Quality

qualitative analysis Chapter 19 Decision Analysis

LEARNING OBJECTIVES

Chapter 19 describes how to use decision analysis to improve management decisions, thereby enabling you to:

1. Learn about decision making under certainty, under uncertainty, and under risk. 2. Learn several strategies for decision-making under uncertainty, including expected payoff, expected opportunity loss, maximin, maximax, and minimax regret. 3. Learn how to construct and analyze decision trees. 4. Understand aspects of utility theory. 5. Learn how to revise probabilities with sample information.

CHAPTER OUTLINE

19.1 The Decision Table and Decision Making Under Certainty Decision Table Decision-Making Under Certainty

19.2 Decision Making Under Uncertainty Maximax Criterion Maximin Criterion Hurwicz Criterion Minimax Regret

19.3 Decision Making Under Risk Decision Trees Expected Monetary Value (EMV) Expected Value of Perfect Information Utility

19.4 Revising Probabilities in Light of Sample Information Expected Value of Sample Information

KEY TERMS

Decision Alternatives Hurwicz Criterion Decision Analysis Maximax Criterion Decision Making Under Certainty Maximin Criterion Decision Making Under Risk Minimax Regret Decision Making Under Uncertainty Opportunity Loss Table Decision Table Payoffs Decision Trees Payoff Table EMV'er Risk-Avoider Expected Monetary Value (EMV) Risk-Taker Expected Value of Perfect Information States of Nature Expected Value of Sample Information Utility

STUDY QUESTIONS

1. In decision analysis, decision-making scenarios are divided into three