之以事前规制的政策组合,反垄断政策工具创新应主要针对自主学习算法合谋。算法合谋反垄断规制政策需重新界定构成非法合谋的"协议"要件,明确当事企业的主体责任,重在采取以"软执法"为主的反垄断执法体制。事前规制政策应坚持"基于设计来遵守法律"的原则,强化算法审查机制和审查能力建设,并将提升算法透明度和可问责性作为重点。
Algorithmic pricing adopted by digital business companies will significantly increase the possibility and enforceability of collusion and has a greater risk of price collusion, which becoming the focus of antitrust law in the digital economy. Theoretically, the questions that need to be clear are the internal mechanism and mechanisms of different types of algorithmic pricing to promote collusion;how to innovate antitrust law tools to effectively regulate self-learning algorithms;whether and how to implement the ex-ante regulation because of the invalidity of ex-post antitrust enforcement. This research shows that antitrust regulation of algorithmic collusion should adhere to the principle of classified governance, adopt a policy combination of ex-post antitrust prohibition and supplemented by ex-ante regulation, and the innovation of antitrust policy tools aiming at self-learning algorithm collusion. The focus of the algorithmic collusion antitrust regulation policy is to redefine the requirements of illegal "agreement" that constitute the illegal collusion, clarify the responsibilities of the entity, and adopting an antitrust enforcement system based on "soft law enforcement".
作者
唐要家
尹钰锋
TANG Yao-jia;YIN Yu-feng
出处
《产经评论》
CSSCI
北大核心
2020年第2期5-16,共12页
Industrial Economic Review
基金
国家社会科学基金重点项目“数字经济政府监管再定位及监管体系创新研究”(项目编号:19AJY004,项目负责人:唐要家)。
关键词
算法合谋
自主学习算法
反垄断
事前规制
政策工具创新
algorithmic collusion
self-learning algorithm
antitrust
ex-ante regulation
innovation of policy tools
