An Assessment of Markov Renewal Models in Forecasting International Affairs

Abstract

A major difficulty for designers of systems to forecast international affairs has been to allow the use of both observable data and subjective estimates. Under DARPA sponsorship, a novel approach has been developed based upon a Bayesian stochastic model. This approach has been developed and demonstrated in a preliminary fashion by other DARPA contractors. The present report provides a brief appraisal of the approach, offers preliminary suggestions for modifications, and suggests candidate areas of application. The treatment of time and nonstationary is explored in some detail, and modifications are suggested which could lessen the need to reassess model parameters each time a change occurs in an underlying political process.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1981
Accession Number
ADA097295

Entities

People

  • Anthony N. S. Freeling
  • David A. Seaver
  • James O. Chinnis Jr.

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Bayes Theorem
  • Case Studies
  • Computer Programs
  • Databases
  • Delphi Method
  • Field Tests
  • Governments
  • Markov Chains
  • Markov Models
  • Markov Processes
  • Models
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Stochastic Processes
  • Test And Evaluation
  • Transitions

Readers

  • Computational Modeling and Simulation
  • Statistical inference.
  • Strategic Security Studies

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference