ESTIMATING COST UNCERTAINTY USING MONTE CARLO TECHNIQUES

Abstract

Suggested in this memorandum is a technique for expressing cost estimates of future systems as probability distributions to reflect the uncertainty of the estimate. The impact of this information is shown to be relevant to the decision-making process. For the purpose of this study, the relationship between the sources of uncertainty and system cost estimates is depicted as an input-output model. Within this framework, a procedure was developed to estimate probability distributions for each of the input uncertainties. From the input distributions, a Monte Carlo procedure is used to generate a series of system cost estimates. A frequency distribution and common statistical measures are then prepared from the set of output estimates to ascertain the nature and magnitude of the system cost unertainty. To illustrate the proposed technique, a case study involving the cost estimate of a hypothetical aircraft system with air-to-surface missiles is presented.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1966
Accession Number
AD0629082

Entities

People

  • Paul F. Dienemann

Organizations

  • RAND Corporation

Tags

Communities of Interest

  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Administrative Personnel
  • Air Force
  • Aircrafts
  • Case Studies
  • Computers
  • Cost Analysis
  • Cost Estimates
  • Cost Models
  • Data Science
  • Databases
  • Information Science
  • Maintenance Personnel
  • Monte Carlo Method
  • Probability Distributions
  • United States
  • Weapon Systems
  • Weapons

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Statistical inference.
  • Systems Analysis and Design