The stack resource protocol based on real time transactions

Current hard real time (HRT) kernels have their timely behaviour guaranteed at the cost of a rather restrictive use of the available resources. This makes current HRT scheduling techniques inadequate for use in a multimedia environment where one can profit by a better and more flexible use of the resources. It is shown that one can improve the flexibility and efficiency of real time kernels and a method is proposed for precise quality of service schedulability analysis of the stack resource protocol. This protocol is generalised by introducing real time transactions, which makes its use straightforward and efficient. Transactions can be refined to nested critical sections if the smallest estimation of blocking is desired. The method can be used for hard real time systems in general and for multimedia systems in particular

Strictness of Leniency Programs and Cartels of Asymmetric Firms

This paper studies the effects of leniency programs on the behavior of firms participating in illegal cartel agreements.The main contribution of the paper is that we consider asymmetric firms.In general, firms differ in size and operate in several different markets.In our model, they form a cartel in one market only.This asymmetry results in additional costs in case of disclosure of the cartel, which are caused by an asymmetric reduction of the sales in other markets due to a negative reputation effect.This modeling framework can also be applied to the case of international cartels, where firms are subject to different punishment procedures according to the laws of their countries, or in situations where following an application for leniency firms are subject to costs other than the fine itself and where these costs depend on individual characteristics of the firm.Moreover, following the rules of existing Leniency Programs, we analyze the effects of the strictness of the Leniency Programs, which reflects the likelihood of getting complete exemption from the fine even in case many firms self-report simultaneously.Our main results are that, first, leniency programs work better for small (less diversified) companies, in the sense that a lower rate of law enforcement is needed in order to induce self-reporting by less diversified firms.At the same time, big (more diversified) firms are less likely to start a cartel in the first place given the possibility of self-reporting in the future.Second, the more cartelized the economy, the less strict the rules of leniency programs should be.Antitrust Policy;Antitrust Law;Self-reporting;Leniency Programs

Statistical inference for the mean outcome under a possibly non-unique optimal treatment strategy

We consider challenges that arise in the estimation of the mean outcome under an optimal individualized treatment strategy defined as the treatment rule that maximizes the population mean outcome, where the candidate treatment rules are restricted to depend on baseline covariates. We prove a necessary and sufficient condition for the pathwise differentiability of the optimal value, a key condition needed to develop a regular and asymptotically linear (RAL) estimator of the optimal value. The stated condition is slightly more general than the previous condition implied in the literature. We then describe an approach to obtain root-$n$ rate confidence intervals for the optimal value even when the parameter is not pathwise differentiable. We provide conditions under which our estimator is RAL and asymptotically efficient when the mean outcome is pathwise differentiable. We also outline an extension of our approach to a multiple time point problem. All of our results are supported by simulations.Comment: Published at http://dx.doi.org/10.1214/15-AOS1384 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Evaluating the Impact of Treating the Optimal Subgroup

Suppose we have a binary treatment used to influence an outcome. Given data from an observational or controlled study, we wish to determine whether or not there exists some subset of observed covariates in which the treatment is more effective than the standard practice of no treatment. Furthermore, we wish to quantify the improvement in population mean outcome that will be seen if this subgroup receives treatment and the rest of the population remains untreated. We show that this problem is surprisingly challenging given how often it is an (at least implicit) study objective. Blindly applying standard techniques fails to yield any apparent asymptotic results, while using existing techniques to confront the non-regularity does not necessarily help at distributions where there is no treatment effect. Here we describe an approach to estimate the impact of treating the subgroup which benefits from treatment that is valid in a nonparametric model and is able to deal with the case where there is no treatment effect. The approach is a slight modification of an approach that recently appeared in the individualized medicine literature

End-of-Life Inventory Problem with Phase-out Returns

We consider the service parts end-of-life inventory problem of a capital goods manufacturer in the final phase of its life cycle. The final phase starts as soon as the production of parts terminates and continues until the last service contract expires. Final order quantities are considered a popular tactic to sustain service fulfillment obligations and to mitigate the effect of obsolescence. In addition to the final order quantity, other sources to obtain serviceable parts are repairing returned defective items and retrieving parts from phase-out returns. Phase-out returns happen when a customer replaces an old system platform with a next generation one and returns the old product to the original equipment manufacturer (OEM). These returns can well serve the demand for service parts of other customers still using the old generation of the product. In this paper, we study the decision-making complications stemming from phase-out occurrence. We use a finite horizon Markov decision process to characterize the structure of the optimal inventory control policy. We show that the optimal policy consists of a time varying threshold level for item repair. Furthermore, we study the value of phase-out information by extending the results to cases with an uncertain phase-out quantity or an uncertain schedule. Numerical analysis sheds light on the advantages of the optimal policy compared to some heuristic policies.spare parts;end-of-life inventory management;phase-out returns

Equilibrium adjustment of disequilibrium prices

We consider an exchange economy in which price rigidities are present. In the short run the non-numeraire commodities have a exible price level with respect to the numeraire commodity but their relative prices are mutually fixed. In the long run prices are assumed to be completely exible. For a given price level and fixed relative prices, markets can be equilibrated by means of quantity rationing on demand and supply. Keeping markets in equilibrium through rationing, we provide an adjustment process in prices and quantities converging from a trivial equilibrium with complete demand rationing on all non-numeraire markets to a Walrasian equilibrium. Along the path initially all relative prices are kept fixed and the price level is increased. Rationing schemes are adjusted to keep markets in equilibrium. Doing so the process reaches a short run equilibrium with only demand rationing and no rationing on the numeraire and at least one of the other commodities. The process allows for a downward price adjustment of non-rationed non-numeraire commodities and reaches a Walrasian equilibrium in the long run.Equilibrium Theory;market economy;Prices;Disequilibrium Theory;Rationing;economic theory