Decision Tree: Group No: 4 Marketing Batch

Decision Tree Yes

NO

Group no: 4 Marketing Batch

Introduction • Decision theory is a set of techniques which are used for making decision in in the decision- environment of uncertainity and risk • In a decision tree, we describe the choices and uncertainties facing a single decision-making agent. • This usually means a single decision maker, but it could also mean a decision-making group or a company.

Decision Tree • Three types of “nodes” • Decision nodes - represented by squares (□) • Chance nodes - represented by circles (Ο) • Terminal nodes - represented by triangles (optional)

• Solving the tree involves pruning all but the best decisions at decision nodes, and finding expected values of all possible states of nature at chance nodes • Create the tree from left to right • Solve the tree from right to left

Using Decision Trees • Can be used as visual aids to structure and solve sequential decision problems • Especially beneficial when the complexity of the problem grows • Decision trees are commonly used in operations research, specifically indecision analysis, to help identify a strategy most likely to reach a goal. • Another use of decision tree is as a descriptive means for calculating conditional probabilities

What they look like ? • Works like a flow chart • Looks like an upside down tree • Nodes • appear as rectangles or circles • represent test or decision • Lines or branches - represent outcome of a test • Circles - terminal (leaf) nodes • Top or starting node- root node • Internal nodes - rectangles

Types Of Decision making Environment Certainty • A state of certainty exists when a decision makes knows, with reasonable certainty, what the alternative are and what conditions are associated with each alternative • Very few organizational decisions, are made under these conditions

Risk • A state of Risk exists when a decision maker makes decisions in which the availability of each alternative and its payoffs and cost are all associated with probability estimate • Decision such as these are based on past experiences, relevant information, the advice of others and one’s own judgment • Decision is calculated on the basis of which alternative has the highest probability of working effectively

Uncertainty • A state of Uncertainty exists when a decision maker does not known all of the alternatives, the risk associated with each or the consequences of each alternative is likely to have • Most of the major decision making in today’s organizations is done under these condition • To make effective decision under these conditions, managers must secure as much relevant information as possible

• The Maximax payoff criterion seeks the largest of the maximum payoffs among the actions. • The maximin payoff criterion seeks the largest of the minimum payoffs among the actions. • The minimax regret criterion seeks the smallest of the maximum regrets among the actions.

Risk Averse Organization • Most of organizations are cautious in situations where they think they might be vulnerable to large losses. •

These organizations may shy away from project decisions which, if they were to fail, would expose the organization to large losses, even if such project decisions might also offer a possibility of large gains associated with success. This behaviour is called “risk-averse.” • Decisions made by risk-averse organizations’ tend to maximize their E(U) rather than EMV, and that utility may give serious (negative) weight to the possibility of large losses. • Most decision tree software allows the user to design a utility function that reflects the organization's degree of aversion to large losses.

Risk Neutral Organization Risk neutral organization evaluates alternatives decisions using expected monetary value, calculated by multiplying the value of each possible result by its probability of occurring and adding the probability – weighted values of all possible result. This is equivalent to applying a linear utility function. Generally if the value of a decision calculated this way is not large enough the organization will not do it.

Difference Risk Averse • Risk Averse generally choose the guaranteed payment. • Believes in Something is better than Nothing. • Risk-averse investors tend to choose safer investments to place their assets

Risk Neutral • Risk neutral normally selects the investment with the highest expected return • It preferences simply wants to maximize their expected value • Risk neutral is indifference to risk.

UNCERTAIN PARAMETERS • Uncertain parameters become known only after a decision is made. • When a parameter is uncertain, we treat it as if it could take on two or more values, depending on influences beyond our control. • These influences are called states of nature, or more simply, states. • In many instances, we can list the possible states, and for each one, the corresponding value of the parameter. • Finally, we can assign probabilities to each of the states so that the parameter outcomes form a probability distribution.

PAYOFF TABLES AND DECISION CRITERIA • For each action-state combination, the entry in the table is a measure of the economic result. • Typically, the payoffs are measured in monetary terms, but they need not be profit figures. • They could be costs or revenues in other applications, so we use the more general term payoff.

INCORPORATING PROBABILITIES • We can immediately translate this information into probability distributions for the payoffs corresponding to each of the potential actions. • We use the notation EP to represent an expected payoff (e.g., an expected profit). • Note that the expected payoff calculation ignores no information: all outcomes and probabilities are incorporated into the result.

Example • A glass factory specializing in crystal is experiencing a substantial backlog, and the firm's management is considering three courses of action: • A) Arrange for subcontracting (S1) • B) To begin overtime production (S2) • C) Construct new facilities (S3) • The correct choice depends largely upon future demand, which may be low, medium, or high. By consensus, management estimates the respective demand probabilities as 0.10, 0.50, and 0.40. A cost analysis reveals the effect upon the profits.

Demand

Probability

Course of Action S1 (SUBCONTRACTING )

S2 (BEGIN OVERTIME)

S3 (CONSTRUCT FACILITIES)

low (L)

0.10

10

-20

-150

Medium (M)

0.50

50

60

20

High (H)

0.40

50

100

200

1

M1 (p= 0.50

4

0.10 x 10

= 01 (in '000 Rs)

5

0.50 x 50

= 25

6

0.40 x 50 = 20 46

7

0.10 x -20 = -02

8

0.50 x 60 = 30

9

0.40 x 100 = 40

EMV=46

S2: Begin overtime 0

2 EMV=68

M2 (p= 0.50

68 10

0.10 x -150 = -15

11

0.50 x 20

12

0.40 x 200 = 80

= 10

3

EMV=75

75

Decision Tree used in different fields

Choosing Investments

Product Launch Strategy

Granting loans by Banks

Advantages of Decision Tree • Simple, Understanding-brief explanation • Value- Hard data, Important insights- experts describing • Helps in determining • White box • Combined- other decision techniques

Disadvantages of Decision Tree • Unstable • Relatively inaccurate • Information gain in decision trees is biased • Calculations-complex

Summary • A decision tree is a specialized model for recognizing the role of uncertainties in a decision-making situation. • Trees help us distinguish between decisions and random events, and more importantly, they help us sort out the sequence in which they occur. • Probability trees provide us with an opportunity to consider the possible states in a random environment when there are several sources of uncertainty, and they become components of decision trees.

• The key elements of decision trees are decisions and chance events. A decision is the selection of a particular action from a given list of possibilities. • A chance event gives rise to a set of possible states, and each actionstate pair results in an economic payoff. • In the simplest cases, these relationships can be displayed in a payoff table, but in complex situations, a decision tree tends to be a more flexible way to represent the relationships and consequences of decisions made under uncertainty.

• The choice of a criterion is a critical step in solving a decision problem when uncertainty is involved. • There are benchmark criteria for optimistic and pessimistic decision making, but these are somewhat extreme criteria. They ignore some available information, including probabilities, in order to simplify the task of choosing a decision. • The more common approach is to use probability assessments and then to take the criterion to be maximizing the expected payoff, which in the business context translates into maximizing expected profit or minimizing expected cost.

Thank You

Reference • https://www.slideshare.net/macjones25/decision-tree-10164318 • https://www.mindtools.com/dectree.html • https://www.slideshare.net/anandarora/decision-tree-analysisslideshare • https://en.wikipedia.org/wiki/Decision_tree • https://education2research.com/decision-making-definition-typesand-decision-making-environments/ • https://www.wisdomjobs.com/e-university/quantitative-techniquesfor-management-tutorial-297/decision-making-under-uncertainty10067.html