Markov Decision Processes (MDPs) provide an organized approach to thinking about risk when the result is unpredictable, and judgments must be taken in phases. They are particularly beneficial in complicated projects that need constant reassessment and adjustment as new information becomes available.
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Sequential Decision-Making – MDPs model project choices across time. At each level (state), the process evaluates prospective actions and results, allowing teams to make more educated, step-by-step choices.
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Probabilistic Transitions – Unlike static risk registers, MDPs allow for the probability of transitioning from one project state to another, making them perfect for dynamic and unpredictable contexts.
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Policy Optimization – You may create a "policy" (a rule for action selection) that maximizes predicted value across the project's duration. This may increase ROI or reduce predicted delays and risk exposure.
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Scenario-Based Learning – MDPs learn over iterations, using input from previous transitions to improve future forecasts. This is critical in Agile and iterative contexts.
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Visualization of Risk Paths – Project managers may discover high-risk paths and proactively reduce or avoid them.
Using MDPs changes risk assessment from a one-time static exercise to a continual, feedback-driven activity. This allows for better pivots in initiatives that are inherently unpredictable.
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