Modeling and Analysis in Support of Decision Making for Technological Investment
Engineering design, resource allocation, military operations, and investment strategies share a major common trait, which is, to a large extent, independent of their different origins, specific features, and intended goals. The unifying trait is the fact that, in any of these endeavors, one has to make reasonable choices, at multiple levels of decision making, among various possible and sometimes competing prospective solutions to an important and consequential practical problem. While the specifics of the problem depend on application, context, additional constraints, etc., the ultimate--albeit imprecise--goal in all these activities is to ''optimize performance,'' which is to have maximal success/profit/return with minimal time/effort/investment. In general, the underlying system is ruled by complex and often unknown dynamics, and affected by various uncertainties, which are unknown as well; on the other hand, there are numerous levels of decision making, which result in a hierarchical structure in the decision process (tree) that is both asynchronous and non-deterministic. Usually, indifferent of the specific application, as one lowers the level of decision making, alternatives depend on fewer independent variables and models become more detailed and physics/engineering based. On the contrary, at higher levels, various components aggregate and decision making is based more on fuzzier criteria instead of readily quantifiable physics/engineering details. Moreover, decisions are strongly influenced by the educational and personal biases of the people who take them. In some instances, this may blur, if not totally obfuscate objective comparisons between various options. Therefore, a crucial point in decision-making is properly understanding and quantifying the tradeoffs, including all their future relevant consequences. Since the interaction between various choices is an intricate nonlinear process, the focus shifts from the dynamics itself to the overall performance and affordability. This is not unreasonable, since oftentimes major upgrades on some components have little impact, while minor upgrades of other components turn out to be critical. To illustrate the approach, we assume that one deals with only two levels. At the lower level, physical/engineering processes are described by continuous and/or discrete, analytic and/or computer models. These models are supposed to be deterministic (e.g. dynamics as ruled by well established physical laws), but their outcome may depend in an unpredictable way on: (i) small nonlinearities unaccounted for in the model development and/or (ii) factors that--at the specific level of modelization--may be treated as stochastic terms (weather conditions, human factors, political circumstances, fluctuations in the quality of carburant, wear and tear, etc.) To this extent, the outcomes of the model processes may be considered stochastic variables/fields with a certain probability distribution function (PDF). Upon many realizations of the model, one can get reliable information about the essential features of this PDF.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- US Department of Energy (US)
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 814390
- Report Number(s):
- ORNL/TM-2003/116; TRN: US200317%%200
- Resource Relation:
- Other Information: PBD: 11 Jun 2003
- Country of Publication:
- United States
- Language:
- English
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