CHAPTER 19 Decision Theory to accompany Introduction to business statistics fourth edition by ronald M. Weiers Presentation by Priscilla Chaffe-Stengel Donald N. stengel o 2002 The Wadsworth Group
CHAPTER 19: Decision Theory to accompany Introduction to Business Statistics fourth edition, by Ronald M. Weiers Presentation by Priscilla Chaffe-Stengel Donald N. Stengel © 2002 The Wadsworth Group
l Chapter 19-Learning objectives Express a decision situation in terms of decision alternatives, states of nature, and payoffs Differentiate between non-Bayesian and Bayesian ecision criteria Determine the expected payoff for a decision alternative Calculate and interpret the expected value of perfect information Express and analyze the decision situation in terms of opportunity loss and expected opportunity loss Apply incremental analysis to inventory-level decisions o 2002 The Wadsworth Group
Chapter 19 - Learning Objectives • Express a decision situation in terms of decision alternatives, states of nature, and payoffs. • Differentiate between non-Bayesian and Bayesian decision criteria. • Determine the expected payoff for a decision alternative. • Calculate and interpret the expected value of perfect information. • Express and analyze the decision situation in terms of opportunity loss and expected opportunity loss. • Apply incremental analysis to inventory-level decisions. © 2002 The Wadsworth Group
l Chapter 19- Key Terms Levels of doubt Maximin criteria Risk Maximax criteria Uncertainty Minimax regret gnorance Expected value of · Decision situation perfect information Decision alternatives States of nature Expected opportunity Probabilities Incremental analysis Expected payoff o 2002 The Wadsworth Group
Chapter 19 - Key Terms • Levels of doubt – Risk – Uncertainty – Ignorance • Decision situation – Decision alternatives – States of nature – Probabilities – Expected payoff • Maximin criteria • Maximax criteria • Minimax regret • Expected value of perfect information • Expected opportunity loss • Incremental analysis © 2002 The Wadsworth Group
l The Decision situation The decision maker can control which decision alternative(row)is selected but cannot determine which state of nature (column will occur The decision alternative is selected prior to knowing the state of nature o 2002 The Wadsworth Group
The Decision Situation • The decision maker can control which decision alternative (row) is selected but cannot determine which state of nature (column) will occur. • The decision alternative is selected prior to knowing the state of nature. © 2002 The Wadsworth Group
I An example Problem 19.34: a ski resort operator must decide before the winter season whether he will lease a snow making machine. If he has no machine, he will make $20,000 if the winter is mild, $30,000 if it is typical, and $50,000 if the winter is severe. If he decides to lease the machine, his profits for these conditions will be $30,000, $35,000, and $40,000, respectively. The probability of a mild winter is 0.3, with a 0.5 chance of a typical winter and a 0.2 chance of a severe winter. If the operater wants to maximize his expected profit should he lease the machine? what is the most he should be willing to pay for a perfect forecast? o 2002 The Wadsworth Group
An Example • Problem 19.34: A ski resort operator must decide before the winter season whether he will lease a snowmaking machine. If he has no machine, he will make $20,000 if the winter is mild, $30,000 if it is typical, and $50,000 if the winter is severe. If he decides to lease the machine, his profits for these conditions will be $30,000, $35,000, and $40,000, respectively. The probability of a mild winter is 0.3, with a 0.5 chance of a typical winter and a 0.2 chance of a severe winter. If the operater wants to maximize his expected profit, should he lease the machine? What is the most he should be willing to pay for a perfect forecast? © 2002 The Wadsworth Group