Decision Making Tree

DECISION MAKING TREE

Description also available in video format (attached below), for better experience use your desktop.

 Introduction

• A Decision Tree is a graphical representation of possible solutions to a decision based on different conditions.
• It helps in making structured, logical, and data-driven choices.
• Widely used in Operations Research, Strategic Management, Risk Analysis, and Healthcare Administration.

Key Concepts

Term	Meaning
Node	A point on the tree where decisions or outcomes happen
Decision Node (□)	Square node – represents a point where a decision is to be made
Chance Node (○)	Circular node – represents a random event or uncertainty
Branches	Lines connecting nodes – represent decision alternatives or outcomes
End Nodes (Terminal nodes)	Indicate the final outcome/result

Elements of a Decision Tree

1. Decision Points (Squares) – Options available to the decision-maker.
2. Chance Events (Circles) – Events with uncertainty, like probabilities of success/failure.
3. Outcomes (Leaves) – The final result associated with each decision path.
4. Probabilities – Likelihood of occurrence assigned to each chance event.
5. Payoffs/Costs – Monetary or utility values assigned to each outcome.
6. Expected Value (EV) – Used to evaluate which path yields the best average outcome.

Steps to Construct a Decision Tree

1. Define the problem clearly.
2. Identify the decision alternatives.
3. Identify chance events and their probabilities.
4. Estimate outcomes or payoffs for each path.
5. Calculate Expected Value (EV) at each chance node:

EV=∑(Probability × Payoff) EV = \sum (Probability × Payoff)

6. Choose the path with the highest EV (for gains) or lowest EV (for costs).

Example of Decision Tree Use

Situation:

A hospital is deciding whether to invest in a new diagnostic machine.

• Option 1: Buy the machine.
• Success (60%): Profit = ₹5,00,000
• Failure (40%): Loss = ₹2,00,000
• Option 2: Don’t buy – No gain or loss.

EV Calculation:

EV= (0.6×5,00,000) + (0.4×−2,00,000) = 3,00,000−80,000=₹2,20,000

# Since EV is positive, buying the machine is the better option.

Advantages

• Clear and visual structure for complex decisions.
• Helps compare multiple options.
• Integrates risk and uncertainty via probabilities.
• Facilitates evidence-based decision making.

Limitations

• Can become complex with too many alternatives.
• Requires accurate data (probabilities and outcomes).
• May oversimplify real-world uncertainties.
• Prone to bias in assigning probabilities or values.

Applications

• Healthcare: Treatment options, equipment purchase decisions.
• Business: Investment, marketing strategies.
• Operations: Resource allocation, project planning.
• Policy Making: Public health interventions.

Tips for Effective Use

• Keep it simple and structured.
• Use reliable data for probabilities and outcomes.
• Consider sensitivity analysis for major decisions.
• Combine with tools like Cost-Benefit Analysis or SWOT for better insights. 

Video Description

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