# To Analyze and Regulate Human-in-the-loop Learning for Congestion Games

arXiv: https://arxiv.org/abs/2501.03055 · HTML: https://mlsanity.com/paper/2501.03055/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 6 Jan 2025.

## Builds on (most cited first, 15 of 15)

- [Learning Unknown Service Rates in Queues: A Multi-Armed Bandit Approach](https://mlsanity.com/paper/1604.06377/) (2016)
- [Signaling in Bayesian Network Congestion Games: the Subtle Power of Symmetry](https://mlsanity.com/paper/2002.05190/) (2020)
- [Learning an Unknown Network State in Routing Games](https://mlsanity.com/paper/1905.04433/) (2019)
- [Dynamic Pricing and Mean Field Analysis for Controlling Age of Information](https://mlsanity.com/paper/2004.10050/) (2020)
- [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)
- [Human-in-the-loop Learning for Dynamic Congestion Games](https://mlsanity.com/paper/2404.15599/) (2024)
- [Distributed Learning for Dynamic Congestion Games](https://mlsanity.com/paper/2405.03031/) (2024)
- [To Optimize Human-in-the-loop Learning in Repeated Routing Games](https://mlsanity.com/paper/2411.09867/) (2024)
- [Dynamic routing for social information sharing](https://mlsanity.com/paper/1608.03397/) (2016)
- [Uncertainty in Multi-Commodity Routing Networks: When does it help?](https://mlsanity.com/paper/1709.08441/) (2017)

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

- [Competitive Multi-armed Bandit Games for Resource Sharing](https://mlsanity.com/paper/2503.20975/) (2025)

## Abstract (arXiv)

In congestion games, selfish users behave myopically to crowd to the shortest paths, and the social planner designs mechanisms to regulate such selfish routing through information or payment incentives. However, such mechanism design requires the knowledge of time-varying traffic conditions and it is the users themselves to learn and report past road experiences to the social planner (e.g., Waze or Google Maps). When congestion games meet mobile crowdsourcing, it is critical to incentivize selfish users to explore non-shortest paths in the best exploitation-exploration trade-off. First, we consider a simple but fundamental parallel routing network with one deterministic path and multiple stochastic paths for users with an average arrival probability λ. We prove that the current myopic routing policy (widely used in Waze and Google Maps) misses both exploration (when strong hazard belief) and exploitation (when weak hazard belief) as compared to the social optimum. Due to the myopic policy's under-exploration, we prove that the caused price of anarchy (PoA) is larger than (frac11-ρ^frac1λ), which can be arbitrarily large as discount factor (ρrightarrow1). To mitigate such huge efficiency loss, we propose a novel selective information disclosure (SID) mechanism: we only reveal the latest traffic information to users when they intend to over-explore stochastic paths upon arrival, while hiding such information when they want to under-explore. We prove that our mechanism successfully reduces PoA to be less than~(2). Besides the parallel routing network, we further extend our mechanism and PoA results to any linear path graphs with multiple intermediate nodes.