Abstract

Abstract

Abstract
This paper investigates the expansion of the network of a monopolist firm that produces a durable good and is also involved in the corresponding aftermarket. We characterize the Markov Perfect Equilibrium of the continuous time dynamic game played by the monopolist and the forward-looking consumers, under the assumption that consumers benefit from the subsequent expansion of the network. The paper contributes to the theoretical discussion on the validity of the Coase conjecture, analyzing whether Coase's prediction that the monopolist serves the market in a "twinkling of an eye" remains valid in our setup. We conclude that the equilibrium network development may actually be gradual, contradicting Coase's conjecture. We find that a necessary condition for such a result is the existence of aftermarket network effects that accrue (at least partly) to the monopolist firm.

Abstract
In a restructured electricity sector, day-ahead markets can be modeled as a game where some players - the producers - submit their proposals. To analyze the companies' behavior we have used the concept of Nash equilibrium as a solution in these multi-agent interaction problems. In this paper, we present new and crucial adaptations of two well-known mechanisms, the adjustment process and the relaxation algorithm, in order to achieve the goal of computing Nash equilibria. The advantages of these approaches are highlighted and compared with those available in the literature.

Abstract
Social media advancements and the rapid increase in volume and complexity of data generated by Internet services are becoming challenging not only technologically, but also in terms of application areas. Performance and availability of data processing are critical factors that need to be evaluated since conventional data processing mechanisms may not provide adequate support. Apache Hadoop with Mahout is a framework to storage and process data at large-scale, including different tools to distribute processing. It has been considered an effective tool currently used by both small and large businesses and corporations, like Google and Facebook, but also public and private healthcare institutions. Given its recent emergence and the increasing complexity of the associated technological issues, a variety of holistic framework solutions have been put forward for each specific application. In this work, we propose a generic functional architecture with Apache Hadoop framework and Mahout for handling, storing and analyzing big data that can be used in different scenarios. To demonstrate its value, we will show its features, advantages and applications on health Twitter data. We show that big health social data can generate important information, valuable both for common users and practitioners. Preliminary results of data analysis on Twitter health data using Apache Hadoop demonstrate the potential of the combination of these technologies. (C) 2015 The Authors. Published by Elsevier B.V.

Abstract
The ordering of events is a fundamental problem of distributed computing and has been extensively studied over several decades. From all the available orderings, total ordering is of particular interest as it provides a powerful abstraction for building reliable distributed applications. Unfortunately, deterministic total order algorithms scale poorly and are therefore unfit for modern large-scale applications. The main contribution of this paper is EPTO, a total order algorithm with probabilistic agreement that scales both in the number of processes and events. EPTO provides deterministic safety and probabilistic liveness: integrity, total order and validity are always preserved, while agreement is achieved with arbitrarily high probability. We show that EPTO is well-suited for large-scale dynamic distributed systems: it does not require a global clock nor synchronized processes, and it is highly robust even when the network suffers from large delays and significant churn and message loss.