---
id: "lib-2026-10-08-who-leads-and-who-collects-algorithmic-collusion"
type: "library"
title: "Who Leads and Who Collects: Algorithmic Collusion in Markets of Heterogeneous Language Models"
lang: "EN"
org: "arXiv"
source_type: "Paper"
date: "2026-10-08"
week: "2026-W41"
url: "https://nosov.ai/library/lib-2026-10-08-who-leads-and-who-collects-algorithmic-collusion/"
original_url: "https://arxiv.org/abs/2610.11256"
tier: "A"
tier_name: "Strong evidence"
tier_reason: "Single-author working paper (Pusan National University) running a pre-specified oligopoly experiment with four commercial LLMs, 11 market compositions and 20 runs each, with non-parametric tests; strong design, but cheapest model tiers and one prompt."
summary_basis: "full-text"
original_minutes: "60"
tags: ["ai technology", "business transformation", "pricing agents"]
---

# Who Leads and Who Collects: Algorithmic Collusion in Markets of Heterogeneous Language Models

Tier A (Strong evidence): Single-author working paper (Pusan National University) running a pre-specified oligopoly experiment with four commercial LLMs, 11 market compositions and 20 runs each, with non-parametric tests; strong design, but cheapest model tiers and one prompt.

Summary based on the full text.

## In short

Jun Yeong Lee of Pusan National University (arXiv, October 2026) tests whether AI pricing agents from different vendors collude. Four cheapest-tier models (Gemini, Claude, GPT, DeepSeek) set prices in a simulated four-firm market without communicating. Collusion depends on which models are present: Claude and Gemini markets reach about three-quarters of monopoly profit, DeepSeek a quarter, GPT none. In mixed markets, the model that raises the price earns the least, as price-leadership theory predicts.

## Key numbers

- 79% / 72%: share of monopoly rent captured in all-Claude and all-Gemini markets (collusion index 0.79 and 0.72)
- 24%: share of monopoly rent captured in all-DeepSeek markets; all-GPT markets capture none
- 0.92: collusion index in Gemini plus Claude markets, higher than either alone
- 2 of 4: GPT firms needed to keep any market from converging

## What it reports

- Models: gemini-3.1-flash-lite, claude-haiku-4-5, gpt-5-nano, deepseek-flash, reasoning set to the lowest available level.
- Homogeneous markets: collusion index 0.79 (Claude), 0.72 (Gemini), 0.24 (DeepSeek), -0.15 (GPT). 20 of 20 Claude runs and 18 of 20 Gemini runs earn well above competitive profit, against 10 of 20 DeepSeek and 6 of 20 GPT.
- GPT does not compete: its prices drift above the monopoly level and never settle, so profit falls for the opposite reason.
- Mixing alone does not cut collusion. Gemini plus Claude reaches 0.92; Gemini plus DeepSeek 0.73. Only the market with one firm per model (0.02) is significantly less collusive than the homogeneous average.
- Stability follows the least stable member: every cell without GPT converges in at least 14 of 20 runs; no cell with GPT converges in more than 5.
- Profit order inside mixed markets: DeepSeek over Claude over Gemini over GPT, the reverse of who sets high prices. Gemini anchors high; rivals price slightly below; once settled it answers an undercut only about one time in five.
- Agent notes use "collude" in 21-23% of firm-periods for two models, versus 0.1% for GPT.

## How they got it

A logit Bertrand market with Calvano et al. (2020) parameters and the prompt and memory structure of Fish et al. (2024). 11 compositions (4 homogeneous, 6 two-by-two, 1 fully mixed), 20 runs of 200 periods each, run on 16-17 September 2026. Collusion is measured on the last 50 periods with a profit index, a price index and outcome categories; tests are non-parametric with bootstrap intervals.

## Limits

- One prompt, one market design, cheapest model tiers only; larger models may behave differently.
- Model rankings are a snapshot; providers update models every few months.
- Agent explanations are text, not evidence of mechanism.

## Why it matters for companies

Retailers and distributors adopting AI pricing tools are, in effect, choosing a pricing disposition through their vendor's model. The paper suggests prices can stay well above competitive levels without any agreement, and that the firm that profits most may never have raised a price. That matters for competition-law exposure and for audits: legal and pricing teams should test tools in simulated markets before deployment and log decisions.

Original: https://arxiv.org/abs/2610.11256