# Algorithmic collusion in a two-sided market: A rideshare example

arXiv: https://arxiv.org/abs/2405.02835 · HTML: https://mlsanity.com/paper/2405.02835/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 5 May 2024.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [PettingZoo: Gym for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2009.14471/) (2020)
- [Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning](https://mlsanity.com/paper/2002.02667/) (2020)
- [Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy Optimization](https://mlsanity.com/paper/1911.05478/) (2019)
- [Monte Carlo Optimization for Solving Multilevel Stackelberg Games](https://mlsanity.com/paper/2312.03282/) (2023)
- [Mathematical Program Networks](https://mlsanity.com/paper/2404.03767/) (2024)
- [Decreasing Wages in Gig Economy: A Game Theoretic Explanation Using Mathematical Program Networks](https://mlsanity.com/paper/2404.10929/) (2024)

## Built on by (most cited first, 1 of 1)

- [Wasserstein Equilibrium Decoding for Reliable Medical Visual Question Answering](https://mlsanity.com/paper/2605.18313/) (2026)

## Abstract (arXiv)

With dynamic pricing on the rise, firms are using sophisticated algorithms for price determination. These algorithms are often non-interpretable and there has been a recent interest in their seemingly emergent ability to tacitly collude with each other without any prior communication whatsoever. Most of the previous works investigate algorithmic collusion on simple reinforcement learning (RL) based algorithms operating on a basic market model. Instead, we explore the collusive tendencies of Proximal Policy Optimization (PPO), a state-of-the-art continuous state/action space RL algorithm, on a complex double-sided hierarchical market model of rideshare. For this purpose, we extend a mathematical program network (MPN) based rideshare model to a temporal multi origin-destination setting and use PPO to solve for a repeated duopoly game. Our results indicate that PPO can either converge to a competitive or a collusive equilibrium depending upon the underlying market characteristics, even when the hyper-parameters are held constant.