Mobile data traffic has been steadily rising in the past years. This has generated a significant interest in the deployment of incentive mechanisms to reduce peak-time congestion. Typically, the design of these mechanisms requires information about user demand and sensitivity to prices. Such information is naturally imperfect. In this paper, we propose a fixed-budget rebate mechanism that gives each user a reward proportional to his percentage contribution to the aggregate reduction in peak time demand. For comparison, we also study a time-ofday pricing mechanism that gives each user a fixed reward per unit reduction of his peak-time demand. To evaluate the two mechanisms, we introduce a game-theoretic model that captures the public good nature of decongestion. For each mechanism, we demonstrate that the socially optimal level of decongestion is achievable for a specific choice of the mechanism's parameter. We then investigate how imperfect information about user demand affects the mechanisms' effectiveness. From our results, the fixedbudget rebate pricing is more robust when the users' sensitivity to congestion is "sufficiently" convex. This feature of the fixedbudget rebate mechanism is attractive for many situations of interest and is driven by its closed-loop property, i.e., the unit reward decreases as the peak-time demand decreases.
Index Terms-congestion pricing; lottery-based incentive mechanisms; public good provisioning; probabilistic pricingPatrick Loiseau is with EURECOM,