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Decision Tree Analysis

5 minutes 5 Questions

Decision Tree Analysis is a quantitative decision-making tool used in risk management to evaluate various courses of action in the face of uncertainty. It involves creating a tree-like diagram that maps out decisions, chance events, probabilities, costs, and outcomes. Each branch of the tree represents a possible decision or chance event, allowing project managers to visualize the implications of choosing different paths and the associated risks and rewards. In project management, Decision Tree Analysis helps in assessing the expected value of different strategic options by incorporating probabilities of risk events and their potential impacts. By calculating the Expected Monetary Value (EMV) for each possible outcome, decision-makers can compare alternative actions based on quantitative data. This method supports the selection of the option that offers the highest expected value or the most acceptable balance between risk and reward. The visual nature of decision trees makes them effective for communicating complex risk scenarios to stakeholders. They help in identifying decision points, chance events, and endpoints, facilitating a clear understanding of the consequences of decisions under uncertainty. Decision Tree Analysis is particularly useful when dealing with sequential decisions and interdependent risks, where the outcome of one risk event influences subsequent decisions and outcomes. By systematically evaluating each possible decision path, Decision Tree Analysis aids in making informed and rational choices, taking into account both the probabilities and impacts of risk events. It enhances the robustness of the risk management process by providing a structured approach to analyze and compare different strategies, ultimately contributing to more effective risk mitigation and project success.

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PMI-RMP - Quantitative Risk Analysis Example Questions

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Question 1

What principle guides the assignment of probability values to branches in a Decision Tree Analysis during early project planning?

Correct Answer: Historical data from similar projects and expert judgment combined

In Decision Tree Analysis during early project planning, the most reliable principle for assigning probability values to branches comes from combining historical data from similar projects with expert judgment. This approach is most effective because: Historical data provides concrete, real-world evidence from past similar situations, while expert judgment brings context-specific insights and experience-based knowledge to interpret and adjust that historical data for the current project's unique circumstances. The other options are less reliable because: Market conditions and stakeholder preferences are too volatile and subjective for probability assignments in this context. Purely mathematical calculations based on matrices and complexity factors lack the practical, real-world context needed for accurate probability assessment. Team consensus and organizational process assets alone may be biased and lack the objective historical basis needed for accurate probability assignments.

Question 2

When implementing sensitivity analysis in a Decision Tree model, which factor most effectively reveals the robustness of the chosen decision path?

Correct Answer: The variance in EMV across different probability ranges

The variance in EMV (Expected Monetary Value) across different probability ranges is the most effective factor in revealing decision path robustness because: 1. It measures the actual impact of probability changes on the final outcome 2. It provides quantifiable measurements of how sensitive decisions are to uncertainty 3. It helps identify which variables have the greatest impact on the decision's success 4. It allows for systematic evaluation of risk tolerance levels The other options are less effective because: The total number of alternative paths is a static measure that doesn't reflect the quality or impact of choices. Cumulative effects of probability adjustments can be misleading as they may overemphasize minor variables. Relative importance of decision nodes is subjective and may not accurately represent the true sensitivity of the decision path. EMV variance provides concrete, measurable data that directly correlates to the decision's stability under different scenarios.

Question 3

In Decision Tree Analysis, which method best captures the influence of competing stakeholder priorities on decision outcomes?

Correct Answer: Weighted branch scoring based on stakeholder impact levels

Weighted branch scoring based on stakeholder impact levels is the most effective method for capturing stakeholder priorities in Decision Tree Analysis because: 1. It provides a quantifiable way to measure different stakeholder influences 2. It allows for proportional representation of stakeholder importance 3. It integrates stakeholder priorities into the actual decision-making framework 4. It enables objective comparison of different decision paths based on stakeholder impact Why other options are less suitable: Automated calculation through mathematical modeling is too rigid and may miss nuanced stakeholder considerations. Cumulative probability assessments focus more on likelihood rather than actual stakeholder influence and importance. Multi-dimensional analysis is too broad and lacks the specific scoring mechanism needed for practical decision-making in a decision tree context.

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