1. Introduction Dr. Yan Liu Department of Biomedical, Industrial & Human Factors Engineering Wright State University

2. 2 Making Hard Decisions Decision Making  Cognitive process of reaching a decision We occasionally need to make hard decisions  What is the HARDEST decision that you have ever had to make? Decision Domains  Personal domain e.g. which career to pursue, where to live, etc  Business domain e.g. which product to invest, where to locate  Government domain e.g. how to cope with social problems, how to deal with international conflicts

3. 3 Key Terms and Concepts Decision  A conscious irrevocable allocation of resources with the purpose of achieving a desired objective Objective  Something specific that the decision maker wants to achieve e.g. Maximize payoffs of investment in stocks Uncertainty  Something that is unknown or not perfectly known e.g. Performance of stock market during the next five years

4. 4 Key Terms and Concepts (Cont.) Outcomes (States of Nature)  The possible things that can happen in the resolution of an uncertain event e.g. The stock market is stable during the next five years Values  Things that matter to the decision maker e.g. payoffs of investment  Decision maker should use values to compare one alterative versus another Decision Context  The particular decision situation which determines what objectives are considered e.g. personal financial status, economic status of the nation  Decision context and objectives go hand in hand

5. 5 Requisite Decision Models A model is considered requisite if contains everything that is essential for solving the problem and only those that are essential It captures the essence of a decision modeling process It requires a fully development of the decision maker’s thoughts about the problem, beliefs regarding uncertainty, and preferences

6. 6 Why Are Decisions Hard Structural Reasons  uncertainty, trade-offs, complexity Emotional Reasons  anxiety, pressure Organizational Reasons  different perspectives, consensus

7. 7 Why Study Decision Analysis Decision Analysis  A prescriptive approach designed for normally intelligent people who want to think hard and systematically about some important real problems  A tool to offer guidance to normal people making hard decisions based on fundamental principles and knowledge about human frailties in judgment and decision making Studying Decision Analysis Leads to Better Decisions  Performance of decision making is better on average  Decisions are consistent The same decision will be made given the same information  No surprises due to thorough study of the problem

8. 8 Good Decisions What Is a Good Decision  Emerges as a result of careful consideration of the available information and thorough deliberation about the goals and possible outcomes Good Decision  Good Outcome  An outcome can be good because of good luck  Decision analysis cannot improve our luck, but it can certainly help us understand our problems better and thus make better decisions in general

9. 9 Origins of Decision Analysis Bernoulli (1738)  Proposed the expected utility model with a logarithmic utility function Explain deviations from the expected value model Bayes (1763)  Proposed Bayes Theorem The revision of probability based on observations von Neumann and Morgenstern (1947)  Theory of Games and Economic Behavior, in which the expected utility (EU) model was proposed People choose among bets that maximize expected utility Savage (1954)  The Foundations of Statistics proposed subjective expected utility model (SEU) Combine the ideas of utility theory and subjective probability

10. 10 Origins of Decision Analysis (Cont.) Schlaifer’s (1959)  Probability and Statistics for Business Decisions espoused Bayesian and decision analytic principles for business decisions Raiffa and Schlaifer’s (1961)  Applied Statistical Decision Theory provided a detailed mathematical treatment of decision analysis, with focus on Bayesian Statistical Models Pratt (1964)  Article “Risk Aversion in the Small and in the Large” made significant contributions to the theory of utility for money, formalizing a measure of risk aversion Howard (1966)  First coined the term “decision analysis” Raiffa (1968)  Decision Analysis established the decision analysis as a methodology in real applications

11. 11 Process of Decision Analysis Objectives e.g. Min. cost, Max. profit Alternatives e.g. Invest or not invest, Choose A or B Decompose and Model “Divide & Conquer” modeling techniques, mathematical and statistical tools Sensitivity Whether a slight change in one or more aspects of the model would affect the optimal decision Such a process provides not only a structured way of thinking about decisions but also a structure in which the decision maker can develop beliefs and feelings (Figure 1.1 in the textbook)

12. 12 Hartsfield International Airport in Atlanta, Georgia, is one of the busiest airports in the world. Commercial development around the airport prevents it from building additional runways to handle the future air traffic demands. Therefore, plans are being developed to build another airport outside the city limits. Two possible locations (A and B) for the new airport have been identified, but a final decision is not expected for another year. The Magnolia Inns hotel chain intends to build a new facility near the new airport. Land values around the two possible sites for the new airport are increasing as investors speculate that property values will increase greatly in the vicinity of the new airport. Hartsfield Airport Example

13. 13 Objective: To maximize the profit Alternatives: Purchase a land at location A Purchase a land at location B Purchase a land at both locations A and B Do nothing and wait till more information is obtained Decomposing and Modeling: Two possible outcomes: i) the airport is built at A, ii) the airport is built at B Information available: the payoffs of all combinations of alternatives and outcomes, the probability of each outcome Develop a decision tree model for the decision

14. 14 Select the best alternative Purchasing a land at A has the highest expected payoff Sensitive analysis The decision is not sensitive to small changes of model parameters Implement the chosen plan of purchasing a land at A