Incentive Mechanisms for Internet Congestion Management: Fixed-Budget Rebate versus Time-of-Day Pricing

TL;DR

This paper compares fixed-budget rebate and time-of-day pricing mechanisms for managing internet congestion, demonstrating that the rebate approach is more robust under certain demand sensitivities through a game-theoretic analysis.

Contribution

It introduces a fixed-budget rebate mechanism for congestion management and analyzes its robustness compared to time-of-day pricing using a game-theoretic model.

Findings

Fixed-budget rebate achieves socially optimal decongestion under specific parameters.

Rebate mechanism is more robust with convex user demand sensitivities.

Closed-loop property of rebate mechanism reduces unit reward as demand decreases.

Abstract

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 \emph{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-of-day 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 \emph{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 fixed-budget rebate pricing is more robust when the users' sensitivity to congestion is "sufficiently" convex. This feature of the fixed-budget 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.