1. Elements of Decision Problems
Given a complicated decision problem, one should first identify the elements of the problem
Values and objectives
Decisions to make (and alternatives)
Uncertain events (and outcomes)
Consequences

2. Values and Objectives Values Objectives
What the decision maker cares about
Objectives
The specific things to be achieved
Include objects and the direction of preference
e.g. Maximizing profits, maximizing life quality
Some objectives can be related
e.g. Improving the image of a company may in turn bring it more profits
There can be tradeoffs among multiple objectives
e.g. Economic benefits gained by using chemical insecticide lead to health risk
A requisite model includes all objectives that matter and only those that matter in the decision context at hand

3. Decisions to Make Decision Alternatives Sequential Decisions
Specific alternatives
e.g. Purchasing or leasing a car
Choose a specific value out of a range of possible values
e.g. Deciding the amount of money to invest in a project
Consider the possibility of no action and of waiting to obtain more information
Sequential Decisions
Multiple decisions are ordered sequentially
Dynamic decisions: a future decision may depend on exactly what happened before
e.g. First decide purchase or lease a car, next decide what kind of car to purchase or lease, then decide where to get the car, …
It is important to identify the order in which the decisions occur

4. Uncertain Events
Uncertainty about the future makes decision problems hard
It is important to focus on only the relevant uncertain events
The possible outcomes can be finite or within some range of values
e.g. whether it will rain or not, the annual profits of a company
Complicated decision problems can involve interdependent uncertain events
e.g. The interest rate of car mortgage depends on the market economy status
The time sequence of uncertain events in sequential decisions is critical
What events are unknown and what information is available before a decision is made

5. Consequences
The final results after the last decision has been made and the last uncertain event has been resolved
Defining Measurement Scales for Consequences
Consequences corresponding to objectives with natural attribute scales can be measured objectively
e.g. monetary values, time, length, weight, etc.
Consequences corresponding to objectives without natural attribute scales
Measured indirectly with proxies
e.g. GPA as a measure of a person’s intelligence
Measured subjectively using an attribute rating scale
e.g. The quality of life can be measured using a five-point Likert scale questionnaire (best, better, satisfactory, worse, and worst)
Tradeoffs among multiple objectives

6. Computer Industry Growth
Influence Diagrams
Invest?
Venture Succeeds
or Fails
Return on Investment
Computer Industry Growth
Overall
Satisfaction
Decision Node
Chance Node
Computation Node
Payoff Node
Influence Diagram of a Venture Capitalist’s Decision Problem

7. Influence Diagrams (Cont.)
Relationships between nodes are symbolized with arrows or directed arcs
Distinctions are made here between sequence and dependence arcs only for teaching purposes. Once you are familiar with the differences, you can use solid arcs throughout the influence diagram like the convention used in the textbook

8. Influence Diagrams (Cont.)
Influence Diagrams and Fundamental Objectives Hierarchy
The Payoff node corresponds to the most general objective (located at the upper-most level) in the fundamental-objectives hierarchy
The computation nodes correspond to the objectives at the lower levels in the hierarchy

9. Constructing an Influence Diagram
No set strategy is given; a good approach is to put together a simple version of the diagram first and then add details as necessary
Steps for Constructing Influence Diagram
1. Identify the decisions to be made. If there are more than one decision, determine their time sequence and draw sequence arcs to connect the decision nodes
2. Structure fundamental objectives hierarchy and convert the fundamental objectives into payoff or computation nodes in the influence diagram
3. Identify relevance relationships between the decision nodes and computation nodes or payoff node and draw corresponding arcs
4. Identify all the uncertain events
5. Identify the sequence relationships between the chance nodes and decision nodes and draw corresponding arcs
6. Identify the relevance relationships between the chance nodes and draw corresponding arcs

10. Constructing an Influence Diagram
Steps for Constructing Influence Diagram (Cont.)
7. Identify the relevance relationships between the chance nodes and computation nodes or payoff node and draw corresponding arcs
8. Check the appropriateness of the influence diagram (any missing and/or irrelevant information)

11. EPA Example
The Environmental Protection Agency (EPA) often must decide whether to permit the use of an economically beneficial chemical that may induce cancer (carcinogenic). Furthermore, the decision often must be made without perfect information about either the long-term benefits or health hazards. Alternative courses of actions are to permit the use of the chemical, restrict its use, or to ban it all together. Tests can be run to learn something about the carcinogenic potential, and survey data can give an indication of the extent to which people are exposed when they do use the chemical. These pieces of information are both important in making the decision. For example, if the chemical is only mildly toxic and the exposure rate is minimal, then restricted use may be reasonable. On the other hand, if the chemical is only mildly toxic but the exposure rate is high, then banning its use may be imperative.

12. Carcinogenic Potential
Influence Diagram of the EPA Decision Problem
Net Value
Economic Value
Cancer Cost
Usage Decision?
Lab Test
Net Value
Survey
Exposure Rate
Economic Value
Cancer Cost
Carcinogenic Potential

13. Influence Diagram of the EPA Decision Problem
Usage Decision?
Lab Test
Survey
Carcinogenic Potential
Net Value
Economic Value
Cancer Cost
Exposure Rate
Cancer Risk
Influence Diagram of the EPA Decision Problem
(adding a computation node)

14. Decision Trees Decision Trees Display A Decision Problem in Detail
Decision trees explicitly identify the sequence of decisions/events (from left to right)
Decision trees show all possible future scenarios
One branch for each decision alternative
One branch for each outcome of an uncertain event (outcomes must be mutually exclusive and collectively exhaustive)

15. Decision Trees (Cont.)
Decision Alternative
Chance Node
Consequence
Widely Success
$3,000
Business
Decision Node
Flop
Business Result $0
Outcome of Uncertain Event
Investment Choice
$200
Savings
Decision Tree of the Investment Decision Problem

16. Product-Switching Example
A company needs to decide whether to switch to a new product or not. The product that the company is currently making provides a fixed payoff of $150,000. If the company switches to the new product, its payoff depends on the level of sales. It is estimated that there are about 30% chance of high-level sales ($300,000 payoff), 50% chance of medium-level sales ($100,000 payoff), and 20% chance of low-level sales (losing $100,000). A survey which costs $20,000 can be performed to provide information regarding the sales to be expected. If the survey shows high-level sales, then there are about 60% chance of high-level sales and 40% chance of medium-level sales when the company sells the product. On the other hand, if the survey shows low-level sales, then there are about 60%chance of medium-level sales and 40% chance of low-level sales when the company sells the product. 16

17. Old $150,000 $130,000 Survey High High $300,000 (0.6) $280,000 (0.5)
New
Medium
$100,000 (0.4)
$80,000
Old
$150,000
Perform Survey
$130,000
Survey Low
Medium
$100,000 (0.6)
(0.5)
$80,000
New
-$20,000
Low
-$100,000 (0.4)
-$120,000
Don’t Perform
Old
$150,000
$150,000
New
High
$300,000 (0.3)
$300,000
Medium
$100,000 (0.5)
$100,000
Low
-$100,000 (0.2)
-$100,000 17