论文标题
从规则到Regs：勾结研究的结构性主题模型
From Rules to Regs: A Structural Topic Model of Collusion Research
论文作者
论文摘要
公司的合法实践仍然是对竞争和消费者福利的主要威胁。关于该主题的学术研究旨在了解卡特尔等人的经济驱动力和行为模式，以指导竞争当局如何解决它们。在自然语言处理领域中利用局部机器学习技术使我能够以新颖的方式分析有关该问题的出版物。研究人员来自风格化的寡头游戏理论的重点，最近转向了过去卡特尔的经验案例研究。 UNI-和多元时间序列分析表明，后者并未取代前者，但填补了基于规则的推理的差异。加上涵盖主题的单一培养的趋势以及对主题品种的内源性收缩的趋势，卡特尔研究的过程发生了显着变化：包括各种受试者的种类已经增长，但是解决经济问题的多元化是下降的。将来，这是否会受益还是损害卡特尔检测能力。
Collusive practices of firms continue to be a major threat to competition and consumer welfare. Academic research on this topic aims at understanding the economic drivers and behavioral patterns of cartels, among others, to guide competition authorities on how to tackle them. Utilizing topical machine learning techniques in the domain of natural language processing enables me to analyze the publications on this issue over more than 20 years in a novel way. Coming from a stylized oligopoly-game theory focus, researchers recently turned toward empirical case studies of bygone cartels. Uni- and multivariate time series analyses reveal that the latter did not supersede the former but filled a gap the decline in rule-based reasoning has left. Together with a tendency towards monocultures in topics covered and an endogenous constriction of the topic variety, the course of cartel research has changed notably: The variety of subjects included has grown, but the pluralism in economic questions addressed is in descent. It remains to be seen whether this will benefit or harm the cartel detection capabilities of authorities in the future.
