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Weekly Blog #57 - Deductive vs. inductive reasoning...

First of all I want to apologize for not posting as regularly as I used to. Most of all I think that I have to apologize to myself since I wanted to create a good habit of writing and learning while doing so every week. So, let's try to return to the old ways...


This week I want to write about something that I've first heard of while reading about the foundations of scientific work about 18 months ago. I didn't put too much thought into it back then, but while recently reading "The Black Swan" by Nassim N. Taleb I stumbled across the differences between deductive vs. inductive reasoning again.

Let me give you a short explanation of what that is: The main difference between inductive and deductive reasoning is that inductive reasoning aims at developing a theory while deductive reasoning aims at testing an existing theory. Inductive reasoning moves from specific observations towards broad generalizations, and deductive reasoning the other way around.

When applying deductive logic, we follow a thought pattern similar to this: all humans are mortal. I am a human. I am mortal. When applying inductive logic things look a little bit different. We use inductive reasoning to simplify the world around us. In broader terms, this means that whenever we stumble upon a situation that fits a certain pattern, we tend to generalize it as "another one of those". We think that while for each day of our lives the sun has risen in the east, it will rise again in the east tomorrow. This example from David Hume is intuitively a sound argument – most of us believe the sun will rise in the east tomorrow. However, it lacks the same strength as a deductive argument. Even if the premise is true, it does not guarantee the conclusion must be true.

The main issue with inductive reasoning is that there are rare cases in which our "theory" does not apply. Hume stated that we “proceed upon the supposition that the future will be conformable to the past”. In reality, the state of nature can change. He thus showed that we cannot reach a general conclusion simply based on a particular set of observations. Nor is it just a matter of probabilities (because if the state of nature changes, so do the probabilities). Hume offered little solution, except that even if inductive reasoning is invalid, it is a matter of human custom and habit.

Taleb called such rare events "black swans" derived from the roman poet Juvenal, who characterized something as being like “a rare bird in the lands and very much like a black swan”. He defined a black swan event as having three characteristics: “First, it is an outlier, as it lies outside the realm of regular expectations, because nothing in the past can convincingly point to its possibility. Second, it carries an extreme ‘impact’. Third, in spite of its outlier status, human nature makes us concoct explanations for its occurrence after the fact, making it explainable and predictable.” Taleb’s main insight was that many financial institutions and businesses were (and still are) very vulnerable to these events. His recommendation isn’t to predict these black swan events which would be too challenging, but to build in additional robustness to be able to handle these events. He also criticized the use of normal distributions in statistical models, arguing the real world has fat tails – i.e. higher chances of rare high impact events taking place. His analysis gained popularity in the wake of the 2008 financial crisis.


What's the lesson to be learned from this? Well, while writing my masters thesis I stumbled across a large variety of theories that are related to the topic I am writing about: learning theories, leadership theories, personality and trait related theories, etc... Some of the studies I've read use inductive reasoning to create or feed into certain patterns of thinking or behavior. Their findings are not linked to any of the above mentioned theories. Although in some cases theses arguments seem to be valid, in many cases they don't since there's enough room for error either in the data provided, the samples collected or other hidden and confounding variables. But there are also studies that link their findings to theories by using deductive reasoning. These studies are - although a little less interesting in my opinion because we love the narrative of a good story - more reliable and should therefore be in the focus of attention.

But what's the moral for every day life? In my case I feel a lot more confident in discussions, arguments and decision making processes - especially including predictions of any kind - knowing that there are different ways of reasoning and how to identify them. It is helpful to know that, in case the outcome can't be predicted, it can be rather helpful to create a worst case scenario that goes against our typical way of overestimating our own success rates. Leaving the comfort zone and allowing us to think beyond the margin of error that we usually allow for ourselves is crucial. What would we do if the sun didn't rise in the east tomorrow? Uncomfortable scenario, isn't it?

Another important thing I've learned is this: when I take part in a discussion that's mostly based on opinions and observations, I tend to be rather careful to jump to conclusions derived from the information provided. Ultimately, being able to tell facts from fiction, opinions from evidence and real experts (competent people in an area where expertise can be built upon historical data and information that allow for solid predictions) from so called "empty suits" (incompetent people with too much confidence in their own knowledge and predictions in a field that doesn't allow for predictions at all) helps a great deal in making real life decisions and - probably most importantly - become more relaxed in regards to the daily flood of information provided


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