What’s the definition of epiphenomena? Does it explain the difference between causation and correlation or cause and effect?
Epiphenomena by definition is the incorrect belief that one thing causes another. While there is such a thing as cause and effect, people often create links between events when there are none, or believe that correlation is the same as causation.
Read more about epiphenomena, it’s definition, and more.
Epiphenomena Definition: Confusing Cause and Effect
Another common mistake that intelligent people make is falling victim to epiphenomena—the incorrect belief that one thing causes another. In other words, the epiphenomena definition comes down to confusing correlation for causation. For example, if you knew nothing about ships, you might come to the conclusion that the ship’s compass is directing it rather than simply telling which direction it’s going.
One epiphenomenon that pervades modern life is the belief that greed causes economic crises. Intelligent people note that economic depressions and extreme wealth inequality are often seen together, and they conclude that people hoarding money is the reason for the depression. This leads to the conclusion that greed is a new problem, and that if we could eliminate it, then our economies would be stable.
However, greed goes much further back in history than our current fragile economic systems. We see greed mentioned in texts from more than two millennia ago: The ancient Roman poet Virgil talked about greed of gold, and the Latin version of the New Testament warns that greed is the root of evil—radix malorum est cupiditas.
In all the time since, nobody’s found the cure for human greed. The much simpler and more practical approach is to build systems that won’t be destroyed by it. Remember that one major cause of fragility in a system is size; our economic systems today are gargantuan, complex systems that are terribly fragile as a result.
Separating Correlation From Causation
The simplest way to debunk epiphenomena is to look at sequences of events, and check whether one always comes before the other. If so, you still can’t positively conclude that one causes the other—however, if the pattern doesn’t hold, you can prove that one doesn’t cause the other.
One place to look carefully for such patterns is in academia—and specifically when academic theories are used to run the real world. There’s an old joke about a Harvard professor who lectured birds about how to fly, and he was convinced that his brilliant teaching was why they were so good at it. That’s clearly ridiculous, but people often credit scientists and mathematicians for systems that worked perfectly well without them.
For example, the mathematician Michael Atiyah, best known for his work on string theory, once came to New York on a fundraising mission. He made a speech listing numerous ways that math had been usefully applied to the real world, such as in traffic signals. Notably absent from his speech, however, were times when mathematics had caused immense harm, such as grim economic forecasts leading to self-fulfilling panics.
A side note: The kind of cherry-picking that Atiyah used in his speech is yet another example of options leading to antifragility. The one doing the picking gets to choose which facts to include and which to leave out. The wider the spread of facts is—in other words, the more options there are—the better (or worse) the cherry-picker can make the situation look. The cherry-picker’s story becomes stronger thanks to the randomness of the information he can choose from.
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Here's what you'll find in our full Antifragile summary :
- How to be helped by unforeseen events rather than harmed by them
- Why you shouldn't get too comfortable or you'll miss out on the chance to become stronger
- Why you should keep as many options available to you as possible