ITSC 2024 Paper Abstract

Close

Paper FrAT12.4

Koirala, Pravesh (Vanderbilt University), Laine, Forrest (Vanderbilt University)

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

Scheduled for presentation during the Regular Session "ITS Policy and markets" (FrAT12), Friday, September 27, 2024, 11:30−11:50, Salon 20

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Keywords Theory and Models for Optimization and Control, ITS Policy, Design, Architecture and Standards, Simulation and Modeling

Abstract

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.

 

 

All Content © PaperCept, Inc.


This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2024 PaperCept, Inc.
Page generated 2024-12-26  17:10:09 PST  Terms of use