Scenario Analysis: Applications and Extensions

Abstract

The primary aim of this work was to enhance the method of scenario analysis for decision-making under uncertainty. An effect of such enhancement could be to help the Army make better decisions in situations involving resource allocation under uncertainty, particularly those involving force and weapon mix issues. Scenario analysis models employ a finite probability distribution describing different scenarios (that is, possible states of the decision environment), and they then compute solutions that optimize against these scenarios according to some appropriate decision criterion. For example, one might seek a decision that provided maximum expected performance on some measure of effectiveness, subject to various operational constraints (which can be different in different scenarios), or one might look for a decision providing minimum variance in performance across scenarios, subject to a floor on expected performance. Various supporting studies were also carried on to improve the underlying optimization methodology that supports the scenario analysis paradigm. These included work in stochastic optimization and in nonsmooth optimization, as well as in Bayesian analysis for estimating value in military system testing.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1993
Accession Number
ADA275452

Entities

People

  • Stephen M. Robinson

Organizations

  • University of Wisconsin–Madison

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Computational Science
  • Computer Simulations
  • Engineering
  • Industrial Engineering
  • Markov Models
  • Mathematical Models
  • Mathematical Programming
  • Military Research
  • Operations Research
  • Optimization
  • Probability
  • Probability Distributions
  • Scientists
  • Simulations
  • Uncertainty
  • Weapon Systems

Readers

  • Military Training and Readiness Simulation
  • Operations Research
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms