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Model-Based Decision Support System for Evaluating Uncertainty and Complexity

Develop and use models in DSS to analyze problem situations under uncertainty. Utilize deterministic, probabilistic, and statistical models for decision analysis and sensitivity analyses. Evaluate alternatives and optimize goals. Incorporate sensitivity and uncertainty measures for informed decision-making.

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Model-Based Decision Support System for Evaluating Uncertainty and Complexity

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  1. Model Based DSSCreating Information Under Conditions of Uncertainty and Complexity Information Technology for Enterprise Strategic Systems DATABASE MODELBASE External Data External Models DBMS MBMS DATA WAREHOUSING ON LINE ANALYTICAL PROCESSING Interface GUI, Intelligent Objects, Voice, GroupWare, etc.

  2. Developing and Using Models Processes for Analyzing Problem/Opportunities Situations Model Representation Deterministic Analysis Probabilistic Analysis Evaluation of Alternatives Decision Analysis Models Statistical, OR/MS, Financial Models Sensitivity Analyses (1-way, n-way) Goal Seeking and Optimization Using Probabilities (Decision Analyses) Using Probability Distributions (Simulation) Using Heuristics, Consensus Methods Goal Maximization, Internal Consistency

  3. Evaluating Uncertainty Using Models Deterministic Analyses for answering “what-if” questions Model Outcome Controllable Intermediate Uncontrollable Measuring the sensitivity (changes) in the outcome due to changes in the input variable One-way Sensitivity Analysis • Compares one-way sensitivity analysis • Most sensitive variable is at top and descends in sensitivity • Shows sensitivity range • Produces a tornado appearance INPUT Variables Profit (Outcome) n-way Sensitivity Analysis • Changes in several inputs • Helps evaluate scenarios • Scenario manger in Excel

  4. Evaluating Uncertainty Using Models Probability Analyses for answering “what-if” questions Model Outcome Controllable Intermediate Uncontrollable Simple Linear Regression (One -Way Probabilistic) Regression Analyses Model uncertain relationships using data Multiple Linear Regression (N -Way Probabilistic) Monte-Carlo Simulation Model uncertain variables using probability distributions

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