The Model Thinker

Models are wrong because they simplify. They omit details. By considering many models, we can overcome the narrowing of rigor by crisscrossing the landscape of the possible. To rely on a single model is hubris. It invites disaster. To believe that a single equation can explain or predict complex real-world phenomena is to fall prey to the charisma of clean, spare mathematical forms.

When taking actions, wise people apply multiple models like a doctor’s set of diagnostic tests. They use models to rule out some actions and privilege others. Wise people and teams construct a dialogue across models, exploring their overlaps and differences.

To quote the evolutionary biologist J. B. S. Haldane, “You can drop a mouse down a thousand-yard mine shaft; and, on arriving at the bottom, it gets a slight shock and walks away, provided that the ground is fairly soft. A rat is killed, a man is broken, a horse splashes.”

The Uses of Models (REDCAPE) Reason: To identify conditions and deduce logical implications. Explain: To provide (testable) explanations for empirical phenomena. Design: To choose features of institutions, policies, and rules. Communicate: To relate knowledge and understandings. Act: To guide policy choices and strategic actions. Predict: To make numerical and categorical predictions of future and unknown phenomena. Explore: To investigate possibilities and hypotheticals.

Any single way of looking at the world leaves out details and makes us prone to blind spots.

Every fundamental law has exceptions. But you still need the law or else all you have is observations that don’t make sense. And that’s not science. That’s just taking notes. —Geoffrey West

In long-tailed distributions, large events occur with sufficient probability to be of concern. In the models we covered, long-tailed distributions arise because of feedbacks and interdependencies. We should pay heed to that observation. As our world becomes more interconnected and feedbacks increase, we should see more long tails. And the current long tails that we see may get stretched even further. Inequities may increase, catastrophes grow larger, and volatility become more pronounced. None of these is desirable.

By thinking about the equation, we see that even in a context that depends almost entirely on skill, such as running, biking, swimming, chess, or tennis, if skill differences are small, luck largely determines who wins. We might expect that in the most competitive environments, like the Olympics, skill differences are small, and thus luck matters.