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# What Is the Problem of Induction?

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• Written By: Mary McMahon
• Edited By: Nancy Fann-Im
• Images By: Evgenia Tiplyashina, n/a
2003-2019
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The problem of induction is a question among philosophers and other people interested in human behavior who want to know if inductive reasoning, a cornerstone of human logic, actually generates useful and meaningful information. A number of noted philosophers, including Karl Popper and David Hume, have tackled this topic, and it continues to be a subject of interest and discussion. Inductive reasoning is often faulty, and thus some philosophers argue that it is not a reliable source of information.

In the course of inductive reasoning, a series of observations are used to draw a conclusion on the basis of experience. One problem with this logic is that simply because a set of experiences all support a logical conclusion doesn't mean something isn't out there to contradict that conclusion. One of the most famous examples is that of the black swan. A subject sees a series of white swans and concludes on the basis of this information that all swans are white, as whiteness must be an intrinsic state of swans. When this person sees a black swan, it disproves that conclusion and illustrates the problem of induction.

Humans are forced to make logical decisions on the basis of inductive reasoning constantly, and sometimes these decisions are not reliable. In finance and investing, for example, investors rely on their experiences with the market to make assumptions about how the market will move. When they are incorrect, they can incur financial losses. After the fact, they understand that the conclusion they reached was wrong, but they had no way of being able to predict this when the market always behaved in a way that matched their expectations before.

The problem of induction can play a key role in understanding probability and how people make decisions. In a situation where conclusions hinge on a series of positive observations with no negative to contradict them, the conclusions could be more accurately expressed in terms of probability, as opposed to statistics. For example, if a rider has never fallen off a horse and prepares to try out a new mount, she could say she is unlikely to be thrown, based on her previous experiences, but she should not rule out the possibility altogether.

Thanks to the problem of induction, people can make decisions on the basis of limited information, and this may lead them to make bad choices. Each event that reinforces the conclusion is taken as further supporting evidence for the conclusion, instead of another data point to consider. This can create a false sense of confidence. The problem of induction can also play a role in logical fallacies like the belief that an observed correlation is evidence of causation.