What are decision trees and how can you use them to make informed decisions?

1 What is a decision tree?

A decision tree is a diagram that shows the structure of a decision problem, starting from a root node that represents the initial situation, and branching out into different nodes that represent the possible actions, events, or outcomes. Each node has a value or a probability associated with it, depending on the type of decision tree. The branches are connected by decision points, where you have to choose among the available options, or chance nodes, where the outcome depends on some random factor. The end nodes, or leaves, represent the final outcomes or payoffs of the decision problem.

2 Why use a decision tree?

A decision tree can help you to visualize the logic and consequences of your decision problem, and to compare the expected values or probabilities of different scenarios. A decision tree can also help you to identify the key factors and uncertainties that affect your decision, and to evaluate the sensitivity of your results to changes in those factors. A decision tree can also help you to communicate your decision process and rationale to others, and to solicit feedback and suggestions.

3 How to create a decision tree?

To create a decision tree, you must first define the objective and scope of the decision problem, as well as identify the main alternatives and criteria to evaluate them. Then, draw a root node that represents the initial situation. From there, draw branches to the first level of nodes, which represent possible actions or choices. For each action node, draw branches to the second level of nodes, which represent possible events or outcomes. Continue this process until you reach the end nodes or leaves, which represent the final outcomes or payoffs. Assign a value or probability to each node and path, then evaluate the decision tree by calculating their expected value or probability. Lastly, compare them to find the optimal or preferred solution.

4 How to use a decision tree?

Using a decision tree to analyze and interpret the results of your evaluation can help you identify the best or most likely outcome, the worst or least likely outcome, the most influential or uncertain factors, the trade-offs or risks involved in each action, and the opportunities or improvements that can be made to your decision problem. This is done by examining the highest expected value or probability of each branch, node, or path; the lowest expected value or probability of each branch, node, or path; the variation or sensitivity of the expected value or probability of each branch, node, or path to changes in those factors; and the difference or range of the expected value or probability of each branch, node, or path. Additionally, you can receive feedback and suggestions from others and use your own analysis to help identify these outcomes.

5 What are the limitations of a decision tree?

A decision tree is a useful and powerful tool for decision-making, but it also has some limitations that you need to be aware of and address. For example, a decision tree can become too complex or large to handle, especially if there are many actions, events, outcomes, or criteria involved. Additionally, a decision tree can be biased or inaccurate, depending on the quality and validity of the data used to create and evaluate it. Moreover, a decision tree can be subjective or incomplete, depending on the perspective and preferences used to define and frame the decision problem. Therefore, you may need to simplify or prune your decision tree, verify and update it, and consider other perspectives and preferences in order to make it more manageable, reliable, accurate, objective, and comprehensive.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?