A quantile-based approach to system selection

We propose a quantile-based ranking and selection (R&S) procedure for comparing a finite set of stochastic systems via simulation. Our R&S procedure uses a quantile set of the simulated probability distribution of a performance characteristic of interest that best represents the most appropriate selection criterion as the basis for comparison. Since this quantile set may represent either the downside risk, upside risk, or central tendency of the performance characteristic, the proposed approach is more flexible than the traditional mean-based approach to R&S. We first present a procedure that selects the best system from among K systems, and then we modified that procedure for the case where K − 1 systems are compared against a standard system. We present a set of experiments to highlight the flexibility of the proposed procedures.     

Stochastic dominance based comparison for system selection

We present two complementing selection procedures for comparing simulated systems based on the stochastic dominance relationship of a performance metric of interest. The decision maker specifies an output quantile set representing a section of the distribution of the metric, e.g., downside or upside risks or central tendencies, as the basis for comparison. The first procedure compares systems over the quantile set of interest by a first-order stochastic dominance criterion. The systems that are deemed nondominant in the first procedure could be compared by a weaker almost first-order stochastic dominance criterion in the second procedure. Numerical examples illustrate the capabilities of the proposed procedures.     

Use of Intervals and Possibility Distributions in Economic Analysis

Traditional approaches to capital budgeting are based on the premise that probability theory is necessary and sufficient to deal with the uncertainty and imprecision which underlie the estimates of required parameters. This paper argues that, in many circumstances, this premise is invalid since the principal sources of uncertainty are often non-random in nature and relate to the fuzziness rather than the frequency of data. To capture and quantify correctly the underlying uncertainty present in non-statistical situations, this paper suggests two alternative representations: interval analysis and possibility distributions. The use of these representations in economic analysis is discussed, and their application is illustrated through numerical examples.     

Model management systems: A survey

This paper provides a survey of model management literature within the mathematical modeling domain. The first part of the survey is a review and a summary of the literature. After giving some basic definitions of modeling, modeling life cycle, and model management, two representative algebraic modeling languages followed by three approaches to modeling are introduced. These approaches are database, graph-based, and knowledge-based. The discussion is followed by a review of two specialized model management systems. The second part of the survey is a categorization of various modeling systems based on the modeling functions they provide and some of their features. These functions include life cycle support and model base administration. The degree of model independence provided by model management systems and the implemented environment systems is also summarized. The last part of the paper provides directions for future research.     

ChemInform Abstract: The Monoclinic Form of Trilithium Dichromium(III) Tris(orthophosphate).

Monoclinic Li₃Cr₂(PO₄)₃ is prepared by melting equimolar amounts of Cr and P in a reactive LiCl/CsCl (4:1) flux (reactant/flux ratio of 1:3, evacuated silica tube, 1123 K, 2 d, 4 K/h cooling rate).