Preview
IB DP Business Management — Business Management Toolkit | HL | Decision Trees
📋 What You Will Learn
- Define a decision tree, expected value, and probability, and explain how they are used in business decision-making
- Construct a decision tree from given data, correctly placing decision nodes, chance nodes, branches, probabilities, and outcomes
- Calculate expected monetary values (EMV) and use them to identify the optimal decision
- Apply decision tree analysis to a realistic business scenario involving investment, product launch, or market entry
- Evaluate the strengths and limitations of decision trees as a quantitative decision-making tool
- Link decision tree analysis to the concepts of change, risk, and ethics in managerial decision-making
📊 Topic Importance
| Paper | Relevance | Common Question Types |
|---|---|---|
| Paper 1 | High | Construct or complete a decision tree for the pre-seen company; calculate EMV; recommend a course of action |
| Paper 2 | High | Section B: calculate EMV from given data; Section C: evaluate decision trees as a tool linked to a concept |
| Paper 3 (HL) | High | Use a decision tree to justify a recommendation for a social enterprise facing uncertain outcomes |
🔍 Guiding Question When a business faces two or more possible courses of action — each with uncertain outcomes — how can managers make a rational, evidence-based choice rather than relying on instinct alone?
🌍 Why This Matters In 2023, Volkswagen faced a critical decision: accelerate investment in its own battery gigafactories or continue sourcing cells from external suppliers. Each path carried different costs, uncertain demand outcomes, and competitive risks. Decision trees give managers a structured, visual way to compare exactly these kinds of high-stakes choices — quantifying uncertainty so that the numbers, not just gut feeling, inform the final call.
What Is a Decision Tree?
Businesses rarely choose between options with guaranteed outcomes. Most real decisions involve risk — the possibility that things turn out differently from what was planned. A decision tree makes that risk visible and quantifiable.
📘 Decision Tree A quantitative decision-making tool that maps out all possible courses of action, their associated probabilities, and their financial outcomes in a branching diagram — allowing managers to calculate the expected monetary value of each option and identify the most rational choice.
🔗 Analogy Think of a decision tree like a weather forecast for a business decision. A meteorologist does not say "it will rain" — they say "there is a 70% chance of rain." A decision tree applies the same logic: instead of pretending the future is certain, it assigns probabilities to each possible outcome and calculates a weighted average result. The manager can then choose the option with the best weighted average — just as you would pack an umbrella when the probability of rain is high enough.
Key Components of a Decision Tree
Every decision tree is built from the same set of components. Understanding each one is essential before attempting any construction or calculation question.
Decision Node (Square □)
- Represented by a square .
- A point at which the manager must make a deliberate choice between two or more options.
- The manager controls what happens here — they choose which branch to follow.
- Example: "Should we launch Product A or Product B?"
Chance Node (Circle ○)
- Represented by a circle .
- A