Maximizing expected utility and decision trees – for

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As explained in this week’s resources, maximization of expected utility involves the following five steps of decision making:

Identifying future conditions, along with the likelihood of the condition being realized
Listing possible alternatives
Estimating the payoff or utility for each alternative under each future condition
Calculating the expected utility
Selecting the best alternative

Decision trees can be used to illustrate all of this information in a graphic manner.

In this Assignment, apply the information from this week’s resources to solve decision problems and complete a Decision Analysis for those problems. Solve problems 5, 10, and 11 on pp. 231–234 of the Stevenson text by creating decision trees, determining expected utilities of the decision alternatives, and offering recommended decisions based on the decision tree analysis.

Approximate length: 4–5 pages

Submit your Decision Analysis for the assigned problems by Day 3.

Stevenson, W. (2014). Operations management (12th ed.). New York, NY: McGraw-Hill/Irwin.

Supplement to Chapter 5, “Decision Theory” (pp. 221–224, pp. 231–234)

Discussion: Examining Decisions

To participate in the Discussion this week, it is important for you to first solve the decision problems in the Assignment. This will ensure that you know how to set up and solve a decision tree representation of a decision problem. For this Discussion, you will select a decision that you have made or that you need to make and formulate it as a decision tree to include the likelihood of the states of nature and the expected utilities of alternative solutions. After providing this information to your colleagues in your Discussion post, you will respond to two colleagues with your solutions to their decision trees.

Post:

Describe a decision you have made or need to make.
Provide the decision tree for your situation.
Do not provide the solution to your decision tree.

Provide additional resources in APA format

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Maximizing expected utility and decision trees – for

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