Recommending Paths: Follow or Not Follow?

Abstract

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 (that are allowed to have access to the information collected by the platform) 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.

Crowd-MECS: A Novel Crowdsourcing Framework for Mobile Edge Caching and Sharing

“…devices (EDs) for contributing their storage resources to form the edge storage pool. This leads to a novel crowdsourcingbased [8]- [10] mobile edge caching and sharing service called Crowd-MECS. More specifically, in Crowd-MECS, massive EDs (e.g., mobile smartphones, WiFi routers, and smallcell base stations) are used to cache popular contents at network edges and then share with each other in a decentralized peer-to-peer and on-demand fashion.…”

“…Solving the inequalities in (10) yields two conditions: w > p + s D + c − Ψ and c < Ψ. That is, a type-(w, c) ED satisfying the two conditions will choose the role of being an agent.…”

Distributed double auctions for large-scale device-to-device resource trading

“…We can correct that by properly increasing the correction paymentд(τ ) which is subsidized to this buyer when she submits τ truthfully (line 7). However, after applying the newд(τ ) (line 8-10), (8a) holds for v i =τ but may be violated for a buyer with v i =τ − 1 who may over-report the adjacent value τ to obtain the increased correction paymentд(τ ) (line [11][12]. If this is the case, we further correct it by increasinḡ д(τ − 1) in a way that we don't violate the previously corrected AIC constraint at τ (line 13).…”

“…This is possible due to (7a) and the monotonicity property ofQ B (v i ). Yet this newд(τ − 1) may affect the AIC constraint at v i = τ − 2 and we proceed similarly to correct any possible violations at τ − 2, τ − 3, etc., until (8a) holds for all v i ≤ τ (line [11][12][13][14][15][16][17][18]. After this, we can iteratively increase τ one by one whiling updating the correction payment for given τ , and will eventually ensure (8a) holds for any value in V.…”

“…[6] investigates the cooperative local caching under heterogeneous file preferences, and [17] studies the incentives for nearby mobile users to cooperatively downloading video segments. In [11,12], the sharing platforms are designed to collect the social information from users when traveling and benefit all users that participate. Figure 1: Our double auction mechanism.…”