Thinking models in practice: first principles

First principles thinking means breaking a problem down to the things you are confident are true — physical limits, real costs, actual requirements — and reasoning up from those, instead of starting from how the problem is usually solved. It is slow and effortful, so it is worth using only where convention may be wrong and the stakes justify the work.

Part of Thinking models in practice, a short series on classic methods and how to run them on your own material.

Analogy versus first principles

Most reasoning is by analogy, and that is fine: "we did it this way last time", "this is how others in the industry price it". Analogy is fast and usually right.

It fails when the conditions that made the old answer right have changed — and nobody has noticed, because the answer is inherited rather than derived. First principles is the method for checking whether an inherited answer still follows from anything.

The idea is old: Aristotle described first principles as the propositions from which a thing is known and which are not themselves derived from anything else. The modern, practical version is less grand: which parts of this are actually required, and which are just habit?

How to do it

1. Write down the current answer and the reasons usually given for it.
2. Label each reason. Is it a constraint (a law, a physical limit, a hard cost), a requirement (something the user genuinely needs), or a convention (how it is done)?
3. Set the conventions aside. Not delete — set aside.
4. Rebuild from constraints and requirements only. What is the simplest thing that satisfies them?
5. Compare with the current answer. Where they differ, either the convention carries a reason you missed (put it back, labelled properly) or it is the opportunity.

A worked example

Problem: a small research group spends two weeks every year preparing a reading list for new students.

Usual reasons: every student needs a canonical list; the list must cover the whole field; a senior member must curate it.

Labelled: "must cover the whole field" is a convention — students need to start working, not survey the field. "Senior member must curate" is a requirement only for the first five papers, which set the frame. The real constraint is the students' time in their first month.

Rebuilt: five foundational papers chosen by a senior member, plus a standing folder of the group's own annotated reading that students extend themselves. Two days instead of two weeks, and the list keeps itself current.

Running it with AI

AI is useful at step 2, because it is good at noticing that a "must" is really a habit, and at step 4, because it can generate several rebuilt answers quickly. It is weaker at knowing which of your constraints are real — it does not know your budget, your regulator or your users. Give it those explicitly.

In MindMax, first principles is one of the built-in "dig deeper" skills on the canvas. Apply it to a card holding the problem, ideally with your notes or documents attached, and it strips away the analogy and convention and reasons up from what is left, one branch per step. Aim it at a single line of a card to work on one assumption without disturbing the rest.

Where it goes wrong

• Everything is labelled a convention. Some conventions encode hard-won lessons. If you cannot say why a convention exists, find out before discarding it.
• It is used where analogy would do. For most decisions, "what worked last time" is good enough. Save this for the ones where it might not be.
• The rebuilt answer is never tested. A derivation is a hypothesis. Try the smallest version of it first.

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Part of thinking models on the canvas. Previous: the steelman. Next: SCAMPER.