Recommending Paths: Follow or Not Follow?

by   Yunpeng Li, et al.

Mobile social network applications constitute an important platform for traffic information sharing, helping users collect and share sensor information about the driving conditions they experience on the traveled path in real time. In this paper we analyse the simple but fundamental model of a platform choosing between two paths: one with known deterministic travel cost and the other that alternates over time between a low and a high random cost states, where the low and the high cost states are only partially observable and perform respectively better and worse on average than the fixed cost path. The more users are routed over the stochastic path, the better the platform can infer its actual state and use it efficiently. At the Nash equilibrium, if asked to take the riskier path, in many cases selfish users will myopically disregard the optimal path suggestions of the platform, leading to a suboptimal system without enough exploration on the stochastic path. We prove the interesting result that if the past collected information is hidden from users, the system becomes incentive compatible and even `sophisticated' users (in the sense that they have full capability to reverse-engineer the platform's recommendation and derive the path state distribution conditional on the recommendation) prefer to follow the platform's recommendations. In a more practical setting where the platform implements a model-free Q-learning algorithm to minimise the social travel cost, our analysis suggests that increasing the accuracy of the learning algorithm increases the range of system parameters for which sophisticated users follow ! the recommendations of the platform, becoming in the limit fully incentive compatible. Finally, we extend the two-path model to include more stochastic paths, and show that incentive compatibility holds under our information restriction mechanism.


page 1

page 10


To Rectify Routing Games for Social Information Sharing

We study a new incentive problem of social information sharing for locat...

A Stackelberg Game Approach Towards Socially-Aware Incentive Mechanisms for Mobile Crowdsensing

Mobile crowdsensing has shown a great potential to address large-scale d...

Optimal dynamic information provision in traffic routing

We consider a two-road dynamic routing game where the state of one of th...

When Congestion Games Meet Mobile Crowdsourcing: Selective Information Disclosure

In congestion games, users make myopic routing decisions to jam each oth...

Incentivizing Combinatorial Bandit Exploration

Consider a bandit algorithm that recommends actions to self-interested u...

Resilient Information Aggregation

In an information aggregation game, a set of senders interact with a rec...

To Save Crowdsourcing from Cheap-Talk: Strategic Learning from Biased Users

Today many users are invited by a crowdsourcing platform (e.g., TripAdvi...

Please sign up or login with your details

Forgot password? Click here to reset