1. Module 5 Part 2: Decision Theory
BUS216
Module 5 Part 2: Decision Theory
BUS216 “Probability & Statistics for Business and Economics”
Tidewater Community College
Linda S. Williams, MBA, MSA
Professor, Business Administration

2. Module 5 Part 2: Decision Theory
BUS216
Module 5 Part 2: Decision Theory
Decision-Making under Certainty
Decision-Making under Uncertainty
Decision-Making under Risk
Goals:
Maximize Returns
Minimize Loss
Minimize Regret
Content created by Linda Williams of Tidewater Community College for Z Degree Project, originally published at learn.vccs.edu under a CC BY-NC-SA license.

3. Module 5 Part 2: Decision Theory
BUS216
Module 5 Part 2: Decision Theory
Components of All Decisions:
Decision Alternatives
These are the options or choices open to any decision maker.
Identification of possible alternatives is the starting point for all decision theory
States of Nature
These are any condition in “nature” that can occur after a decision has been made
The conditions affect the outcome of the decision in either a positive, neutral or negative way
The decision maker does not control the States of Nature
Payoffs
These are the rewards for any given decision alternative
Payoffs can range in size and in some instances may be a loss
Content created by Linda Williams of Tidewater Community College for Z Degree Project, originally published at learn.vccs.edu under a CC BY-NC-SA license.

4. Module 5 Part 2: Decision Theory
BUS216
Module 5 Part 2: Decision Theory
Payoff Tables
State of Nature
(S1)
(S2)
(S3)
(S4)
Decision Alternative (d1)
Payoff (P1,1)
Payoff (P1,2)
Payoff (P1,3)
Payoff (P1,4)
Decision Alternative (d2)
Payoff (P2,1)
Payoff (P2,2)
Payoff (P2,3)
Payoff (P2,4)
Decision Alternative (d3)
Payoff (P3,1)
Payoff (P3,2)
Payoff (P3,3)
Payoff (P3,4)
Content created by Linda Williams of Tidewater Community College for Z Degree Project, originally published at learn.vccs.edu under a CC BY-NC-SA license.

5. Module 5 Part 2: Decision Theory
BUS216
Module 5 Part 2: Decision Theory
Decision Making Under Certainty
State of Nature is known
Select Decision Alternative with the highest payoff
…. And this happens when ?????
ALMOST NEVER!
Content created by Linda Williams of Tidewater Community College for Z Degree Project, originally published at learn.vccs.edu under a CC BY-NC-SA license.

6. Module 5 Part 2: Decision Theory
BUS216
Module 5 Part 2: Decision Theory
Decision Making Under Uncertainty
The decision maker does not know which state of nature will occur
The decision maker does not know the probability of the various states of nature occurring
Approaches to decision making under uncertainty depend upon the criteria for the decision and the decision maker’s outlook
Payoff Tables are used to determine possible payoffs under various states of nature
Content created by Linda Williams of Tidewater Community College for Z Degree Project, originally published at learn.vccs.edu under a CC BY-NC-SA license.

7. Module 5 Part 2: Decision Theory
BUS216
Module 5 Part 2: Decision Theory
Maximax Criterion
Optimistic approach where decision maker bases action on the “best case” scenario”
Isolate the highest payoff under each decision alternative
Select the decision alternative that provides the highest payoff of the maximums
Often called “best of the best” approach to decision making
Content created by Linda Williams of Tidewater Community College for Z Degree Project, originally published at learn.vccs.edu under a CC BY-NC-SA license.

8. Module 5 Part 2: Decision Theory
BUS216
Module 5 Part 2: Decision Theory
Maximin Criterion
This is a pessimistic approach to decision making
The assumption is that the “worst” will occur and so the decision is made to minimize the damage
Determine the smallest payoff under each decision alternative
Select the “best” of these worst case scenario payoffs
We refer to this as “maximizing the minimum” return
Content created by Linda Williams of Tidewater Community College for Z Degree Project, originally published at learn.vccs.edu under a CC BY-NC-SA license.

