IB Business Management Decision Trees Notes

Decision trees map options, outcomes, probabilities, and payoffs

1. Each decision is one possible option.
   1. Example: "Launch Product" vs "Do Nothing".
2. Each decision has outcomes (things that might happen).
   1. Example: demand might be high, medium, or low.
3. Every outcome gets a probability (chance it happens, from 0 to 1) and a payoff (how much money you gain or lose).
4. All probabilities for one decision must add up to exactly 1.0 because one of the outcomes will definitely happen.
5. Expected value is calculated by multiplying each outcome's probability by its payoff and adding the results.
6. The decision with the highest EV is usually the best rational choice.

Decision trees use nodes, branches, and outcome values to show decision paths

Nodes show decisions, chance events, and final outcomes

Nodes represent decision points, chance events, or outcomes.

1. Decision Nodes (Squares/Rectangles): Points where a choice must be made.
2. Chance Nodes (Circles): Points where an outcome depends on probability.
3. Outcome Nodes: End points showing the result of a decision path.

Branches

Branches show possible choices and associated probabilities.

1. Decision Branches: Connect decision nodes to possible actions.
2. Chance Branches: Connect chance nodes to potential outcomes, each with a probability.

Expected value gives a probability weighted average outcome

Expected value calculates the average financial outcome by weighting each possible outcome by its probability.

Build a decision tree by defining options, outcomes, and expected values

Step 1: Define the Decision

1. State the decision clearly and define the choice being evaluated.
2. Example: "Should we invest in a new marketing campaign?"

Step 2: Identify Options and Outcomes

1. List all possible choices and their potential outcomes.
2. Include probabilities for chance events.

Step 3: Calculate Expected Values

1. For each chance node, calculate the expected value.
2. Use these values to compare different decision paths.

Decision trees support planning and risk management

Decision trees support strategic planning by comparing uncertain options

1. Product Launches: Evaluate potential success or failure.
2. Investment Decisions: Assess risks and returns.

Decision trees support risk management by identifying and quantifying uncertainty

1. Identify uncertainties: Visualise potential risks.
2. Quantify Impact: Use expected values to measure financial implications.

Decision trees improve clarity but depend on assumptions

Advantages

1. Clarity: Visual representation simplifies complex decisions.
2. Data-Driven: Incorporates probabilities and financial outcomes.
3. Flexibility: Adapts to different scenarios and industries.

Limitations

1. Assumptions: Relies on accurate probability estimates.
2. Complexity: Large trees can become difficult to manage.
3. Focus on Quantitative Factors: May overlook qualitative aspects.