1. “ The one word that makes a good manager – decisiveness.”
DECISION THEORY

2. What is Decision Theory?
“ A process of best selection from a set of alternative courses of action, that course Of action which is supposed to meet objectives up to satisfaction of the decision maker “
Courses of Action: A set of predermined rules
Without a clear direction we slow down, uncertain of where to head.
Decision theory provides direction with clarity that allows for focused effort and speed.

3. The Decision Maker refers to ‘Individual’ or a ‘group of individual’ responsible for making the choice of an appropriate course of action amongst the available course of action.

4. Decision Alternatives
There is a finite number of decision alternatives available to the decision maker at each point in time When a decision is made.
These alternatives are also called Courses of action ( Actions, acts, or Strategies ) and are not only under control but are also known to the decision maker.

5. States of Nature
These are the future conditions (also called consequences, events or scenarios) that are not under the control of decision maker.
A state of nature can be a state of economy (e.g. inflation) , a weather condition, a political development, etc. These are the result of an ’act of God’ or result of many situations pushing in various directions.

6. States of Nature
The states of Nature are mutually exclusive and collectively exhaustive (total) With respect to any decision problem.
The states of nature may be described numerically such as , demand of 100 units of an item or non – numerically such as employees strike, etc.

7. Pay off
A numerical value (Out Come) resulting from each possible combination of alternatives and states of nature is called payoff . A tabular arrangement of these values is known as payoff matrix.
States of Nature
Courses of Action A1 A2 …. An N1
a11
a12 …
a1n N2
a21
a22
a2n N3
a31
a3n . Nm
am1
am2
a mn

8. Pay off matrix example. Weather Yield in kg per hectare 500 1700 4500
States of
Nature
Yield in kg per hectare
Weather
Dry(E1)
Moderate(E2)
Damp(E3)
Price
Rs.Per Kg
paddy (A1)
500
1700
4500
1.25
Ground Nut (A2)
800
1200
1000
4.00
Tobacco(A3)
100
300
200
15.00
Courses of action

9. Regret or Opportunity loss
Opportunity loss is incurred due to failure of not adopting most favourable course of action or strategy. The difference between the highest possible profit for a state of nature and the actual profit obtained for the particular action taken is known as opportunity loss.

10. Types of DECISION-MAKING Environments
1. Decision under certainty
2. Decision Under Uncertainty
(W/0 probability)
3. Decision under Risk
(With probability)

11. 1. Decision under certainty
In this case the decision maker has the complete knowledge of consequence of every decision choice with certainty.
Example.
Break even point analysis, wherein precise information about the cost and sales revenue is known, and liner programming , wherein the required resource , available resources, and the costs/profits associated with it are known.

12. 2. Decision Under Uncertainty (With out Probability)
The decision situations where there is no way in which the decision maker can assess the probabilities of the various states of nature are called decisions under uncertainty.
Example.
A new product is introduced in the market.
A new plant is set up.

13. 3. Decision under Risk (With probability)
In this case decision maker Supposed to believe Authentic information, knowledge, past experience or happenings to enable him to assign probability values to the likelihood of occurrence of each state of nature.

14. Decision Making Under Uncertainty
The five commonly used criteria for decision making under uncertainty are:
the optimistic approach (Maxi-max)
the conservative approach (Maxi-min)
the minimax regret approach (Mini-max regret)
Equally likely (Laplace criterion)
Criterion of realism with  (Hurwicz criterion)

15. 1. Optimistic Approach
The optimistic approach would be used by an optimistic decision maker.
The decision with the largest possible payoff is chosen.
If the payoff table was in terms of costs, the decision with the lowest cost would be chosen.

16. Example: CAL Condominium Complex
A developer must decide how large a luxury condominium complex to build – small, medium, or large. The profitability of this complex depends upon the future level of demand for the complex’s condominiums.

17. CAL Condos: Elements of Decision Theory
States of nature: The states of nature could be defined as low demand and high demand.
Alternatives: CAL could decide to build a small, medium, or large condominium complex.
Payoffs: The profit for each alternative under each potential state of nature is going to be determined.

18. CAL Condos: Payoff Table
THIS IS A PROFIT PAYOFF TABLE
States of Nature
Alternatives
Low
High
Small 8 8
Medium 5 15
Large
-11 22
(payoffs in millions of dollars)

19. CAL Condos: Optimistic Decision (maximax )
If the optimistic approach is selected:
STATES OF NATURE BEST
Alternatives Low High PROFIT
Small
Medium
Large
Maximax payoff
Maximax decision

20. 2. Conservative Approach
The conservative approach would be used by a conservative decision maker.
For each decision the minimum payoff is listed and then the decision corresponding to the maximum of these minimum payoffs is selected. (Hence, the minimum possible payoff is maximized.)
If the payoff was in terms of costs, the maximum costs would be determined for each decision and then the decision corresponding to the minimum of these maximum costs is selected. (Hence, the maximum possible cost is minimized.)

