Interconnected algorithms subtly manipulating market prices

Algorithmic Collusion: Are Your Smart Devices Secretly Rigging the System?

Mira Elwood in Tech & Innovation November 2025 • 4 min read.

"Explore how algorithms in AI, finance, and even everyday apps might be learning to collude—and what it means for you."

In an era increasingly governed by algorithms, our reliance on automated systems has expanded into nearly every facet of life. From setting prices in online retail to executing complex financial trades, algorithms now make decisions that profoundly impact markets and consumers. This shift raises a critical question: are these algorithms merely optimizing for efficiency, or are they learning to collude, potentially undermining fair competition and consumer welfare?

The concept of "algorithmic collusion" introduces a new twist to traditional economic theory. Unlike human collusion, which requires explicit agreements and carries significant legal risks, algorithmic collusion can emerge subtly as algorithms independently learn to coordinate their actions. This can lead to artificially inflated prices, reduced innovation, and diminished consumer choices—all without any visible agreement among the parties involved.

Recent research is starting to uncover the conditions under which algorithms, particularly those using reinforcement learning, can spontaneously develop collusive behaviors. These findings suggest that the design and competitive environment in which these algorithms operate play a crucial role in whether collusion arises. Understanding these dynamics is essential for policymakers, businesses, and consumers alike to ensure that the benefits of AI and automation are not overshadowed by their potential to manipulate the system.

What is Algorithmic Collusion and How Does It Work?

Interconnected algorithms subtly manipulating market prices

Algorithmic collusion occurs when algorithms, designed to operate independently, learn to coordinate their actions in a way that reduces competition and increases profits for those deploying them. This coordination doesn't require any explicit communication or agreement; rather, it emerges as a result of the algorithms' learning processes.

The most common scenario involves algorithms using reinforcement learning, where they repeatedly interact within a competitive environment, adjusting their strategies based on the outcomes. Over time, these algorithms can identify strategies that, while not explicitly collusive, lead to outcomes similar to those achieved through traditional collusion.

Reinforcement Learning: Algorithms learn through trial and error, optimizing their actions based on rewards or penalties.
Exploration vs. Exploitation: Algorithms balance exploring new strategies with exploiting known profitable ones.
Asynchronous Updating: Algorithms update their strategies at different times, leading to complex interactions.
Emergent Coordination: Collusive behavior arises without explicit programming or communication.

Consider two competing online retailers using Q-learning algorithms to set prices. Each algorithm independently adjusts its pricing strategy to maximize profits. Over time, both algorithms might learn that by maintaining higher prices, they both achieve greater profits than they would in a fiercely competitive pricing war. This happens because each algorithm recognizes that undercutting the other leads to a cycle of decreasing prices and reduced margins for both. The result is a stable, collusive pricing strategy that benefits the retailers at the expense of consumers.

The Future of Algorithmic Competition

As algorithms continue to permeate our economic systems, understanding and mitigating the risks of algorithmic collusion will become increasingly important. Future research should focus on developing methods to detect and prevent collusion, as well as establishing regulatory frameworks that promote fair competition in the age of AI. By addressing these challenges, we can harness the benefits of algorithmic automation while safeguarding the interests of consumers and ensuring a level playing field for businesses.

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.2312.02644,

Title: Algorithmic Collusion Under Competitive Design

Subject: econ.th

Authors: Ivan Conjeaud

Published: 05-12-2023

Everything You Need To Know

What exactly is algorithmic collusion and how does it differ from traditional collusion?

Algorithmic collusion is when algorithms, operating independently, learn to coordinate their actions to reduce competition and increase profits for their deployers. Unlike traditional collusion, which requires explicit agreements and carries legal risks, algorithmic collusion emerges subtly through the algorithms' learning processes. This can lead to artificially inflated prices and diminished consumer choices without visible agreement. The algorithms use Reinforcement Learning and constantly Explore vs. Exploit strategies. The algorithms update asynchronously leading to complex interactions and Emergent Coordination.

How do reinforcement learning algorithms contribute to algorithmic collusion?

Reinforcement learning algorithms play a significant role in algorithmic collusion by enabling algorithms to learn through trial and error within a competitive environment. They adjust strategies based on the outcomes of their actions, optimizing for rewards or penalties. Over time, these algorithms can identify strategies that, while not explicitly collusive, lead to outcomes similar to traditional collusion, such as maintaining higher prices for increased profits. The algorithms may use Q-learning. Reinforcement learning enables Emergent Coordination.

Can you provide a concrete example of how algorithmic collusion might work in practice?

What are the key factors that determine whether algorithms will engage in collusive behavior?

Several factors influence whether algorithms engage in collusive behavior. The design of the algorithms, particularly the use of reinforcement learning, plays a crucial role. The competitive environment in which algorithms operate is also significant; factors such as the number of competitors and the frequency of interactions can impact collusion. Additionally, the algorithms' ability to explore different strategies (Exploration vs. Exploitation) and update their strategies asynchronously can contribute to the emergence of collusive behaviors. The lack of explicit programming for collusion and Emergent Coordination, makes it even more difficult to detect and regulate.

What steps can be taken to prevent or mitigate the risks of algorithmic collusion in the future?

Preventing algorithmic collusion requires a multi-faceted approach. Future research should focus on developing methods to detect and prevent collusion, such as monitoring pricing patterns and identifying coordinated behavior. Establishing regulatory frameworks that promote fair competition in the age of AI is also essential. These frameworks should consider the unique challenges posed by algorithms, such as the lack of explicit communication. It's also vital to foster transparency in algorithmic design and operation. The ability for algorithms to use Reinforcement Learning and constantly Explore vs. Exploit strategies needs to be monitored.