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Decision Modeling

Decision Modeling. Introduction to Modeling. Modeling in Decision-Making. Quantitative approach Put numbers on inputs Determine (assume) mathematical relationship between decisions and outcomes Put numbers on decisions and outcomes Commonly uses spreadsheets Known interface

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Decision Modeling

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  1. Decision Modeling Introduction to Modeling

  2. Modeling in Decision-Making • Quantitative approach • Put numbers on inputs • Determine (assume) mathematical relationship between decisions and outcomes • Put numbers on decisions and outcomes • Commonly uses spreadsheets • Known interface • Convenient tool for manipulating data • Provides insight about decision problem • Process of quantifying inputs and relationships • Sensitivity analysis • Improves managerial intuition about decision at hand

  3. Herbert Simon’s Model • Problem Definition • Alternative Generation • Choice among Alternatives • Implementation • Evaluation of Decision Iterative process:If no alternative produces a satisfactory result, re-examine problem definition and generate more alternativesEvaluation of decision facilitates learning (generation of experience)

  4. be explicit about your objectives. 1. identify and record the types of decisions that influence those objectives. 2. identify and record interactions and trade-offs among those decisions. 3. think carefully about which variables to include. 4. consider what data are pertinent and their interactions. 5. recognize constraints or limitations on the values. 6. The Modeling Process Decision Support Models force you to • Models allow us to use the analytical power of spreadsheets hand in hand with the data storage and computational speed of computers. • Test fictitious situations (scenario analysis) • Study sensitivity of solution to different factors (managerial focus) • Prepare responses to potential challenges and opportunities (preparedness)

  5. are models in which all relevant data are assumed to be known with certainty. can handle variety situations with many decisions and constraints. are very useful when there are few uncontrolled model inputs that are uncertain. can easily incorporate constraints on variables. Deterministic v. Probabilistic Models Deterministic Models

  6. are models in which some inputs to the model are not known with certainty. uncertainty is incorporated via probabilities on these “random” variables. very useful when there are only a few uncertain model inputs and few or no constraints. often used for strategic decision making involving an organization’s relationship to its environment. Deterministic v. Probabilistic Models Probabilistic (Stochastic) Models

  7. Digression on Objectives • Single objective • Deterministic problems • Max revenue, profit • Min cost • Stochastic problems • Maximin payoff • Max expected profit, NPV • Min expected cost, regret • Multiple objectives? • Agency issues • Whose profit/NPV/cost?

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