Epistemology Overview

Hooks

  • We have unlimited resources. The idea that we have limited resources is false.
  • More data can lead to worse decisions, because we find false correlations.
  • Epistemology is the solution to

Why I’m Learning About Epistemology

  • Learners are investors.
  • They are investing scarce time for a payoff in the future. That payoff can show up in a lot of unexpected ways and formats.
    • For example, while at Empact, I built a relationship with Emerson, and
  • It seems a little like poker.
  • I am noticing that there is a whole class of thinkers (Steven Wolfram, Nassim Taleb, etc) that seem to think at a deeper level. They look at a lot of the research that’s out there and call BS on it. I think I tend to be even too trusting of academic research.

My Synthesis

  • We want to succeed in the future.
  • To succeed in the future, we want to make good decisions now.
  • Some types of decisions to make:
    • Building up assets that can be used in the future
      • Healthy body
      • Developed mind
      • Lots of money
      • Belief system about the world that is the most accurate evaluate (in other words, the most no wrong). Knowledge is a form of preadaptation.
    • Making bets into experiments to build up that
  • The world is rapidly changing.
    • Just because something happened in the past, it doesn’t mean it will happen again in the future. ”No amount of sophistication is going to allay the fact that all of your knowledge is about the past and all your decisions are about the future.” - Ian E. Wilson (former Chairman of GE)
  • We have limited time and there is a ton of information
  • We are human
    • We are constantly getting triggered.
    • We have cognitive biases
    • We have huge blindspots.

Types Of Learners

Day Traders

Overview

Staying on top of the latest trends.

Case Studies

  • Garyvee
  • Paul Tudor Jones

Buy & Hold

Overview

Find the best long-term investments that will pay back for years (even decades) and hold them.

Case Studies

  • Warren Buffett

Benefits

  • Takes less active management

Types Of Cognitive Failure

The human brain is designed for survival, not for understanding truth. For more on this, read Donald Hoffman’s The Case Against Reality.

Our brain is an energy hog. While the brain is just 2% of a person’s weight, it accounts for 20% of the body’s energy use, according to research. So, the brain develops shortcuts that are right the large majority of the time, but can be wrong.

Our brain’s shortcuts are becoming wrong more often. As society changes more rapidly, there are more and more cases arising where these shortcuts become ineffective due to being a new environment it wasn’t evolved for. For example, our brains weren’t designed for environments of:

Many of the world’s richest and smartest actors (media, companies, and governments) are aware of and trigger our our supernormal stimulators.

Tale Of Three Swans

There are three types of swans:

  • White swans
  • Gray swans
  • Black swans

White Swans

Most people operate in the realm of white swans. All they have seen is white swans for their entire life. So they assume all swans are white. People who operate go based off of their life experiences and extrapolate that across time and space. For example, “Since I have never seen white swans, it means there have never been any in the past and there won’t be any in the future. It means that there aren’t any in any other parts of the world.”

Gray Swans

More sophisticated people operate in the land of gray swans. They may have never seen gray swans in their life, but through reading about other people’s experiences in different parts of the world or in the past, they learn that grey swans exist. For example, maybe they come once every 70 years on the dot. They operate on some sort of repeating loop that is predictable if you look for it. Ray Dalio is an example of an investor who operates heavily in the land of black swans. His thought leadership is based on looking back hundreds (and even thousands) of years to find repeating loops.

Black Swans

The most sophisticated people also operate in the area of black swans. Black swan thinking is the idea that even though there are no black swans that have ever been seen by humans, it doesn’t mean that they don’t exist. Furthermore, black swans are extremely consequential—think one million times more consequential than other swans. Let’s say they carry a disease that could kill billions of people. People who leave space in their mind for black swans, leave space in their investing for unpredictable huge upside and downside events.

How To Move Forward

Most people are white swan only. Some also include gray swans—professional investors. Then, a tiny sliver consider black swans. Given that investors make money by betting against the consensus and being right, it makes sense to develop grey swan and black swan thinking. Assymetric bests are often underpriced. When markets are going up, it’s hard for people to imagine a huge drop. When markets are going down or when a venture is still in the idea phase, it’s hard to imagine a huge increase. The best investors don’t necessarily try to predict a specific future—being in the right place at the right time. Rather, they try to succeed in any future over time.

History Of The Scientific Method

Schools Of Thought

Skeptical Empirical Tradition

Overview

What’s real is what you can observe.

Sub Schools

  • Negative Empiricism
    • Brochard
    • Favier
    • Popper
  • Academia
    • Carneades
    • Cicero

Probability In Epistemology

Overview

You should start with a belief-weighted probability based on priors and then update as you get more experiences.

Key Thinkers

  • Bayes
  • Peirce
  • Ramsey
  • Carnap
  • Levi
  • Kyburgh
  • Jeffreys

Concerns

The problem with this is the idea of the black swan. One big data value could throw off everything else.

For example, data might show that we’re getting safer and safer as a result in violence, but then in the end, we might have one war that kills billions of people and throws off the mean average.

David Deutsch provides a simple, technical refutation of the Bayesian philosophy of science on his blog:

By ‘Bayesian’ philosophy of science I mean the position that (1) the objective of science is, or should be, to increase our ‘credence’ for true theories, and that (2) the credences held by a rational thinker obey the probability calculus. However, if T is an explanatory theory (e.g. ‘the sun is powered by nuclear fusion’), then its negation ~T  (‘the sun is not powered by nuclear fusion’) is not an explanation at all. Therefore, suppose (implausibly, for the sake of argument) that one could quantify ‘the property that science strives to maximise’. If T had an amount q of that, then ~T would have none at all, not 1-q  as the probability calculus would require if q were a probability. Also, the conjunction (T₁ & T₂) of two mutually inconsistent explanatory theories T₁ and T₂ (such as quantum theory and relativity) is provably false, and therefore has zero probability. Yet it embodies some understanding of the world and is definitely better than nothing. Furthermore if we expect, with Popper, that all our best theories of fundamental physics are going to be superseded eventually, and we therefore believe their negations, it is still those false theories, not their true negations, that constitute all our deepest knowledge of physics. What science really seeks to ‘maximise’ (or rather, create) is explanatory power.

Fat Tails

Investing