Surreal illustration of corporate buildings as chess pieces engaging in secret handshakes to symbolize collusion.

Collusion in the 21st Century: Are We Losing the Fight?

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Theo Raines in Business & Economy August 2025 • 4 min read.

"An in-depth look at how collusion research is evolving, and what it means for antitrust efforts."


Collusive practices by companies pose an ongoing threat to market competition and consumer well-being. Academic research plays a crucial role in understanding the drivers and patterns of cartels, and guides competition authorities in their efforts to combat them. Recent advancements in machine learning techniques applied to natural language processing now provide new ways to analyze publications and track trends in collusion research.

Historically, collusion research was grounded in stylized oligopoly-game theory. However, researchers have increasingly turned to empirical case studies of past cartels. This shift reflects a move towards analyzing real-world examples. Time series analysis further reveals how these empirical studies haven't replaced theoretical models, but rather filled a gap left by a decline in rule-based reasoning.

This article examines the evolution of collusion research over the past 20 years, revealing important changes and their potential impact on competition authorities. We'll explore topic modeling techniques, analyze research trends, and discuss whether the current direction of collusion research will ultimately strengthen or weaken the ability to detect and deter cartels.

The Shifting Sands of Collusion Research: From Rules to Real-World Cases

Surreal illustration of corporate buildings as chess pieces engaging in secret handshakes to symbolize collusion.

For decades, collusion research was heavily influenced by game theory and rule-based models. These models aimed to understand the fundamental incentives and strategic interactions that led firms to collude. They provided a framework for predicting cartel behavior and designing effective deterrents. However, in recent years, there's been a noticeable shift away from these abstract models towards more empirical, data-driven approaches.

This transition involves analyzing case studies of actual cartels, examining real-world market data, and using econometric techniques to identify patterns of collusion. Several factors may have contributed to this shift:

  • Data Availability: The increasing availability of detailed market data has made empirical research more feasible and attractive.
  • Computational Power: Advances in computing power have allowed researchers to analyze larger datasets and develop more sophisticated econometric models.
  • Leniency Programs: The success of leniency programs has led to a wealth of information about cartel operations, providing valuable insights for case studies.
  • Limitations of Traditional Models: Some argue that traditional game-theoretic models are too simplistic to capture the complexities of real-world collusion.
While empirical case studies provide valuable insights, it's essential to consider the potential downsides of a heavy reliance on them. These studies are inherently backward-looking, focusing on past cartels rather than predicting future ones. This may lead to a focus on specific industries or types of collusion, while neglecting emerging forms of anti-competitive behavior in new markets.

The Path Forward: Balancing Theory and Evidence in Collusion Research

The evolution of collusion research presents both opportunities and challenges for competition authorities. While empirical case studies offer valuable real-world insights, it's crucial to maintain a strong foundation in economic theory and rule-based models. A balanced approach, combining both theoretical frameworks and empirical analysis, is essential for developing effective strategies to detect and deter collusion in the 21st century. Further research into the behavioral and organizational aspects of cartels, combined with advances in machine learning and data analysis, will be critical for staying ahead of increasingly sophisticated collusive schemes.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

Everything You Need To Know

1

What is "collusion" and why is it considered a threat to markets?

"Collusion" refers to scenarios where companies conspire to fix prices or limit competition. It poses a significant threat to markets because it undermines fair competition, leading to artificial price increases and reduced consumer choice, ultimately harming consumer welfare. Competition authorities work to combat these practices using insights from academic research.

2

How has collusion research changed over the past two decades?

Collusion research has shifted from being grounded in stylized oligopoly-game theory to a greater emphasis on empirical case studies of past cartels. This involves analyzing real-world market data and using econometric techniques to identify patterns of collusion. Time series analysis reveals that empirical studies haven't replaced theoretical models entirely but filled a gap left by a decline in rule-based reasoning.

3

What factors have contributed to the shift towards empirical research in the study of collusion?

Several factors have contributed to this shift, including the increasing availability of detailed market data, advances in computing power that allow for the analysis of larger datasets and the development of more sophisticated econometric models, the success of leniency programs that provide valuable insights into cartel operations, and the limitations of traditional game-theoretic models in capturing the complexities of real-world collusion.

4

What are the potential downsides of relying heavily on empirical case studies in collusion research?

While empirical case studies offer valuable insights, a heavy reliance on them has potential downsides. These studies are inherently backward-looking, focusing on past cartels rather than predicting future ones. This may lead to a focus on specific industries or types of collusion, while neglecting emerging forms of anti-competitive behavior in new markets. Therefore, solely relying on empirical data may not be enough to deter cartels.

5

What is the ideal approach to collusion research to effectively detect and deter cartels?

The ideal approach involves balancing economic theory and rule-based models with empirical analysis. A balanced approach is essential for developing effective strategies to detect and deter collusion. Further research into the behavioral and organizational aspects of cartels, combined with advances in machine learning and data analysis, will be critical for staying ahead of increasingly sophisticated collusive schemes. Competition authorities will need to use all tools to combat the threat.

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