  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.
