Stochastic Risk vs. Policy Oriented Uncertainties: The Case of the Alpine Crossings

This paper focuses on uncertainties in traffic forecasting. Three major sources of uncertainties are observed for freight demand models. The first one is the model specification itself. We are not interested by it. The second one concerns uncertainties over forecasting hypotheses. A mean to control such uncertainties lies in the introduction of risk in the Costs Benefits Analysis (CBA). Two directions have been taken by this research. The first one is the theoretical framework of CBA under uncertainty mainly developed after Dixit and Pindyck (1994). The second one is more empirical and uses Monte Carlo simulations. Major results of these researches are presented. Then, we apply them to a large transport investment simulation. These tools cannot be used for all kinds of uncertainties. The second part of this paper deals with the third source of uncertainties i. e. policy oriented uncertainties. For them, previous methods are useless. The current Alpine crossings context shows that transport policy is a major determinant of traffics. Furthermore, long term forecasting cannot exclude the possibility of changes in transport policy. This uncertainty should be controlled. It is the role of strategic modeling.risk ; uncertainty ; traffic forecasting ; Monte Carlo simulation ; transport policy ; Strategic models ; Alpine crossings

Family of 2-simplex cognitive tools and their application for decision-making and its justifications

Urgency of application and development of cognitive graphic tools for usage in intelligent systems of data analysis, decision making and its justifications is given. Cognitive graphic tool "2-simplex prism" and examples of its usage are presented. Specificity of program realization of cognitive graphics tools invariant to problem areas is described. Most significant results are given and discussed. Future investigations are connected with usage of new approach to rendering, cross-platform realization, cognitive features improving and expanding of n-simplex family.Comment: 14 pages, 6 figures, conferenc

Prognostic Launch Vehicle Probability of Failure Assessment Methodology for Conceptual Systems Predicated on Human Causal Factors

Lessons learned from past failures of launch vehicle developments and operations were used to create a new method to predict the probability of failure of conceptual systems. Existing methods such as Probabilistic Risk Assessments and Human Risk Assessments were considered but found to be too cumbersome for this type of system-wide application for yet-to-be-flown vehicles. The basis for this methodology were historic databases of past failures, where it was determined that various faulty human-interactions were the predominant root causes of failure rather than deficient component reliabilities evaluated through statistical analysis. This methodology contains an expert scoring part which can be used in either a qualitative or a quantitative mode. The method produces two products: a numerical score of the probability of failure or guidance to program management on critical areas in need of increased focus to improve the probability of success. In order to evaluate the effectiveness of this new method, data from a concluded vehicle program (USAF's Titan IV with the Centaur G-Prime upper stage) was used as a test case. Although the theoretical vs. actual probability of failure was found to be in reasonable agreement (4.46% vs. 6.67% respectively) the underlying sub-root cause scoring had significant disparities attributable to significant organizational changes and acquisitions. Recommendations are made for future applications of this method to ongoing launch vehicle development programs

Modeling good research practices - overview: a report of the ISPOR-SMDM modeling good research practices task force - 1.

Models—mathematical frameworks that facilitate estimation of the consequences of health care decisions—have become essential tools for health technology assessment. Evolution of the methods since the first ISPOR modeling task force reported in 2003 has led to a new task force, jointly convened with the Society for Medical Decision Making, and this series of seven papers presents the updated recommendations for best practices in conceptualizing models; implementing state–transition approaches, discrete event simulations, or dynamic transmission models; dealing with uncertainty; and validating and reporting models transparently. This overview introduces the work of the task force, provides all the recommendations, and discusses some quandaries that require further elucidation. The audience for these papers includes those who build models, stakeholders who utilize their results, and, indeed, anyone concerned with the use of models to support decision making