# Individualized Algorithmic Advice as a Strategic Signal on Competitive Markets

arXiv: https://arxiv.org/abs/2511.09454 · HTML: https://mlsanity.com/paper/2511.09454/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 52 references that are in the map.
- First posted on arXiv 12 Nov 2025.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [Hallucination is Inevitable: An Innate Limitation of Large Language Models](https://mlsanity.com/paper/2401.11817/) (2024)
- [Divergent Creativity in Humans and Large Language Models](https://mlsanity.com/paper/2405.13012/) (2024)
- [Metacognitive Myopia in Large Language Models](https://mlsanity.com/paper/2408.05568/) (2024)
- [Algorithmic Collusion in Cournot Duopoly Market: Evidence from Experimental Economics](https://mlsanity.com/paper/1802.08061/) (2018)
- [Delegate Pricing Decisions to an Algorithm? Experimental Evidence](https://mlsanity.com/paper/2510.27636/) (2025)

## Abstract (arXiv)

As algorithms increasingly mediate competitive decision-making, their influence extends beyond individual outcomes to shaping strategic market dynamics. In our experiment, we examined how algorithmic advice affects human behavior in a classic economic game with a unique, non-collusive, and analytically traceable equilibrium. Participants (N = 129) played a Cournot quantity competition with equilibrium-aligned or strategically biased algorithmic recommendations. While individualized equilibrium advice supported stable convergence, collusively downward-biased advice led to sustained underproduction and supracompetitive profits - hallmarks of tacit collusion. Participants' quantities converged faster and more consistently toward individualized than collective equilibrium advice, potentially due to an objective quality advantage or greater perceived ownership of the former. These findings demonstrate that algorithmic advice can function as a strategic signal, shaping coordination even without explicit communication. The results echo real-world concerns about algorithmic collusion and underscore the need for careful design and oversight of algorithmic decision-support systems in competitive environments.