The Tragedy of the Anti-Commons: A New Problem. An Application to the Fisheries.

The operation and management of common property resources (“the commons”) have been exhaustively examined in economics and political science, both in formal analysis and in practical applications. “Tragedy of the Commons” metaphor helps to explain why people overuse shared resources. On the other side, Anti-Commons Theory is a recent theory presented by scientists to explain several situations about new Property Rights concerns. An “anti-commons” problem arises when there are multiple rights to exclude. Little attention has been given to the setting where more than one person is assigned with exclusion rights, which may be exercised. We analyze the “anti-commons” problem in which resources are inefficiently underutilized rather than over-utilized as in the familiar commons setting. In fact, these two problems are symmetrical in several aspects.Anti-Commons Theory; Property Rights

Some notes on a semi-Markov matrix occurring in the M|M|∞ queue parameters study

The main objective of this work is to present a process to compute the Markov renewal matrix R(t) for Markov renewal processes with countable infinite spaces, which semi-Markov matrixes Q(t) are immigration and death type and assume a tridiagonal form. These processes occur often in practical applications. And the difficulty in obtaining friendly results for R(t)is a great obstacle to its application in practical cases modelling. It is considered the application to the M|M|∞ queue particular case

A Specific M∣G∣∞M|G|\infty Queue System Busy Period and Busy Cycle Distributions and Parameters

Solving a Riccati equation, induced by the study of the transient behaviour of the MGInf queue system, a collection of service times distributions is determined. For the MGInf queue, which service time distribution is a member of that collection, the busy period and busy cycle probabilistic studies are performed. In extra, the properties of that distributions collection are deduced and presented.Comment: 6 pages and no figure

Some considerations about the M|G|∞ queue approximation by a Markov renewal process

Some M|G|∞ queue systems interesting quantities values approximations, obtained through the consideration of an adequate Markov renewal process, are presented and studied

The pandemic period length modelled through queue systems

Despite the huge progress in infectious diseases control worldwide, still epidemics happen, being the annual influenza outbreaks examples of those occurrences. To have a forecast for the epidemic period length is very important because, in this period, it is necessary to strengthen the health care. With more reason, this happens with the pandemic period, since the pandemic is an epidemic with a great population and geographical dissemination. Predominantly using results on the M|G|∞ queue busy period, it is presented an application of this queue system to the pandemic period’s parameters and distribution function study. The choice of the queue for this model is adequate, with great probability, since the greatest is the number of contagions the greatest the possibility of the hypothesis that they occur according to a Poisson process

M|G|∞ queue systems busy period and logistics

In the M|G|∞ queueing systems customers arrive according to a Poisson process at rate . Each of them receives immediately after its arrival a service whose length is a positive random variable with distribution function G(.) and mean value a . An important parameter of the system is the traffic intensity . The service of a customer is independent of the services of the other customers and of the arrival process. The busy period of a queueing system begins when a customer arrives there, finding it empty, and ends when a customer leaves the system letting it empty. During the busy period there is always at least one customer in the system. Therefore in a queueing system there is a sequence of idle and busy periods. For these systems with infinite servers the busy period length distribution is difficult to derive, except for a few exceptions. But we present formulas that allow the calculation of some of the busy period length parameters for the M|G|∞ queueing system. These results can be applied in logistics. For instance to the failures that occur in the operation of a fleet of aircraft, of shipping or of trucking. The customers are the failures. And its service time is the time that goes from the instant at which they occur till the one at which they are completely repaired. Here a busy period is a period in which there is at least one failure waiting for reparation or being repaired. The formulas referred allow the determination of measures of the system performance

The modified peakedness as a M|G|∞ busy cycle distribution characterizing parameter

It is exposed that a parameter, then called ߠ,similar to the parameter ߟ proposed in (1) to characterize the ܯ|ܩ|∞ queue busy period distribution, that is a modification of the peakedness proposed in (2) is also useful to characterize the M/G/∞ queue busy cycle distribution

Networks of Networks: The Last Frontier of Complexity-A Book Review

Along this work the book "Networks of Networks: The Last Frontier of Complexity", 978-3-319-03517-8 published in Springer Series “Understanding Complex Systems” is reviewed. This book theme importance is evident and enormous since it deals with something essential to nowadays everybody’s life, the “Critical Infrastructures”. In the various chapters these devices are studied, exemplified and modelled in order to find the tools to solve their problems of governing, managing, maintenance, security and preservation (with special attention to natural catastrophes problems). It is emphasized that their performance and effectiveness depend not only on the reliable physical components but also on the human behavior understanding at individual and collective levels

M|G|∞ system parameters for a particular collection of service time distributions

In this paper we present the problems that arise when we compute the moments of service time distributions, for which the M|G|∞ busy period and busy cycle become very easy to study. We show how to overcome them. We also compute the busy cycle renewal function and the “peak” and the “modified peak” for the M|G|∞ busy period and busy cycle in the case of those service time distributions