9. Module 5 Part 2: Decision Theory
BUS216
Module 5 Part 2: Decision Theory
Hurwicz Criterion
This is a “middle of the road” approach
This criterion selects the maximum payoff and the minimum payoff for each decision alternative
α =the optimism with a value between 0 and 1 with 1 being the MOST optimistic
Multiply the maximum payoff by α
Multiply the minimum payoff by 1 – α
Sum the weighted products for each decision alternative
Select the maximum weighted value and the corresponding decision alternative
Content created by Linda Williams of Tidewater Community College for Z Degree Project, originally published at learn.vccs.edu under a CC BY-NC-SA license.

10. Module 5 Part 2: Decision Theory
BUS216
Module 5 Part 2: Decision Theory
Minimax Regret Strategy
Based on lost opportunity because the wrong decision was made and payoff was not maximized
Transform the Decision Table into an Opportunity Loss Table in order to apply the Minimax Regret criterion
Determine the highest payoff for each decision under the State of Nature
Subtract the payoff for each decision alternative from the highest payoff. This is the “regret”
Replace the payoffs with the “regret” or opportunity losses creates the opportunity loss table
Then determine the maximum regret for each decision alternative and select the smallest regret available
Content created by Linda Williams of Tidewater Community College for Z Degree Project, originally published at learn.vccs.edu under a CC BY-NC-SA license.

11. Module 5 Part 2: Decision Theory
BUS216
Module 5 Part 2: Decision Theory
Decision Making Under Risk
The decision maker does not know which state of nature will occur
The decision maker knows the probability of the various states of nature occurring
Payoff Tables are used to determine possible payoffs under various states of nature
Decisions are made based on the long-run average return for a decision, based on the probability of the various states of nature
Content created by Linda Williams of Tidewater Community College for Z Degree Project, originally published at learn.vccs.edu under a CC BY-NC-SA license.

12. Module 5 Part 2: Decision Theory
BUS216
Module 5 Part 2: Decision Theory
Expected Monetary Value (EMV)
Each payoff under each state of nature is now associated with a probability
Find the EMV of each Decision Alternative: (Payoff) X (Probability of that State of Nature)
Sum the products across the States of Nature to arrive at the EMV of each alternative
Select the decision alternative with the highest EMV
The strategy of EMV is that it maximizes the return over the “long run”
It does not guarantee this return on a single investment
Content created by Linda Williams of Tidewater Community College for Z Degree Project, originally published at learn.vccs.edu under a CC BY-NC-SA license.

13. Module 5 Part 2: Decision Theory
BUS216
Module 5 Part 2: Decision Theory
Expected Value of Perfect Information (EVPI)
The EVPI is the value that a decision maker places on knowing which state of nature will occur
It is always presumed that the information is available
It is always presumed that the information is accurate
As long as the EVPI does not exceed the EMV, the decision maker will pay for the information
EVPI = EMV with Perfect Information – EMV without Information
Content created by Linda Williams of Tidewater Community College for Z Degree Project, originally published at learn.vccs.edu under a CC BY-NC-SA license.

14. Module 5 Part 2: Decision Theory
BUS216
Module 5 Part 2: Decision Theory
Expected Value of Perfect Information (EVPI)
What is the value of knowing which state of nature will occur?
This is the difference between the payoff that would occur if the decision maker knew which state of nature would occur and the expected monetary payoff from the best alternative when there is no information available
EVPI = Expected Monetary Payoff with Perfect Information – Expected Monetary Payoff without Information
Content created by Linda Williams of Tidewater Community College for Z Degree Project, originally published at learn.vccs.edu under a CC BY-NC-SA license.

15. Module 5 Part 2: Decision Theory
BUS216
Module 5 Part 2: Decision Theory
Expected Value of Perfect Information (EVPI)
Expected Monetary Value without information = highest payoff considering the probability of each State of Nature
Perfect Information: Highest Payoff under each State of Nature weighted by the probability of that State of Nature
The difference between these two is the EVPI
Content created by Linda Williams of Tidewater Community College for Z Degree Project, originally published at learn.vccs.edu under a CC BY-NC-SA license.