# Probabilistic Thinking #mentalmodel ## Source - [Probabilistic Thinking](https://fs.blog/2018/05/probabilistic-thinking/) ## Keywords (topics and howto) - [[topic - mental models]] - [[how to make better decisions]] ## Relevant notes ## Notes - Probabilistic thinking is essentially trying to estimate, using some tools of math and logic, the likelihood of any specific outcome coming to pass. ([source](https://fs.blog/2018/05/probabilistic-thinking/)) ## Three types of probabilistic thinking ### Bayesian thinking When getting new information, add it to all the information you already know; don’t simply make a decision based on the new information; it misses all the value of your knowledge. > Consider the headline “Violent Stabbings on the Rise.” Without Bayesian thinking, you might become genuinely afraid because your chances of being a victim of assault or murder are higher than a few months ago. But a Bayesian approach will have you putting this information into the context of what you already know about violent crime. > >You know that violent crime has declined to its lowest rate in decades. Your city is safer now than it has been since this measurement started. Let’s say your chance of being a victim of a stabbing last year was one in 10,000 or 0.01%. The article states, with accuracy, that violent crime has doubled. It is now two in 10,000, or 0.02%. Is that worth being terribly worried about? The prior information here is key. When we factor it in, we realize our safety has not been compromised. ([source](https://fs.blog/2018/05/probabilistic-thinking/)) ### Fat-tailed curves In a non-fat-tailed (i.e. normal distribution), there are limits on the right and left. If the curve shows height, there will never be a person with size 0 or a person 10 times bigger than average. In a fat-tailed curve, there are no limits to the extremes. Wealth is like this, car crashes are here, and death by terrorism is here too. ### Asymmetries > This massively misunderstood concept has to do with asymmetries. If you look at nicely polished stock pitches made by professional investors, nearly every time an idea is presented, the investor looks their audience in the eye and states they think they’re going to achieve a rate of return of 20% to 40% per annum, if not higher. Yet _exceedingly_ few of them ever attain that mark, and it’s not because they don’t have any winners. It’s because they get so many so wrong. They consistently overestimate their confidence in their probabilistic estimates. (For reference, the general stock market has returned no more than 7% to 8% per annum in the United States over a long period before fees.) ([source](https://fs.blog/2018/05/probabilistic-thinking/))