*From Farnam Street.*

In order to improve the accuracy of our decisions we can make use of probabilistic thinking. It is trying to estimate, using tools of math and logic, the likelihood of any specific outcome coming to pass. It helps us to identify the most likely outcomes.

Premise of this thinking is probability theory. We can not be certain what will happen ion the future. It is unpredictable. The best thing we can do is to estimate the future by generating realistic, useful probabilities. Probability has three important aspects to help improve your thinking.

The core of Bayesian thinking is that, given that we have limited useful information about the world, and are constantly encountering new information, we should probably take into account what we already know when we learn something new. It allows ut to use all relevant prior information in making decisions. Statisticians might call it a base rate.

It is important to remember that priors themselves are probability estimates. For each bit of prior knowledge, you are not putting it in a binary structure, saying it is true or not. Youâ€™re assigning it a probability of being true. Therefore, you canâ€™t let your priors get in the way of processing new knowledge. In Bayesian terms, this is called the likelihood ratio or the Bayes factor. Any new information you encounter that challenges a prior simply means that the probability of that prior being true may be reduced. Eventually, some priors are replaced completely. This is an ongoing cycle of challenging and validating what you believe you know. When making uncertain decisions, itâ€™s nearly always a mistake not to ask: What are the relevant priors? What might I already know that I can use to better understand the reality of the situation?

Fat-tailed curves have similarities with bell curves. In a bell curve the extremes are predictable. A fat-tailed curve has no cap on extreme events. The more extreme events that are possible, the longer the tails of the curve get. Farnam Street explains it as:

Think of it this way. In a bell curve type of situation, like displaying the distribution of height or weight in a human population, there are outliers on the spectrum of possibility, but the outliers have a fairly well defined scope. Youâ€™ll never meet a man who is ten times the size of an average man. But in a curve with fat tails, like wealth, the central tendency does not work the same way. You may regularly meet people who are ten, 100, or 10,000 times wealthier than the average person. That is a very different type of world.

It is important not think about every possible scenario in the tail. But to deal with fat-tailed domains in the correct way. We can do that by positioning ourselves to survive or even benefit from the wildly unpredictable future. By being the only ones thinking correctly and planning for a world we don't fully understand.

The last thing has to do with the probability that your probability estimates themselves are any good. This might be called "metaprobability".

Another common asymmetry is peopleâ€™s ability to estimate the effect of traffic on travel time. How often do you leave â€śon timeâ€ť and arrive 20% early? Almost never? How often do you leave â€śon timeâ€ť and arrive 20% late? All the time? Exactly. Your estimation errors are asymmetric, skewing in a single direction. This is often the case with probabilistic decision-making. Far more probability estimates are wrong on the â€śover-optimisticâ€ť side than the â€śunder-optimisticâ€ť side. Youâ€™ll rarely read about an investor who aimed for 25% annual return rates who subsequently earned 40% over a long period of time. You can throw a dart at the Wall Street Journal and hit the names of lots of investors who aim for 25% per annum with each investment and end up closer to 10%.