In this paper, we develop some stochastic dominancetheorems for the location and scale family and linear combinations ofrandom variables and for risk lovers as well as risk averters thatextend results in Hadar and Russell (1971) and Tesfatsion (1976). Theresults are discussed and applied to decision-making.

This paper presents an algorithm which prevents a simulation user from choosing a simulation length. This choice is always tricky and often leads to CPU-time waste, not to mention user-time waste.Too often, simulation users forget to compute confidence intervals: they only guess a simulation length and ignore the confidence on the simulation results. Those who do compute them generally try severallengths (and thus run several simulations) so as to obtain small enough confidence intervals.The algorithm aims at optimizing this length choice by running onlyone simulation and by stopping it nearly as soon as possible, i.e. when some predefined relative confidence intervals on each of the performance criteria are reached. For this purpose, the confidence intervals are periodically computed, at run-time, with the batch mean method. According to these intermediate results and to estimatorsproperties, a mobile simulation length is (also periodically) predicted.The algorithm automatically determines batch size and batches number. This process goes on until all confidence intervals are smaller thanthe predefined thresholds.This algorithm is implemented in MIMESIS, a computer architectureperformance evaluation tool.

The relations between automatic clustering methods and inferentiel statistical models have mostely been studied when the data involves only one set. We propose to study these relations in the caseof data involving two sets. We shall look at cross clustering methods assuggested by Govaert [6]; we show that these methods, like the simple clusteringmethods, can be considered as a clustering approach of a mixture model. Weintroduce the notion of crossed mixture from a concret example and define thenotions of likelihood and associated clustered likelihood. Then, we study therelations which exist between the crossed mixture models and simple models and weshow that these relations are completely similar to those which exist betweenthe crossed clustering methods and simple clustering methods.

This paper presents an application of Multiple Attribute Utility Theory onstrategic choices concerning energy transportation. The environmental assessmentof a network reinforcement strategy is emphasized. Our assessment bringsabout to consider multidimensional variables in MCDM. However, Multi-AttributedUtility Theory (MAUT) cannot, as a practical matter, manage such variables. We therefore work out a methodology to transform multidimensional variablesinto unidimensional ones. We apply it then to a pratical case. From the application, we draw some conclusions on Multi-Attributed Utility Theoryand on its interest for strategic choices dealing with environmental consequences.

Our concern here, is the characterization of dissimilarity indexes defined over finite sets, whose spatial representation is spherical. Consequently, we propose a methodology (NormedMultiDimensional Scaling) to determine the spherical euclidean representation of a set ofitems best accounting for the initial dissimilarity between items. Thismethodology has the advantage of being graphically readable on individual qualitiesof projection like the normed PCA, of which it constitutes a generalization. Moreover, it avoids the arbitrary character of spherical encoding which the use of similitude functions currently used in MDS, implies.