MICROWAVE: A GENERIC FRAMEWORK FOR MICRO SIMULATIONBASED EX ANTE POLICY EVALUATION

This paper presents the MicroWave approach that has been developed to improve the process of modeling in the context of micro simulation. It leads to a more efficient model development, better quality of models and their output and improvement in knowledge management. A conceptual framework has been developed and translated into a hierarchical structure of GAMS program code. Besides, several software applications and other tools have been developed for support. These products are presented and some examples illustrate how MicroWave can be applied. MicroWave is especially useful in interdisciplinary research in which different persons are involved in the modeling process and when different models have to be combined.Research Methods/ Statistical Methods,

Modelling performance in a Balanced Scorecard : findings from a case study

Cet article s'intéresse à la mise en place de l'approche dite du "Balanced Scorecards" dans des unités opérationnelles plutôt qu'au niveau d'une direction générale. Il s'appuie sur une étude de cas. On propose de traiter les questions relatives à la coordination, à la fixation des objectifs et au contrôle en s'appuyant sur une méthodologie originale pour construire le modèle d'interaction entre les différentes entités de l'organisation. Cette méthodologie fait une part importante à l'apprentissage organisationnel permettant ainsi une compréhension mutuelle des degrés de liberté individuels et une meilleure observation réciproque. Cette approche "horizontale" est mieux adaptée à ce type de contexte que l'approche "verticale" plus traditionnelle du BCS.Pilotage;Tableaux de bord;Incitations;Apprentissage organisationnel

Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. We present a novel, scalable, and efficient technique for verifying properties of deep neural networks (or providing counter-examples). The technique is based on the simplex method, extended to handle the non-convex Rectified Linear Unit (ReLU) activation function, which is a crucial ingredient in many modern neural networks. The verification procedure tackles neural networks as a whole, without making any simplifying assumptions. We evaluated our technique on a prototype deep neural network implementation of the next-generation airborne collision avoidance system for unmanned aircraft (ACAS Xu). Results show that our technique can successfully prove properties of networks that are an order of magnitude larger than the largest networks verified using existing methods.Comment: This is the extended version of a paper with the same title that appeared at CAV 201

Goal-conflict detection based on temporal satisfiability checking

Goal-oriented requirements engineering approaches propose capturing how a system should behave through the speci ca- tion of high-level goals, from which requirements can then be systematically derived. Goals may however admit subtle situations that make them diverge, i.e., not be satis able as a whole under speci c circumstances feasible within the domain, called boundary conditions . While previous work al- lows one to identify boundary conditions for con icting goals written in LTL, it does so through a pattern-based approach, that supports a limited set of patterns, and only produces pre-determined formulations of boundary conditions. We present a novel automated approach to compute bound- ary conditions for general classes of con icting goals expressed in LTL, using a tableaux-based LTL satis ability procedure. A tableau for an LTL formula is a nite representation of all its satisfying models, which we process to produce boundary conditions that violate the formula, indicating divergence situations. We show that our technique can automatically produce boundary conditions that are more general than those obtainable through existing previous pattern-based approaches, and can also generate boundary conditions for goals that are not captured by these patterns

A Fourier interpolation method for numerical solution of FBSDEs: Global convergence, stability, and higher order discretizations

The implementation of the convolution method for the numerical solution of backward stochastic differential equations (BSDEs) introduced in [19] uses a uniform space grid. Locally, this approach produces a truncation error, a space discretization error, and an additional extrapolation error. Even if the extrapolation error is convergent in time, the resulting absolute error may be high at the boundaries of the uniform space grid. In order to solve this problem, we propose a tree-like grid for the space discretization which suppresses the extrapolation error leading to a globally convergent numerical solution for the (F)BSDE. On this alternative grid the conditional expectations involved in the BSDE time discretization are computed using Fourier analysis and the fast Fourier transform (FFT) algorithm as in the initial implementation. The method is then extended to higher-order time discretizations of FBSDEs. Numerical results demonstrating convergence are also presented.Comment: 28 pages, 8 figures; Previously titled 'Global convergence and stability of a convolution method for numerical solution of BSDEs' (1410.8595v1

MULTIPLE OPTIMAL SOLUTIONS IN QUADRATIC PROGRAMMING MODELS

The problem of determining whether quadratic programming models possess either unique or multiple optimal solutions is important for empirical analyses which use a mathematical programming framework. Policy recommendations which disregard multiple optimal solutions (where they exist) are potentially incorrect and less than efficient. This paper proposes a strategy and the associated algorithm for finding all optimal solutions to any positive semidefinite linear complementarity problem. One of the main results is that the set of complementary solutions is convex. Although not obvious, this proposition is analogous to the well-known result in linear programming which states that any convex combination of optimal solutions is itself optimal.Research Methods/ Statistical Methods,

DISCRETE STOCHASTIC SEQUENTIAL PROGRAMMING: A PRIMER

The purpose of this paper is to present an overview of discrete stochastic sequential programming and to illustrate the technique through a numerical example. The application of the technique to empirical problems involving decision making will be briefly discussed and an empirical application will be summarized.Research Methods/ Statistical Methods,