First Principles Thinking

Why is it important: First Principles thinking is touted by some very successful people (Elon Musk, Steve Jobs, Jeff Bezos) as the reason they succeeded. I tend to believe in the power of first principles thinking. This post will provide an alternative worldview

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Summary

When your arguments are laid out one after another, and there is no logical gap in those arguments, you can be convinced of their conclusion

But you can make a bunch of mistakes while reasoning like this. Specifically:

One or more of your ‘principles’ or ‘axioms’ turns out to be mistaken

You make a mistake in one of your inference steps

There are other axioms that you may have ignored

You may be arguing at the wrong level of abstraction – you could be "right" in a phyrric, meaningless sense

The form of the error is subtle, and therefore more difficult to detect; the best description I have for it is: ‘perfectly rational, logically constructed, and not really wrong — but not as useful or as powerful as some other framing.’

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When doing first principles thinking, it's always a good idea to look at ways where your conclusion can be falsified. That'll allow you to do better research

Plausible sounding arguments are dealt with in a simple manner: you try the recommendations that unfold from the analysis, but you remain alert to see if they give you exactly the results you want.

If they don’t, you keep the frame for the time being, but you continue to look out for a better explanation. And how would you know if you have found a better way of thinking about your situation? Simple: you listen carefully. In the words of Malaysian magnate Robert Kuok, “you learn to distill wisdom from the air.”

Related: Thinking at the right level of abstraction

You can be absolutely correct in your thinking and yet fail — because you're thinking at the wrong level of abstraction.

Predicting rain doesn't count. Building an ark does. If you're gesticulating at all the storm clouds in the horizon and telling everyone who would listen about the coming thunderstorm. But have no carpentry skills to speak of and don't know anyone who does, you can't take advantage of any of these analyses. It is a waste of time.

Make sure that your analysis of the problem is aligned with what the goal of solving the problem would be. Proximate causes are easier to reason about than remote ones. Remote causes have second and third order effects that are hard to predict

Often, really understanding proximate causes is harder than remote causes because of lack of data and lack of published research. Don't give into this availability bias. Predicting the behaviour of complex systems can be a fool's errand, because:

they are made up of agents that act in parallel. there effects are hard to model

the properties of such systems emerge out of competition and collaboration amongst these agents

they tend to have many levels of organisation, with different properties and behaviours emerging at each level

all complex adaptive systems anticipate the future and act according to their predictions, and

complex adaptive systems are dynamic, and generate new niches and interconnections between those niches over time.

The reason I’ve spent an entire essay arguing that we should ‘seek ideas at the right level of abstraction’ is because I think that the opposite habit — ‘use high-level analyses as a justification for our actions’— is a particularly pernicious trap for smart, analytical people. We do this because it’s a narrative stereotype: we think that geniuses must extrapolate from high-level analyses to individual action, and therefore we should do the same.

Consequence: Action Produces Information

A Chinese businessman we had done business with once put it to me like this: “Why you think so much? Just act first! Then you watch and see what happens. Maybe the customer don't like it. Or maybe your competitor do something to you because you do this. But then you know more than if you just sit here and think think think!”

Why is this the case? Good entrepreneurs are able to adapt their behaviours to match the contours of reality, and the contours of reality in business seem to be:

A sizeable portion of decisions in business are reversible decisions.

The information that comes from action is often more valuable than the insight that comes from analysis. This is especially true if there is a high level of uncertainty in your industry.

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It doesn't even matter what you do as long as you do something, because “action produces information.” So at a certain point, you got to stop pontificating about this stuff and just try something, anything. You're going to be embarrassed by the V1 until you go out there and you create. That's part of the product development process, is just dramatically scaling back kind of the ambition and the feature set and everything to rapidly iterate and prototype these things, but go do anything The first thing you try is almost guaranteed not to work. So don't give up, just go try the next thing, and the next thing, and the next thing. That's the only way that new products and companies ever get created in the world. You got to put a lot of shots on goal to get one to eventually work.

Often, all that analysis just keeps you on the sidelines. Often you're better off flipping a coin and moving in any clear direction. Once you start moving, you get new data regardless of where you're trying to go. And the new data makes the next decision and the next better than staying on the sidelines desperately trying to predict the future without that time machine.

If you had to compare two options, one of which is outstanding and the other of which is terrible, you wouldn’t need to do any analysis. It would be an easy choice. As the two options get closer and closer together in their attractiveness, the decision gets harder. (...) In the example of purchasing a used car, we can see that the three options are all very close—they each have comparable strengths and weaknesses. There just isn’t much that differentiates them. The options were so close together that simply flipping a coin would have been sufficient. (...) I call this the zone of indifference problem.

Some decisions are consequential and irreversible or nearly irreversible — one-way doors — and these decisions must be made methodically, carefully, slowly, with great deliberation and consultation. If you walk through and don’t like what you see on the other side, you can’t get back to where you were before. We can call these Type 1 decisions

But most decisions aren’t like that — they are changeable, reversible — they’re two-way doors. If you’ve made a suboptimal Type 2 decision, you don’t have to live with the consequences for that long. You can reopen the door and go back through. Type 2 decisions can and should be made quickly by high judgment individuals or small groups.

As organisations get larger, there seems to be a tendency to use the heavy-weight Type 1 decision-making process on most decisions, including many Type 2 decisions. The end result of this is slowness, unthoughtful risk aversion, failure to experiment sufficiently, and consequently diminished invention*. We’ll have to figure out how to fight that tendency.