In his latest Insight with Annie Duke, Howard Marks emphasizes the importance of intellectual humility and considering alternate outcomes when making decisions. He argues that most people focus on their expected outcome and neglect the potential for negative consequences. They advocate for calculating expected value while being aware of its limitations and potential to mask the risk of catastrophic outcomes. He encourages a cautious approach, highlighting the need to consider the worst-case scenario and ensure it’s survivable. Here’s an excerpt from the Insight:
I think that people also, it’s very important that they consider, that they practice intellectually humility, which means
practice saying, “What if I’m wrong? The other person could be right, I could be wrong.” And what if things don’t go the way I expect? What would the consequences be?
Most people model an outcome, an outlook and think about the consequences that it goes that way. They may even assign a probability to it, but they’re not as rigorous in saying what are the other ways things can go? What are the probabilities of those? What are the likely consequences?
And I think that you have to have a sense for whether there are outcomes that you can’t survive. Annie mentioned the importance of expected value and calculating the expected value of something is one of the first decision tools we learn.
You multiply each outcome by its probability and then you get a weighted outcome, which is considered the expected
But first of all, the expected outcome, ironically, because of the averaging process, can be something that’s impossible to be achieved. The expected outcome may not be within a set of actual possible outcomes.
And what do you do if the expected outcome is very good, but the possible outcome, under some adverse cases, is not survivable? Then what do you do?
You know, I always say that I don’t want to be the skydiver who was successful 98% of the time. That skydiver has a
very, very high expected value, but it’s not a role for me. So what if I’m wrong, really, is a big help.
You can read the entire insight here:
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