We address whether local ISPs should be allowed to charge content providers, who derive advertising revenue, for the right to access end-users. We compare two-sided pricing where such charges are allowed to one-sided pricing where they are prohibited. By deriving provider equilibrium actions (prices and investments), we determine which regime is welfare-superior as a function of a few key parameters. We find that two-sided pricing is more favorable when the ratio between parameters characterizing advertising rates and end-user price sensitivity is either low or high.
IntroductionToday, an Internet service provider (ISP) charges both end-users who subscribe to that ISP for their last-mile Internet access as well as content providers that are directly connected to the ISP. However, an ISP generally does not charge content providers that are not directly attached to it for delivering content to end-users. One of the focal questions in the network neutrality policy debate is whether these current charging practices should continue and be mandated by law, or if ISPs ought to be allowed to charge all content providers that deliver content to the ISP's end-users. Indeed the current network neutrality debate began when the CEO of AT&T suggested that such charges be allowed (see Whitacre, 2005).To address this question, we develop a two-sided market model of the interaction of ISPs, end-users, and content providers. The model is closely related to the existing two-sided markets literature as we detail later in this section. In our model, the ISPs play the "platform" role that intermediates the two sides: content providers and end-users. We model a "neutral"
This paper develops and analyzes a game theoretic model to study how the network regime (neutral or non-neutral) affects provider investment incentives, network quality and user prices. We formulate the conditions under which a non-neutral network is more favorable for providers and users. Our results indicate that the non-neutral regime is more favorable when the advertising rate is either low or high. When the advertising rate is high relative to the end-users' sensitivity to price, it is beneficial that the transit provider be able to charge the content providers who are not attached directly to them. This has the affect of passing some of the advertising revenue to the transit providers which in turn incentivizes them to invest. Conversely when the advertising rate is low, it is beneficial for all parties for transit providers to pay the content providers, which has the affect of sharing end-user revenue with the content providers in order to incentivize their investment. When the advertising rate is in the intermediate range, the neutral regime can be preferable (in terms of social welfare) because it prevents the multiple-indemnization that occurs in the non-neutral regime because transport providers tend to over-charge. In that latter case, the degree by which the neutral regime is preferable increases with the number of transit providers.
Attack detection is usually approached as a classification problem. However, standard classification tools often perform poorly because an adaptive attacker can shape his attacks in response to the algorithm. This has led to the recent interest in developing methods for adversarial classification, but to the best of our knowledge, there have been very few prior studies that take into account the attacker's tradeoff between adapting to the classifier being used against him with his desire to maintain the efficacy of his attack. Including this effect is key to derive solutions that perform well in practice.In this investigation we model the interaction as a game between a defender who chooses a classifier to distinguish between attacks and normal behavior based on a set of observed features and an attacker who chooses his attack features (class 1 data). Normal behavior (class 0 data) is random and exogenous. The attacker's objective balances the benefit from attacks and the cost of being detected while the defender's objective balances the benefit of a correct attack detection and the cost of false alarm. We provide an efficient algorithm to compute all Nash equilibria and a compact characterization of the possible forms of a Nash equilibrium that reveals intuitive messages on how to perform classification in the presence of an attacker. We also explore qualitatively and quantitatively the impact of the non-attacker and underlying parameters on the equilibrium strategies.
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,
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