Interplay between security providers, consumers, and attackers: a weighted congestion game approach

Network users can choose among different security solutions to protect their data. Those solutions are offered by competing providers, with possibly different performance and price levels. In this paper, we model the interactions among users as a noncooperative game, with a negative externality coming from the fact that attackers target popular systems to maximize their expected gain. Using a nonatomic weighted congestion game model for user interactions, we prove the existence and uniqueness of a user equilibrium, compute the corresponding Price of Anarchy, that is the loss of efficiency due to user selfishness, and investigate some consequences for the (higher-level) pricing game played by security providers.Game theory;Weighted games; Security

Con Edison: The Crisis of the Investor-Owned Utility

Consolidated Edison of New York, Inc. (Con Edison) is an investor-owned urban utility which provides electricity to New York City and most of Westchester County. It is representative of the older investor-owned utilities which are currently in the poorest financial condition. Although these utilities do not serve rapidly expanding service areas, the replacement of obsolete generating plants necessitates continued large capital expenditures. Present conditions raise the possibility that older investor-owned utilities cannot survive in their present form of regulated private monopoly. The failure of Con Edison to pay a quarterly dividend on April 23, 1974 focused attention on the deteriorating financial condition of the utility industry. Investor confidence in all utility securities plummeted and an unprecedented plunge in the value of such securities followed. The key to Con Edison\u27s financial dilemma is that revenues have not increased sufficiently to compensate for higher operating and capital costs. While some of Con Edison\u27s problems are unique, its financial crisis stems from forces buffeting the entire utility industry. This Comment will examine its problems and make suggestions for ameliorating them

Muslim and Gay: Seeking identity coherence in New Zealand

This is an Accepted Manuscript of an article published by Taylor & Francis in Culture, Health & Sexuality on 2015, available online: https://www.tandfonline.com/doi/full/10.1080/13691058.2015.1079927The process of accepting oneself as gay and of ‘coming out’ to family and friends is well documented. For Muslim men, this is complicated by the tension between their emerging sexual identity and their religious and cultural birth identity, which labels homosexuality as sinful. This paper explores this process in a sample of five gay Muslim men living in New Zealand, a liberal secular society where homosexuality is widely accepted and gay rights are endorsed in legislation. Identity Process Theory drives the analysis, which identifies five themes encapsulating the process of striving for psychological coherence: resistance, acceptance, tension, renegotiation and pretence. Initial phases of denial and anger at their emerging sexuality are strongly linked to the conflict with their religious identity. Later, acceptance of their sexuality as natural and even God-given protects them from blame for their ‘sins’. In contrast to earlier work in the UK, for most men, renegotiation of their Muslim identity is adopted as the key strategy for achieving intrapsychic coherence. However, at an interpersonal level, families remain a source of conflict, temporarily resolved through pretence. Renegotiating religious identity leaves men having to pretend not just to be straight, but also to be strongly religious.tru

Estimating the Probability of a Rare Event Over a Finite Time Horizon

We study an approximation for the zero-variance change of measure to estimate the probability of a rare event in a continuous-time Markov chain. The rare event occurs when the chain reaches a given set of states before some fixed time limit. The jump rates of the chain are expressed as functions of a rarity parameter in a way that the probability of the rare event goes to zero when the rarity parameter goes to zero, and the behavior of our estimators is studied in this asymptotic regime. After giving a general expression for the zero-variance change of measure in this situation, we develop an approximation of it via a power series and show that this approximation provides a bounded relative error when the rarity parameter goes to zero. We illustrate the performance of our approximation on small numerical examples of highly reliableMarkovian systems. We compare it to a previously proposed heuristic that combines forcing with balanced failure biaising. We also exhibit the exact zero-variance change of measure for these examples and compare it with these two approximations

Competition among providers in loss networks

International audienceCommunication networks are becoming ubiquitous and more and more competitive among revenue-maximizing providers, operating on potentially different technologies. In this paper, we propose to analyze thanks to game theory the competition of providers playing with access prices and fighting for customers. Considering a slotted-time model, the part of demand exceeding capacity is lost and has to be resent. We consider an access price for submitted packets, thus inducing a congestion pricing through losses. Customers therefore choose the provider with the cheapest average price per correctly transmitted unit of traffic. The model is a two-level game, the lower level for the distribution of customers among providers, and the upper level for the competition on prices among providers, taking into account what the subsequent repartition at the lower level will be. We prove that the upper level has a unique Nash equilibrium, for which the user repartition among different available providers is also unique, and, remarkably, efficient in the sense of social welfare (with a so-called price of anarchy equal to one). Moreover, even when adding a higher level game on capacity disclosure with a possibility of lying for providers, providers are better off being truthful, and the unique Nash equilibrium is thus unchanged

