Probabilistic vs Mechanistic (29/30)
I'm writing 30 posts in 30 days. This is number 29.
I’m always thinking a lot about success. Largely because I want to be successful. And I’m categorizing most fields people want to succeed into two categories: probabilistic domains and mechanistic domains.
An example of a probabilistic domain is investment.
An example of a mechanistic domain is playing a type of sports such as tennis.
Fundamental Difference Between the Two
Probabilistic domains tend to be dominated by abstractions which trade off less precisions for more accuracy. In some sense, the fundamental law in probabilistic fields is that precision and accuracy are two different things and there’s a trade-off between the two. Higher precision almost results in a loss of accuracy. And vice versa. In a probabilistic domain, roughly correct outperforms precisely wrong.
Mechanistic domains tend to be dominated by abstractions with higher precisions. The fundamental law is that both precision and accuracy are tightly coupled so as to be like they are the same thing. There’s no trade-off here. Reducing precision is the same as reducing accuracy. In a mechanistic domain, it’s better to be more precise as that always means more accuracy.
Types of Proficiency
Mechanistic domains will produce two clear categories of players and non-players. Going from non-player to player is not luck based. There are clear paths to get from being a non-player to a player. However, moving across different echelons within the player category requires increasingly on factors outside one’s control. These less controllable factors include innate talent, finding the right network of coaches or door-openers or peers, being in the right era or market, etc.
In probabilistic domains, almost anyone can be a player. Essentially, anyone can call themselves a player by virtue of simply playing the game. Classic example is investment. During the 1630s Netherlands, so long as you have money and can buy tulips, you can also call yourself an investor during the tulip mania. Because anyone can get in, it’s harder to differentiate players who got good results via luck and those via some actual insight or skills.
Probabilistic domains don’t mean a total absence of skill. The skill that are involved in probabilistic domains are a bit meta. They are more about knowing when to play, how far to push the boundaries, which actual games within the domain to play, which games to avoid. If you translate it into the example of investments, it’s easier to understand these meta-skills.
When to play becomes when to buy, when to sell. How far to push the boundaries becomes how long to hold onto to a losing position or how long to hold onto your paper gains before cashing them in. Which actual games to play or avoid becomes which type of financial vehicles to specialize in or avoid. Is it cryptocurrencies? Is it bonds? Is it stocks? Even within those types, you can specialize (or avoid) further. Is it bitcoin? Is it Ethereum? Is it government bonds? Is it tech stocks? And so on.
Lindy Effect
But, you can differentiate players in probabilistic domains by using time and natural selection. Over time, those left standing after a long period of time, you can safely assume have more skills than luck. Of course, I say, “more skills than luck” is because there’s still the statistical likelihood some extreme luck may be involved. But, one can never know for sure. There’s no simple binary tests compared to mechanistic domains.
A Drop of Each in the Other
And in truth, while you can categorize most fields into either mechanistic or probabilistic domains, all fields always have some element of probabilistic mechanisms and erm, mechanistic mechanisms. Again, using investing as an example. Most would categorize investing as probabilistic. Yet, there are sub skills in the investment field that are mechanistic. Being a skilful user of spreadsheet is such a skill. And there are many others such as collating public data of companies and scoring the public data against a set criteria, and so on.
The opposite also occurs. Say, when you play in a tennis tournament, probabilistic things can affect your outcome dramatically. Luck of the draw in a tournament, the weather that day, the umpires, etc. If you know the taijitu, the yin-yang symbol in Taoism, you will recognise the drop of yin in the yang side of the symbol and vice versa.
This is similar to how mechanistic domains contains elements of the probabilitics and vice versa.
What About the Domain of Building Business?
Just a quick summary, as you climb up the echelons of a mechanistic domain, the probabilistic elements start to matter more in the outcome. Therefore, meta-skills such as timing skills, meta decision-making, etc and outside your control factors such as talent and luck have significant impact on your results.
For probabilistic domains, there’s no clear skill hierarchy like in mechanistic domains. However, the longer you maintain your results at a certain level, the more likely you have some skills. Again, the skills that seem to matter for your longevity in probabilistic domains are in the meta sense such as timing skills, decision-making skills, etc
Business is one of those fields where arguably it has both probabilistic and mechanistic elements at around 50-50 split. If you repeatedly make successful businesses in different industries, you definitely have some actual skills typical of a player in a mechanistic domain just as a good player would win multiple tournaments. Businesspeople who are successful in multiple industries exist such as Rich Barton ( founded Expedia, Zillow, and Glassdoor) and Steve Jobs (founded Apple, chairman of Pixar). On the other hand, if you build a company that’s successful and lasts for years, you’d also have the kind of skills that do well even in probabilistic domains.
I don’t know what I want to do more. Make multiple businesses in different industries or make a business that last for many years. I like to start with making a very good one that makes a million dollars in ARR first.