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Mental Models
Featured Models

Decision Tree

Decision Trees are used in domains as diverse as manufacturing, investment, management, and machine learning, and they are a tool that you can use to break down complex decisions or automate simple ones.  A Decision Tree is a visual flowchart that allows you to consider multiple scenarios, weigh probabilities, and work through defined criteria to take action.  THE ANATOMY OF A TREE. Decision Trees start with a single node that branches into multiple possible outcomes based on a test or decision. Each branch has additional nodes that can continue to branch into additional possibilities. It’s what you’re likely doing unconsciously, whenever you break down an issue using clarifying questions or criteria.  CATEGORISE OR PREDICT.  I find it most useful to consider Decision Trees in the context of one of two use-cases.  Categorise options to decide: methodically break down a large, complex problem with stepped criteria until you reach a clear action point.  Predict possibilities: assign probabilities and costs to potential outcomes to assess choices based on the ‘expected value’ of those options. This can be applied to potential income from various investment opportunities, right through to risk assessment analysis. See the In Practice section for an example. It’s worth noting that Machine Learning categorises Decision Trees differently, as Categorical Variables, which are binary Y/N tests; and Continuous Variables, which are ranges such as time or distance. However, such classifications are less useful for broader applications of this model.  Again, view the In Practice section below to view different types of Decision Trees.  FOR CLARITY IN COMPLEXITY AND SIMPLE AUTOMATION. This model can help you with both simple and complex decision-making challenges.  For complex decisions, Decision Trees will help you systematically break down a problem into its component decision points. Their visual nature supports collaboration and discussion. Further, assigning probability and costs to alternative scenarios will help identify the expected value behind a decision and assist in interrupting unconscious bias.  For relatively simple decisions that have more than one decision point, Decision Trees can help you to reduce cognitive load and by working through a consistent, informed automatic pilot. See the ‘Should I keep this’ example in the In Practice section below.  GROWING THE TREE. The starting point for a Decision Tree is defining the problem you are solving for and identifying the first, most clarifying criteria or test to assign as your first node.  After that, it’s a case of choosing the most useful categories that help further break down possible options and the problem into clear components. If you are using probability and cost impact as an additional method to assess options, simply multiply the probability and amount of money for each option to determine an average comparison. See the In Practice section for a worked example of this.  IN YOUR LATTICEWORK. Decision Trees play well with a range of other models. Use Framestorming and/or the 5 Whys to ensure that you’re answering the most powerful question before you start. Assigning expected values for each scenario is essentially the application of both Probabilistic Thinking and a Cost-Benefit Analysis.  You can go deeper into your decision analysis with Second-Order Thinking, and I recommended using Decision Trees with your Cynefin Practice — particularly in the Complicated and Clear domains.

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