Description: A probabilistic decision tree is a model that combines the structure of a decision tree with the incorporation of probabilities at each of its nodes. This allows for the representation of the uncertainty inherent in decisions and the outcomes of future events. In a traditional decision tree, decisions are made based on deterministic criteria, while in a probabilistic decision tree, each branch of the tree is associated with a probability that reflects the likelihood of a specific outcome occurring. This feature makes it a powerful tool for decision-making in situations where information is incomplete or uncertain. The nodes of the tree represent decisions or events, and the branches that emerge from them indicate the possible alternatives and their respective probabilities. This approach allows analysts to evaluate not only the most likely outcome but also the variability and risk associated with each decision. In summary, probabilistic decision trees are fundamental in the analysis of complex decisions, as they provide a clear framework for assessing the consequences of different actions in uncertain environments.