On the use of a Modified Latin Hypercube Sampling (MLHS) approach in the estimation of a Mixed Logit model for vehicle choice

Quasi-random number sequences have been used extensively for many years in the simulation of integrals that do not have a closed-form expression, such as Mixed Logit and Multinomial Probit choice probabilities. Halton sequences are one example of such quasi-random number sequences, and various types of Halton sequences, including standard, scrambled, and shuffled versions, have been proposed and tested in the context of travel demand modeling. In this paper, we propose an alternative to Halton sequences, based on an adapted version of Latin Hypercube Sampling. These alternative sequences, like scrambled and shuffled Halton sequences, avoid the undesirable correlation patterns that arise in standard Halton sequences. However, they are easier to create than scrambled or shuffled Halton sequences. They also provide more uniform coverage in each dimension than any of the Halton sequences. A detailed analysis, using a 16-dimensional Mixed Logit model for choice between alternative-fuelled vehicles in California, was conducted to compare the performance of the different types of draws. The analysis shows that, in this application, the Modified Latin Hypercube Sampling (MLHS) outperforms each type of Halton sequence. This greater accuracy combined with the greater simplicity make the MLHS method an appealing approach for simulation of travel demand models and simulation-based models in general

A Randomized Quasi-Monte Carlo Simulation Method for Markov Chains

We introduce and study a randomized quasi-Monte Carlo method for estimating the state distribution at each step of a Markov chain, under the assumption that the chain has a totally ordered (discrete or continuous) state space. The number of steps in the chain can be random and unbounded. The method simulates $n$ copies of the chain in parallel, using a $(d+1)$-dimensional low-discrepancy point set of cardinality $n$, randomized independently at each step, where $d$ is the number of uniform random numbers required at each transition of the Markov chain. This technique is effective in particular to obtain a low-variance unbiased estimator of the expected total cost up to some random stopping time, when state-dependent costs are paid at each step. We provide numerical illustrations where the variance reduction with respect to standard Monte Carlo is substantial. The variance is reduced by factors of several thousands in some cases. We prove bounds on the convergence rate of the worst-case error and variance for special situations. In line with what is typically observed in RQMC contexts, our empirical results indicate much better convergence than what these bounds guarantee

An Optimal Congestion and Cost-Sharing Pricing Scheme for Multiclass Services

We study in this paper a social welfare optimal congestion-pricing scheme for multiclass queuing services which can be applied to telecommunication networks. Most of the literature has focused on the marginal price. Unfortunately, it does not share the total cost among the different classes. We investigate here an optimal Aumann-Shapley congestion-price which verifies this property. We extend the work on the Aumann-Shapley price for priority services, based on the results on the marginal price: instead of just determining the cost repartition among classes for rates, we obtain the rates and charges that optimize the social welfare

Competition between wireless service providers sharing a radio resource

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Analysis of duopoly price competition between WLAN providers

Proceedings of the IEEE International Conference on Communications, 2009, p. 1-5With the rapid development of wireless Internet services, several WLAN service providers may coexist in one public hotspot to compete for the same group of customers, leading to an inevitable price competition. The charged price and the provisioned packet loss at each provider are major factors in determining users' demands and behaviors, which in turn will affect providers' revenue and social welfare. In this paper, we set up a novel game model to analyze a duopoly price competition. We first show the users' demands are distributed between providers according to a Wardrop Equilibrium and then prove the existence of a Nash equilibrium on providers' charged prices. Through analysis, we further find that in Nash equilibrium state the social welfare is very close to its maximal value in cooperative situation. Furthermore, the providers' aggregate revenues also do not decrease when the users have high sensitivity about the charged prices. Thus the competitive duopoly WLAN market can still run in an efficient way even in the absence of complex regulation schemes. ©2009 IEEE.published_or_final_versio