# The Spoils of Algorithmic Collusion: Profit Allocation Among Asymmetric Firms

arXiv: https://arxiv.org/abs/2501.07178 · HTML: https://mlsanity.com/paper/2501.07178/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 61 references that are in the map.
- First posted on arXiv 13 Jan 2025.

## Builds on (most cited first, 3 of 3)

- [Reinforcement Learning for Ridesharing: An Extended Survey](https://mlsanity.com/paper/2105.01099/) (2021)
- [Deep Inventory Management](https://mlsanity.com/paper/2210.03137/) (2022)
- [Using Non-Stationary Bandits for Learning in Repeated Cournot Games with Non-Stationary Demand](https://mlsanity.com/paper/2201.00486/) (2022)

## Abstract (arXiv)

We study the propensity of independent algorithms to collude in repeated Cournot duopoly games. Specifically, we investigate the predictive power of different oligopoly and bargaining solutions regarding the effect of asymmetry between firms. We find that both consumers and firms can benefit from asymmetry. Algorithms produce more competitive outcomes when firms are symmetric, but less when they are very asymmetric. Although the static Nash equilibrium underestimates the effect on total quantity and overestimates the effect on profits, it delivers surprisingly accurate predictions in terms of total welfare. The best description of our results is provided by the equal relative gains solution. In particular, we find algorithms to agree on profits that are on or close to the Pareto frontier for all degrees of asymmetry. Our results suggest that the common belief that symmetric industries are more prone to collusion may no longer hold when algorithms increasingly drive managerial decisions.