From Rules to Regs: A Structural Topic Model of Collusion Research
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Time SeriesTime Series AnalysisSimilar Papers 제목 키워드 기반
Hybrid Collision Avoidance for ASVs Compliant with COLREGs Rules 8 and 13-17
This paper presents a three-layered hybrid collision avoidance (COLAV) system for autonomous surface vehicles, compliant with rules 8 and 13-17 of the International Regulations for Preventing Collisions at Sea (COLREGs).…
Collision AvoidanceModel Predictive ControlVORRT-COLREGs: A Hybrid Velocity Obstacles and RRT Based COLREGs-Compliant Path Planner for Autonomous Surface Vessels
This paper presents VORRT-COLREGs, a hybrid technique that combines velocity obstacles (VO) and rapidly-exploring random trees (RRT) to generate safe trajectories for autonomous surface vessels (ASVs) while following nau…
validLearning to Mitigate AI Collusion on Economic Platforms
Algorithmic pricing on online e-commerce platforms raises the concern of tacit collusion, where reinforcement learning algorithms learn to set collusive prices in a decentralized manner and through nothing more than prof…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)The Branching-Course MPC Algorithm for Maritime Collision Avoidance
This article presents a new algorithm for short-term maritime collision avoidance (COLAV) named the branching-course MPC (BC-MPC) algorithm. The algorithm is designed to be robust with respect to noise on obstacle estima…
Collision AvoidanceCredibility-Aware Learning and Control for Safe USV Navigation under Perception Uncertainty
Safe navigation for Unmanned Surface Vehicles (USVs) under the International Regulations for Preventing Collisions at Sea (COLREGs) remains challenging in dynamic maritime environments, especially when perception uncerta…
Reinforcement LearningCollision Avoidance