Spreadsheet Modeling & Decision AnalysisDecision AnalysisIntroduction to Decision AnalysisDeciding Between Job OffersGood Decisions vs. Good OutcomesCharacteristics of Decision ProblemsAn Example: Magnolia InnsThe Decision AlternativesThe Possible States of NatureConstructing a Payoff MatrixDecision RulesThe Maximax Decision RuleThe Maximin Decision RuleThe Minimax Regret Decision RuleAnomalies with the Minimax Regret RuleAdding an AlternativeProbabilistic MethodsExpected Monetary ValueEMV CautionExpected Regret or Opportunity LossThe Expected Value of Perfect InformationA Decision Tree for Magnolia InnsRolling Back A Decision TreeAlternate Decision TreeUsing TreePlanAbout TreePlanMulti-stage Decision ProblemsMulti-Stage Decision Example: COM-TECHCOM-TECH (continued)Risk ProfilesAnalyzing Risk in a Decision TreeOther Risk Analysis TechniquesUsing Sample Information in Decision MakingExample: Colonial MotorsIncluding Sample InformationThe Expected Value of Sample InformationComputing Conditional ProbabilitiesComputing Conditional Probabilities (cont’d)Bayes’s TheoremUtility TheoryCommon Utility FunctionsConstructing Utility FunctionsConstructing Utility Functions (cont’d)CommentsUsing Utilities to Make DecisionsThe Exponential Utility FunctionIncorporating Utilities in TreePlanMulticriteria Decision MakingThe Multicriteria Scoring ModelThe Analytic Hierarchy Process (AHP)Pairwise ComparisonsNormalization & ScoringConsistencyConsistency (cont’d)Obtaining Remaining Scores & WeightsEnd of Chapter 15Spreadsheet Modeling & Decision AnalysisA Practical Introduction to Management Science 5th editionCliff T. RagsdaleDecision AnalysisChapter 15Introduction to Decision AnalysisModels help managers gain insight and understanding, but they can’t make decisions.Decision making often remains a difficult task due to:–Uncertainty regarding the future–Conflicting values or objectivesConsider the following example...Deciding Between Job OffersCompany A–In a new industry that could boom or bust.–Low starting salary, but could increase rapidly.–Located near friends, family and favorite sports team.Company B–Established firm with financial strength and commitment to employees.–Higher starting salary but slower advancement opportunity.–Distant location, offering few cultural or sporting activities.Which job would you take?Good Decisions vs. Good OutcomesA structured approach to decision making can help us make good decisions, but can’t guarantee good outcomes.Good decisions sometimes result in bad outcomes.Characteristics of Decision ProblemsAlternatives - different courses of action intended to solve a problem.–Work for company A –Work for company B–Reject both offers and keep lookingCriteria - factors that are important to the decision maker and influenced by the alternatives.–Salary –Career potential–LocationStates of Nature - future events not under the decision makers control.–Company A grows–Company A goes bust–etcAn Example: Magnolia InnsHartsfield International Airport in Atlanta, Georgia, is one of the busiest airports in the world.It has expanded many times to handle increasing air traffic. Commercial development around the airport prevents it from building more runways to handle future air traffic.Plans are being made to build another airport outside the city limits. Two possible locations for the new airport have been identified, but a final decision will not be made for a year.The Magnolia Inns hotel chain intends to build a new facility near the new airport once its site is determined.Land values around both possible sites for the new airport are increasing as investors speculate that property values will increase greatly in the vicinity of the new airport. See data in file Fig15-1.xlsThe Decision Alternatives1) Buy the parcel of land at location A.2) Buy the parcel of land at location B.3) Buy both parcels.4) Buy nothing.The Possible States of Nature1) The new airport is built at location A.2) The new airport is built at location B.Constructing a Payoff MatrixSee file Fig15-1.xlsDecision RulesIf the future state of nature (airport location) were known, it would be easy to make a decision.Failing this, a variety of nonprobabilistic decision rules can be applied to this problem:–Maximax–Maximin–Minimax regretNo decision rule is always best and each has its own weaknesses.The Maximax Decision RuleIdentify the maximum payoff for each alternative.Choose the alternative with the largest maximum payoff.See file Fig15-1.xlsWeakness –Consider the following payoff matrix State of NatureDecision 1 2 MAXA 30 -10000 30 <--maximumB 29 29 29The Maximin Decision RuleIdentify the minimum payoff for each alternative.Choose the alternative with the largest minimum payoff.See file Fig15-1.xlsWeakness –Consider the following payoff matrix State of NatureDecision 1 2 MINA 1000 28 28B 29 29 29 <--maximumThe Minimax Regret Decision RuleCompute the possible regret for each alternative under each state of nature.Identify the maximum possible regret for each alternative.Choose the alternative with the smallest maximum regret.See file Fig15-1.xlsAnomalies with the Minimax Regret RuleConsider the following payoff matrix State of NatureDecision 1 2 A 9 2 B 4 6 State of NatureDecision 1 2 MAXA 0 4 4 <--minimumB 5 0 5The regret matrix is:Note that we prefer A to B.Now let’s add an alternative...Adding an AlternativeConsider the following payoff matrixThe regret matrix is: State of NatureDecision 1 2 A 9 2 B 4 6C 3 9 State of NatureDecision 1 2 MAXA 0 7 7B 5 3 5 <--minimumC 6 0 6Now we prefer B to A???Probabilistic MethodsAt times, states of nature can be assigned probabilities that represent their likelihood of occurrence. For decision problems that occur more than once, we can often estimate these probabilities from historical data. Other decision problems (such as the Magnolia Inns problem) represent one-time decisions where historical data for estimating probabilities don’t exist. In these cases, subjective probabilities are often assigned based on interviews with one or more domain experts. Interviewing techniques exist for soliciting probability estimates that are reasonably accurate and free of the unconscious biases that may impact an expert’s