21. CAL Condos: Conservative Decision ( Max-min)
If the conservative approach is selected:
STATES OF NATURE WORST
Alternatives Low High PROFIT
Small
Medium
Large
Maxi-min decision
Maxi-min payoff
The decision with the best profit from the column of worst profits is selected.

22. 3. Mini-max Regret Approach
The minimax regret approach requires the construction of a regret table or an opportunity loss table.
This is done by calculating for each state of nature the difference between each payoff and the largest payoff for that state of nature.
Then, using this regret table, the maximum regret for each possible decision is listed.
The decision chosen is the one corresponding to the minimum of the maximum regrets.

23. 4.Laplace Criterion:
Assume equal amount of probabilities
Computation of expected value for each decision choice

24. First, select coefficient of realism, a, with a value between 0 and 1.
5. Hurwic’z Criterion:
First, select coefficient of realism, a, with a value between 0 and 1.
When a is close to 1, decision maker is optimistic about future, and when a is close to 0, decision maker is pessimistic about future.
Payoff = a (maximum payoff) + (1-a) x(minimum payoff)

25. Decision-making under conditions of Risk ( with probability )
If the availability of information for a decision environment is partial, then a decision under such an environment is called ‘decision under risk’. The available information will be given in the form of probability distribution.
Expected Money value (EMV)
Expected opportunity loss (EOL)
Expected value of perfect Information (EVPI)

26. Expected Money value (EMV)

27. Expected opportunity loss (EOL)

28. Expected value of perfect Information (EVPI)

29. Decision Tree Decision Tree
A Visual Representation of Choices, Consequences, Probabilities, and Opportunities.
A Way of Breaking Down Complicated Situations Down to Easier-to-Understand Scenarios.

30. Easy Example A Decision Tree with two choices.
Go to Graduate School to get my MBA.
Go to Work “in the Real World”

31. Decision Trees
A decision tree is a chronological representation of the decision problem.
Each decision tree has two types of nodes; round nodes correspond to the states of nature while square nodes correspond to the decision alternatives.
The branches leaving each round node represent the different states of nature while the branches leaving each square node represent the different decision alternatives.
At the end of each limb of a tree are the payoffs attained from the series of branches making up that limb.

32. Notation Used in Decision Trees
A box is used to show a choice that the manager has to make.
A circle is used to show that a probability outcome will occur.
Lines connect outcomes to their choice or probability outcome.

33. What are some of the costs we should take into account when deciding whether or not to go to business school?
Tuition and Fees
Rent / Food / etc.
Opportunity cost of salary
Anticipated future earnings

34. Simple Decision Tree Model
2 Years of tuition: $55,000, 2 years of Room/Board: $20,000; 2 years of Opportunity Cost of Salary = $100,000 Total = $175,000.
PLUS  Anticipated 5 year salary after Business School = $600,000.
NPV (business school) = $600,000 - $175,000 = $425,000
Go to Graduate School to get my MBA.
Go to Work “in the Real World”
First two year salary = $100,000 (from above), minus expenses of $20,000.
Final five year salary = $330,000
NPV (no b-school) = $410,000
Go to Business School

35. Example – Joe’s Garage
Joe’s garage is considering hiring another mechanic. The mechanic would cost them an additional $50,000 / year in salary and benefits. If there are a lot of accidents in Providence this year, they anticipate making an additional $70,000 in net revenue. If there are not a lot of accidents, they could lose $20,000 off of last year’s total net revenues. Because of all the ice on the roads, Joe thinks that there will be a 70% chance of “a lot of accidents” and a 30% chance of “fewer accidents”. Assume if he doesn’t expand he will have the same revenue as last year.
Draw a decision tree for Joe and tell him what he should do.

36. Example 2 - Answer
70% chance of an increase in accidents
Profit = $70,000 .7
Hire new mechanic
Cost = $50,000
30% chance of a decrease in accidents
Profit = - $20,000 .3
Don’t hire new mechanic
Cost = $0
Estimated value of “Hire Mechanic” = NPV =.7(70,000) + .3(- $20,000) - $50,000 = - $7,000
Therefore you should not hire the mechanic