AI robots subtly manipulating prices in a digital marketplace.

AI Price Wars: Are Algorithms Secretly Colluding to Raise Prices?

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Beau Callahan in Tech & Innovation July 2025 • 4 min read.

"New research uncovers how artificial intelligence in two-sided markets might be leading to higher prices, and what we can do about it."


Have you ever noticed how prices for certain products online seem to creep up in unison, even when different retailers are selling them? It might not be a coincidence. Algorithmic pricing, powered by artificial intelligence (AI), is becoming increasingly common, and with it comes the potential for tacit collusion – a situation where companies indirectly coordinate to raise prices without explicitly communicating.

A new study dives deep into how AI algorithms, specifically those using a technique called Q-learning, behave in two-sided markets. These are markets where two distinct groups, like buyers and sellers, interact through a platform (think of online marketplaces like Amazon or ride-sharing apps like Uber). The study reveals some unsettling truths about AI's ability to learn and exploit market dynamics to drive up prices.

While AI promises efficiency and innovation, its application in pricing raises significant questions about fairness and transparency. This article will break down the key findings of this research, explore the real-world implications, and discuss potential solutions to keep these AI price wars in check.

Decoding Algorithmic Collusion: How AI Learns to Hike Prices

AI robots subtly manipulating prices in a digital marketplace.

The research focuses on AI agents using Q-learning, a type of reinforcement learning where algorithms learn optimal strategies through trial and error. The study found that AI-driven platforms consistently achieve higher levels of collusion compared to traditional Bertrand competition, a model where companies independently set prices to maximize profits.

But what factors amplify this collusive behavior? The study identifies network externalities as a key driver. Network externalities occur when the value of a product or service increases as more people use it. Think of social media platforms – the more friends you have on a platform, the more valuable it becomes. The research indicates that AI algorithms are adept at exploiting these network effects to maximize profits, leading to even greater collusion.

  • Network Externalities: Increased network externalities significantly enhance collusion, indicating AI algorithms exploit them to maximize profits.
  • User Heterogeneity: Higher user heterogeneity and greater utility from outside options generally reduce collusion.
  • Discount Rates: Higher discount rates increase collusion, especially with significant network externalities.
  • Feasibility: Tacit collusion remains feasible even at low discount rates.
The study also explored the impact of user behavior and market dynamics. They found that higher user heterogeneity (meaning a greater variety in user preferences) and greater availability of outside options (alternatives to the platform) tend to decrease collusion. Conversely, higher discount rates (when companies prioritize short-term gains over long-term consequences) tend to increase collusion, especially when network externalities are strong. Interestingly, the research found that tacit collusion can remain feasible even when companies aren't heavily focused on immediate profits, suggesting this behavior could be more pervasive than previously thought.

Fighting Back Against the AI Price Hike

So, what can be done to mitigate the risks of AI-driven price collusion? The researchers propose incorporating a penalty term into the Q-learning algorithm. This penalty would disincentivize algorithms from setting prices that are significantly higher than the average market price. While this is just one potential solution, it highlights the need for proactive measures to ensure that AI benefits consumers rather than exploits them. As AI becomes increasingly integrated into our economic systems, it's crucial to develop regulatory frameworks and ethical guidelines to prevent algorithmic collusion and promote fair competition. Staying informed, demanding transparency, and supporting policies that protect consumers are essential steps in navigating this new landscape.

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.2407.04088,

Title: Artificial Intelligence And Algorithmic Price Collusion In Two-Sided Markets

Subject: econ.gn cs.ai cs.gt q-fin.ec

Authors: Cristian Chica, Yinglong Guo, Gilad Lerman

Published: 04-07-2024

Everything You Need To Know

1

What is algorithmic pricing and how does it relate to AI price wars?

Algorithmic pricing utilizes artificial intelligence (AI) to automatically adjust prices. The research explores how AI, specifically through Q-learning, is used in two-sided markets, such as online marketplaces, where buyers and sellers interact. This can lead to tacit collusion, where companies indirectly coordinate to raise prices without explicit communication, sparking AI price wars.

2

How does Q-learning contribute to AI-driven price collusion?

Q-learning, a type of reinforcement learning, enables AI algorithms to learn optimal pricing strategies through trial and error. The study found that AI agents using Q-learning in two-sided markets are prone to collusive behavior. They learn to exploit market dynamics, especially network externalities, to maximize profits, often leading to higher prices for consumers.

3

What are network externalities and how do they affect the AI's collusive behavior?

Network externalities refer to the increased value of a product or service as more people use it, like social media platforms. The study indicates that AI algorithms are adept at exploiting network effects. Increased network externalities significantly enhance collusion, indicating AI algorithms exploit them to maximize profits. This means AI algorithms are more likely to collude and raise prices when network effects are strong, as the platform becomes more valuable to users.

4

Besides network externalities, what other factors influence the likelihood of AI-driven price collusion?

User heterogeneity and the availability of outside options can reduce collusion. Higher user heterogeneity (meaning a greater variety in user preferences) and greater availability of outside options (alternatives to the platform) tend to decrease collusion. Conversely, higher discount rates (when companies prioritize short-term gains over long-term consequences) tend to increase collusion, especially when network externalities are strong.

5

What solutions are proposed to mitigate the risks of AI price collusion?

The researchers propose incorporating a penalty term into the Q-learning algorithm. This would disincentivize algorithms from setting prices that are significantly higher than the average market price. This penalty aims to curb the AI's tendency towards collusive pricing. Furthermore, the article highlights the need for regulatory frameworks and ethical guidelines to prevent algorithmic collusion and promote fair competition, and also suggests being informed, demanding transparency, and supporting consumer-protective policies.

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