When Congestion Games Meet Mobile Crowdsourcing: Selective Information Disclosure

TL;DR

This paper investigates how selective information disclosure in mobile crowdsourcing can improve traffic routing efficiency in congestion games, reducing the price of anarchy from potentially infinite to a bounded value.

Contribution

It introduces a novel SID mechanism that strategically reveals traffic info to balance exploration and exploitation, significantly improving efficiency in congestion games.

Findings

PoA of myopic routing can be arbitrarily large as discount factor approaches 1.

SID mechanism reduces PoA to less than 1/(1 - ρ/2).

Simulation results confirm improved average-case performance.

Abstract

In congestion games, users make myopic routing decisions to jam each other, and the social planner with the full information designs mechanisms on information or payment side to regulate. However, it is difficult to obtain time-varying traffic conditions, and emerging crowdsourcing platforms (e.g., Waze and Google Maps) provide a convenient way for mobile users travelling on the paths to learn and share the traffic conditions over time. When congestion games meet mobile crowdsourcing, it is critical to incentive selfish users to change their myopic routing policy and reach the best exploitation-exploration trade-off. By considering a simple but fundamental parallel routing network with one deterministic path and multiple stochastic paths for atomic users, we prove that the myopic routing policy's price of anarchy (PoA) is larger than $\frac{1}{1-\rho}$, which can be arbitrarily large as discount factor $\rho\rightarrow1$. To remedy such huge efficiency loss, we propose a selective information disclosure (SID) mechanism: we only reveal the latest traffic information to users when they intend to over-explore the stochastic paths, while hiding such information when they want to under-explore. We prove that our mechanism reduces PoA to be less than $\frac{1}{1-\frac{\rho}{2}}$. Besides the worst-case performance, we further examine our mechanism's average-case performance by using extensive simulations.