Surreal digital illustration of secret business collusion being uncovered by data science.

Can Data Science Uncover Cartels? A Classroom Game Reveals All

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

"Explore how a unique economics game uses data science to detect hidden market collusion, offering a practical approach to understanding economic dynamics."


Cartels, secret agreements between businesses to control prices or limit production, have long been a subject of intense scrutiny by economists and policymakers. These illicit arrangements not only distort markets but also harm consumers by artificially inflating prices and reducing choice. As the global economy becomes increasingly data-driven, new techniques leveraging data science are emerging as powerful tools in the fight against cartels.

Traditionally, cartel detection relied heavily on theoretical models and qualitative analysis. However, the rise of machine learning and big data analytics has opened up new possibilities for identifying suspicious patterns and behaviors that might indicate collusion. A recent study introduces a classroom game designed to integrate economics and data science, providing students with a hands-on experience in cartel detection.

This innovative game simulates a procurement market where participants act as firms, making decisions about competitive behavior and potential collusion. By combining economic principles with data analysis techniques, the game offers a practical and engaging way to understand the complexities of market dynamics and the role of data science in uncovering illegal activities.

How the Game Works: An Overview

Surreal digital illustration of secret business collusion being uncovered by data science.

The classroom game is structured in three distinct parts, each designed to build upon the previous one and provide a comprehensive learning experience:

In the first two parts of the game, participants step into the shoes of firms operating in a procurement market. Their goal is to maximize profits by strategically bidding on projects. Participants must decide whether to compete fairly or collude with other firms to manipulate prices. Success in these stages depends on a solid understanding of market dynamics, cost structures, and the potential risks and rewards of collusion.

  • Part 1: Competitive Bidding: Firms operate independently, without communication, and must submit bids based on their cost estimates and market knowledge.
  • Part 2: Collusion Allowed: Firms are now allowed to communicate and coordinate their bidding strategies, simulating a cartel environment. However, they must be wary of detection.
  • Part 3: Cartel Detection: Participants transition to the role of competition authority members, tasked with analyzing bidding data to identify patterns indicative of collusion. They use data science techniques to flag suspicious tenders and advise their board on which cases merit further investigation.
The third part shifts the focus to cartel detection. Drawing on real-world examples and recent literature on machine-learning-based cartel screening, participants analyze the bidding data from the previous rounds. They look for patterns and anomalies that might suggest collusive behavior, such as price fixing, bid rotation, or market sharing. Success in this stage depends on their ability to apply data science skills to uncover hidden conspiracies.

The Future of Cartel Detection

The integration of data science techniques is poised to revolutionize cartel detection, making it more efficient and effective. As technology advances, new methods for analyzing market data and identifying suspicious behaviors will continue to emerge. By equipping students with the skills and knowledge to apply these techniques, educators can play a vital role in shaping the next generation of economists and competition authorities.

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.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2401.14757,

Title: How To Use Data Science In Economics -- A Classroom Game Based On Cartel Detection

Subject: econ.gn q-fin.ec

Authors: Hannes Wallimann, Silvio Sticher

Published: 26-01-2024

Everything You Need To Know

1

What is a cartel?

A cartel is a secret agreement among businesses to control prices or limit production. These arrangements distort markets, harming consumers by inflating prices and reducing choices. The classroom game simulates this environment to help participants understand the implications of collusion and how to identify it using data analysis.

2

How does the classroom game teach about cartels?

The game is structured in three parts. In Part 1, participants compete independently. In Part 2, they are allowed to collude, simulating a cartel. In Part 3, participants become the competition authority and use data science techniques to analyze the bidding data and identify patterns of collusion, like price fixing or bid rotation, using methods that are used in the real world.

3

What data science techniques are used in the classroom game to detect cartels?

Participants use data analysis techniques to identify suspicious bidding patterns indicative of collusion. They analyze bidding data for anomalies such as price fixing, bid rotation, or market sharing. The game uses methods inspired by real-world examples of machine-learning-based cartel screening. This hands-on experience equips participants with the skills to uncover hidden conspiracies in a data-driven environment.

4

Why is integrating data science important in the fight against cartels?

Data science provides powerful tools for identifying suspicious patterns and behaviors that might indicate collusion, which can be difficult to spot using traditional methods. Machine learning and big data analytics open new possibilities. The classroom game demonstrates how data science can be applied to analyze market data and uncover illegal activities, making cartel detection more efficient and effective in the future.

5

How does the classroom game's structure enhance understanding of market dynamics and competitive strategy?

The game's three parts build upon each other to provide a comprehensive learning experience. Part 1 introduces competitive bidding. Part 2 allows firms to collude, simulating a cartel environment. Part 3 transitions participants to the role of competition authority, using data analysis to detect collusion. This hands-on approach allows a solid understanding of market dynamics, cost structures, and the risks and rewards of collusion, mirroring real-world scenarios.

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