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## MGS, Test three study guide

by: Tricia Williams

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# MGS, Test three study guide MGS 3100

Tricia Williams
GSU
GPA 3.86

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These notes cover what will be in the next exam
COURSE
Buisness Analysis
PROF.
Mark Sweatt
TYPE
Study Guide
PAGES
3
WORDS
CONCEPTS
decision
KARMA
50 ?

## Popular in Managerial Science

This 3 page Study Guide was uploaded by Tricia Williams on Monday July 18, 2016. The Study Guide belongs to MGS 3100 at Georgia State University taught by Mark Sweatt in Summer 2016. Since its upload, it has received 10 views. For similar materials see Buisness Analysis in Managerial Science at Georgia State University.

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Date Created: 07/18/16
Decision Analysis (Chapter 6) Decision: The action of choosing one alternative over the other. Analyzing a decision consist of: Decision Alternatives States of nature, and Payoffs Decision Alternatives: Are various choices that are available to choose from. (eg. Invest or not to invest, buy or not to buy) States of Nature: These events cannot be control by the decision maker. (eg. The economy, the weather, or the size of a building etc.) Payoffs: Payoffs are express as profits or loss and are stated as the rewards receive from the chosen decision. List of decision-making environments 1. Ignorance: Assumes equality because the decision maker has no knowledge of the probabilities at which the state of nature will occur. 2. Risk: Under this environment, the decision maker now knows the probabilities at which the states of nature will occur. 3. Certainty: The decision maker is aware of which state of nature will happen. List of Criterions under Ignorance 1. Maximax: Also known as risk seeking behavior, this is where the decision maker choses the best of the best decision payoffs. In this criterion, the decision maker choses the max value from each decision alternative, and then the maximum of those chosen. 2. Maximin: Also known as Risk adverse behavior, which means the decision maker is one who wants to avoid risk and choses the best of the worse payoffs. For this criterion, the decision chooses the best of the worse payoffs from each alternatives and then the best of those chosen. 3. Laplace: In this criterion, the decision maker averages the payoffs for each alternative and then choses the alternative with the highest average. 4. Minimax Regret/Lost opportunity: Shows the amount that can be lost from choosing one alternative over the other. This is calculated by taking the maximum payoff of each state of nature and subtracting their corresponding alternatives. The accuracy of the table can be tested by making sure there is a payoff of zero in each state of nature. (It is also important to note that all the numbers in this table should be positive numbers) List of criterions under risk 1. EVUII/EVwII/EV: Expected value of a decision made with perfect information. It is calculated by finding the probability of each corresponding alternative and then choosing the alternative with the max number. 2. EVUPI/EVwPI: Expected value of decision made with perfect information. It is calculated by finding the probability of the max payoff in each state of nature and then sum the weighted averages to get the EVUPI/EVwPI. 3. EVPI: Maximum price of perfect information or the maximum price the decision maker is willing to pay for the perfect information. It is calculated by subtracting EVUII from EVUPI. 4. EVSI: This represents the expected value of sample information, and is calculated by subtracting the maximum possible expected return without sample information (EV), from the maximum possible expected return with sample information. 5. EOL: Expected opportunity loss. This is done by calculating the weighted average of the opportunity losses for each alternative and then choosing the lowest value of the sum EOL.  EOL is also equals to EVPI  EVUPI is also equals to EV + EOL  Probabilities should always equal to 1.  Prior probabilities is what is given  Marginal probabilities is the odds of doing one thing  Joint Probability is the probability of two events happening at the same time. It is calculated by multiplying the prior probability by each new data probability. Decision /choice node are represented by squares:This is the point where the selection made by the decision maker is listed. It also consist of branches representing each decision alternatives Chance/event node are represented by circles: Consist of branches that represents event that may take place/branches representing each state of nature payoffs. Bayes theorem is using new data to revise probabilities. Posterior probabilities contains new data.

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