AI robots playing chess, symbolizing algorithmic collusion.

Are AI Pricing Algorithms Secretly Cheating You? The Truth About Algorithmic Collusion

Elliot Brynn in Business & Economy July 2025 • 4 min read.

"New research reveals how AI used for pricing could lead to higher costs for consumers and lower profits for businesses, understand the trade-offs and what it means for your wallet."

Artificial intelligence (AI) is rapidly transforming how businesses operate, especially in setting prices. Algorithms now analyze vast amounts of data to determine the optimal price for goods and services. This shift raises crucial questions: Are these AI pricing strategies truly competitive, or are they leading to a new form of hidden collusion that ultimately hurts consumers? Recent research sheds light on this complex issue, revealing surprising mechanisms behind algorithmic pricing and its impact on markets.

The rise of AI in pricing has sparked concerns about two main issues: algorithmic price discrimination and algorithmic tacit collusion. Price discrimination involves charging different prices to different customers for the same product, while tacit collusion refers to algorithms coordinating to maintain high prices without explicit agreements. While these issues have often been studied separately, a new study investigates how they interact, uncovering unexpected consequences for both consumers and businesses.

This article explores the key findings of this research, explaining the mechanisms behind algorithmic collusion, the factors that influence it, and the implications for consumer welfare and industry profits. We'll break down the complex concepts into easy-to-understand terms, revealing the truth about how AI pricing algorithms might be secretly cheating you.

How Do AI Pricing Algorithms Collude?

AI robots playing chess, symbolizing algorithmic collusion.

The study uses computer simulations to model how AI pricing algorithms behave in competitive markets. In these simulations, firms use Q-learning algorithms, a type of machine learning, to set prices based on private signals about a buyer's willingness to pay (WTP). The research uncovers two novel mechanisms that can lead to collusive outcomes:

Asymmetric Information: In scenarios where one algorithm has better information than the other, the informed AI can adopt a 'Bait-and-Restrained-Exploit' strategy. This means the informed AI sets higher prices on some signals (the 'bait') to entice the less informed AI to also set high prices. Then, on other signals, the informed AI sets much lower prices (the 'exploit') to capture limited profits while preventing the less informed AI from undercutting them.

Baiting the Competition: The informed AI sacrifices some profit by setting higher prices on specific signals, tricking the less informed AI into thinking that high prices are sustainable.
Restrained Exploitation: To prevent the less informed AI from lowering prices, the informed AI sets very low prices on other signals, accepting limited profits but securing its market share.

Symmetric Information: Even when both algorithms have similar information, competition on certain signals can inadvertently lead to collusion on others. If algorithms compete fiercely on some signals, they may learn to set supra-competitive prices (prices above market value) on the remaining signals to compensate for their losses. The algorithms tend to collude more on signals with higher expected WTP, meaning they charge higher prices to customers who are likely willing to pay more.

The Future of AI Pricing: Balancing Profits and Fairness

The findings suggest a critical takeaway: the over-usage of data by AI in competitive markets can weaken collusion, leading to lower industry profits. This means that firms aiming to boost profits through AI adoption should be aware of this potential drawback. While precise price discrimination might seem like a path to higher earnings, it can inadvertently lead to more competitive pricing and reduced collusion. Ultimately, the key lies in finding the right balance between data usage, pricing strategies, and competitive dynamics to achieve both profitability and fairness in the marketplace.

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.2403.0615,

Title: Algorithmic Collusion And Price Discrimination: The Over-Usage Of Data

Subject: econ.gn q-fin.ec

Authors: Zhang Xu, Mingsheng Zhang, Wei Zhao

Published: 10-03-2024

Everything You Need To Know

What is Algorithmic Tacit Collusion, and how does it differ from Price Discrimination?

Algorithmic tacit collusion involves AI algorithms coordinating to maintain higher prices without explicit agreements, essentially a form of price-fixing. Unlike traditional collusion, it occurs without direct communication. Price discrimination, on the other hand, involves AI charging different prices to different customers for the same product based on their perceived willingness to pay (WTP). While price discrimination focuses on personalized pricing, algorithmic collusion aims at maintaining overall elevated prices across the market. Both are concerns because they can lead to consumers paying more.

How do AI Pricing Algorithms collude, and what are the primary mechanisms involved?

AI pricing algorithms can collude through several mechanisms. One involves asymmetric information, where an informed AI uses a 'Bait-and-Restrained-Exploit' strategy. In this, it sets higher prices ('bait') to trick a less informed AI into matching them, then lowers prices ('exploit') on other signals to secure market share, preventing the less informed AI from undercutting. Another mechanism is symmetric information, where algorithms competing on certain signals may learn to set supra-competitive prices on others to compensate for losses, especially on signals with higher expected willingness to pay (WTP).

What is the 'Bait-and-Restrained-Exploit' strategy, and how does it facilitate collusion among AI pricing algorithms?

The 'Bait-and-Restrained-Exploit' strategy is a key mechanism in how AI algorithms with asymmetric information collude. The informed AI uses this strategy by first setting higher prices ('bait') on specific signals to mislead the less informed AI into believing higher prices are sustainable. Then, the informed AI sets very low prices ('exploit') on other signals, accepting limited profits to prevent the less informed AI from undercutting it, thus maintaining its market share. This carefully orchestrated approach allows the informed AI to subtly manipulate pricing dynamics, leading to collusive outcomes.

How does the over-usage of data impact algorithmic collusion and industry profits in competitive markets?

The over-usage of data by AI in competitive markets can weaken collusion, which subsequently leads to lower industry profits. As AI algorithms utilize more data, they may inadvertently compete more aggressively, leading to more competitive pricing. While precise price discrimination might initially seem like a pathway to higher earnings, it can, paradoxically, promote more competitive pricing and reduce collusion. Finding a balance between data usage, pricing strategies, and competitive dynamics is crucial for both profitability and fairness.

What are the key implications of AI pricing algorithms for consumers and businesses?

For consumers, the use of AI pricing algorithms, especially through algorithmic tacit collusion and price discrimination, poses the risk of higher prices and reduced consumer welfare. They might end up paying more for goods and services due to algorithms coordinating to maintain elevated prices. For businesses, the study shows that while AI can be used to optimize pricing, relying too heavily on data and pursuing precise price discrimination can paradoxically lead to more competitive pricing and lower industry profits. It suggests that firms need to carefully balance their data usage, pricing strategies, and the competitive dynamics to achieve both profitability and fair market practices.