Decision Trees: Visualizing Choices and Their Outcomes

What is this page about?

An explainer of decision trees as visual frameworks that make uncertainty explicit, opening with Merck mapping the Mectizan river-blindness decision. It covers the anatomy (decision nodes, chance nodes, outcomes), how to read and solve a tree, when to use them (high-stakes and sequential decisions under uncertainty) versus when they are overkill, the hard problem of assigning probabilities and running sensitivity analysis, expected-value calculation and its limits, common mistakes, and combining decision trees with scenario planning and second-order thinking.

What has been corrected on this page?

Every accepted correction to this page is recorded with the exact change, so readers can see how the page improved over time.

1. 11 July 2026 · corrected by Emir Baycan

   The planning fallacy was introduced in Kahneman & Tversky's 1979 'Intuitive Prediction' (TIMS), not the 1979 Econometrica Prospect Theory paper.

   Before

   synthesized in their work on the planning fallacy (1979, Econometrica)

   After

   synthesized in their work on the planning fallacy (1979, Intuitive Prediction: Biases and Corrective Procedures, TIMS Studies in the Management Sciences)

   Why: Verified live: body content already correctly cites the TIMS Studies in the Management Sciences paper title and venue. FAQ, excerpt, meta_description, and seo_keywords contain no reference to this citation, so no residual fabrication anywhere on the post.

   View the full record →

Who checked this page?

1 contributor has checked "Decision Trees: Visualizing Choices and Their Outcomes" on When Notes Fly. Each name below links to that person's public CitePep profile, where every contribution they have made is listed with the exact change they proposed.