# Human-in-the-loop Learning for Dynamic Congestion Games

arXiv: https://arxiv.org/abs/2404.15599 · HTML: https://mlsanity.com/paper/2404.15599/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 50 references that are in the map.
- First posted on arXiv 24 Apr 2024.

## Builds on (most cited first, 14 of 14)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Federated Multi-Armed Bandits](https://mlsanity.com/paper/2101.12204/) (2021)
- [Bayesian Exploration: Incentivizing Exploration in Bayesian Games](https://mlsanity.com/paper/1602.07570/) (2016)
- [Multi-Armed Bandits with Correlated Arms](https://mlsanity.com/paper/1911.03959/) (2019)
- [Distributed Bandits with Heterogeneous Agents](https://mlsanity.com/paper/2201.09353/) (2022)
- [Regret-Minimizing Bayesian Persuasion](https://mlsanity.com/paper/2105.13870/) (2021)
- [Dynamic Information Design: A Simple Problem on Optimal Sequential Information Disclosure](https://mlsanity.com/paper/2003.07965/) (2020)
- [Learning an Unknown Network State in Routing Games](https://mlsanity.com/paper/1905.04433/) (2019)
- [Information Design in Non-atomic Routing Games with Partial Participation: Computation and Properties](https://mlsanity.com/paper/2005.03000/) (2020)
- [The Effectiveness of Subsidies and Tolls in Congestion Games](https://mlsanity.com/paper/2102.09655/) (2021)
- [When Congestion Games Meet Mobile Crowdsourcing: Selective Information Disclosure](https://mlsanity.com/paper/2211.14029/) (2022)
- [Recommending Paths: Follow or Not Follow?](https://mlsanity.com/paper/1812.00574/) (2018)
- [Computation Offloading in Heterogeneous Vehicular Edge Networks: On-line and Off-policy Bandit Solutions](https://mlsanity.com/paper/2008.06302/) (2020)
- [Online Pricing Incentive to Sample Fresh Information](https://mlsanity.com/paper/2209.08711/) (2022)

## Built on by (most cited first, 2 of 2)

- [To Analyze and Regulate Human-in-the-loop Learning for Congestion Games](https://mlsanity.com/paper/2501.03055/) (2025)
- [Competitive Multi-armed Bandit Games for Resource Sharing](https://mlsanity.com/paper/2503.20975/) (2025)

## Abstract (arXiv)

Today mobile users learn and share their traffic observations via crowdsourcing platforms (e.g., Waze). Yet such platforms simply cater to selfish users' myopic interests to recommend the shortest path, and do not encourage enough users to travel and learn other paths for future others. Prior studies focus on one-shot congestion games without considering users' information learning, while our work studies how users learn and alter traffic conditions on stochastic paths in a human-in-the-loop manner. Our analysis shows that the myopic routing policy leads to severe under-exploration of stochastic paths. This results in a price of anarchy (PoA) greater than 2, as compared to the socially optimal policy in minimizing the long-term social cost. Besides, the myopic policy fails to ensure the correct learning convergence about users' traffic hazard beliefs. To address this, we focus on informational (non-monetary) mechanisms as they are easier to implement than pricing. We first show that existing information-hiding mechanisms and deterministic path-recommendation mechanisms in Bayesian persuasion literature do not work with even (PoA=∞). Accordingly, we propose a new combined hiding and probabilistic recommendation (CHAR) mechanism to hide all information from a selected user group and provide state-dependent probabilistic recommendations to the other user group. Our CHAR successfully ensures PoA less than (frac54), which cannot be further reduced by any other informational (non-monetary) mechanism. Besides the parallel network, we further extend our analysis and CHAR to more general linear path graphs with multiple intermediate nodes, and we prove that the PoA results remain unchanged. Additionally, we carry out experiments with real-world datasets to further extend our routing graphs and verify the close-to-optimal performance of our CHAR.