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 May 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.

Everything You Need To Know

1

What is algorithmic pricing and how does it work?

Algorithmic pricing utilizes artificial intelligence (AI) to automatically set prices. In the context of online marketplaces, AI algorithms, like those using Q-learning, analyze market data, including competitor pricing, demand, and user behavior, to determine the optimal price for a product or service. These algorithms aim to maximize profits, but can lead to tacit collusion, where companies indirectly coordinate to raise prices without explicit communication.

2

What is tacit collusion and how does AI contribute to it?

The study highlights the potential for tacit collusion driven by AI algorithms. Tacit collusion occurs when companies coordinate to raise prices without direct communication. AI algorithms using Q-learning are particularly adept at this in two-sided markets, where platforms connect buyers and sellers. Network externalities, where the value of a product or service increases with more users, amplify this behavior. Higher discount rates, where companies prioritize short-term gains, also increase the likelihood of collusion, especially when combined with strong network effects.

3

What is Q-learning and how does it relate to pricing?

Q-learning is a type of reinforcement learning used by AI algorithms. These algorithms learn optimal strategies through trial and error, constantly adjusting their pricing decisions to maximize profit. In the context of pricing, Q-learning algorithms analyze market data and user behavior to find the best prices. The study reveals that in two-sided markets, these AI agents using Q-learning can learn to collude, leading to higher prices. This is a significant concern because of the potential for unfair pricing and reduced consumer welfare.

4

How do network externalities influence AI-driven price collusion?

Network externalities significantly influence AI-driven price collusion. Network externalities mean the value of a product increases as more people use it. In the study, AI algorithms exploit these network effects to maximize profits, leading to greater collusion. Platforms with strong network effects, like social media, create an environment where AI can more easily identify and exploit market dynamics to drive up prices. Therefore, understanding and addressing network externalities is crucial in preventing AI-driven collusion.

5

How can we fight against AI-driven price hikes?

To mitigate the risks of AI-driven price collusion, the researchers suggest incorporating a penalty term into the Q-learning algorithm. This penalty disincentivizes algorithms from setting prices significantly higher than the average market price. Other steps include developing regulatory frameworks, promoting transparency in algorithmic pricing, and supporting policies that protect consumers. By focusing on these measures, it's possible to ensure that AI benefits consumers rather than exploits them, thus promoting fair competition and maintaining consumer welfare in the face of advanced pricing strategies.

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