Extrapolating from past data points is not an explanation. Building your confidence that something that will happen—like Bayes Theorem—is useful for descrete, observable problems, but fails to reveal the truth. It’s the equivalent of saying “because it’s always been that way” which is a flawed way of reasoning about the world.
For example, let’s say you are trying to predict the temperature of a beaker water. You start to turn up the heat on a burner and, based on previous data points, you expect the temperature to rise. It correlates well—heat goes up, water temperature goes up. Until it hits the boiling point and the water temperature remains constant. Trends are not sufficient to explain what’s going on here because it doesn’t explain the idea what is truly happening.
Listen to The Beginning of Infinity part 1 on Naval Ravikant’s podcast.
See also:
- More specifically, updating your priors is not an epistemology. It’s useful, but there are downsides to inductive reasoning.
- Creativity is required to offer good explanations.
- The Beginning of Infinity
Links to this note
-
One question I find myself coming back to is whether or not macroeconomics is a useful source of explanations.
-
The Importance of Anecdotes for B2B Businesses
If you are running a B2B business, you need to pay attention to anecdotes more than data.
-
Making complicated things seem simple involves abstracting over reality in such a way that is clear and actionable. Often times, that means reducing things down to one number going up or down. People are drawn to (fixated even) clarity of a single number going up or down.
-
How to Be a Good Product Engineer
Companies don’t really want frontend engineers or backend engineers or infrastructure engineers. If you work at an engineering as product organization, they want good product engineers solving user problems. As an industry, this is poorly understood and little is written to help people understand the principles of good product engineering.
-
Predictions About the Future Don’t Account for New Knowledge
One of the reasons making broad, sweeping predictions about the future tend to be wrong is that it does not account for the creation of new knowledge. Trends are not explanations and without an explanatory model of how knowledge will change (i.e. creativity) predictions such as the end of the world are just another example of a Malthusian catastrophe.
-
Trying to Know the Unknowable Leads to Pessimism
We do not yet know what we have not discovered and trying to know the unknowable (prophesy) leads to pessimism. A Malthusian catastrophe ends up being wrong because it does not predict knowledge that resulted in efficiency of food production. Similarly the pessimism of energy economics is error laden because it can not predict what new discoveries we will make in social and political systems or new defenses.
-
The Six Foot Man in the Stream That Was Five Feet on Average
The parable of the six foot man who drowned in the stream that was five feet on average is a reminder to plan for the possibility of low ends when building a portfolio. The average can be misleading because there can be large swings that still average out to something you might mistakenly believe is survivable.