Loss Aversion
Loss aversion is a concept in behavioral economics that suggests people are more likely to avoid losses than to acquire gains. It is a cognitive bias that causes people to prefer avoiding losses to acquiring equivalent gains. Loss aversion is a form of risk aversion, which is the tendency to prefer a sure gain over a gamble with a higher expected value. Loss aversion is a powerful force in decision-making, and it can lead to irrational decisions in certain situations.
History of Loss Aversion
The concept of loss aversion was first proposed by Amos Tversky and Daniel Kahneman in 1979. They proposed that people are more sensitive to losses than to gains, and that this sensitivity leads to a preference for avoiding losses over acquiring gains. This idea has since been studied extensively in the field of behavioral economics, and it has been found to have a significant impact on decision-making.
Loss aversion has been found to be a powerful force in decision-making, and it can lead to irrational decisions in certain situations. For example, people may be more likely to take a gamble if they are presented with the possibility of a large loss, even if the expected value of the gamble is negative. This is because the potential loss is more salient than the potential gain.
Table of Comparisons
Gain | Loss |
---|---|
+$100 | -$100 |
+$500 | -$500 |
+$1000 | -$1000 |
Summary
Loss aversion is a concept in behavioral economics that suggests people are more likely to avoid losses than to acquire gains. It is a cognitive bias that causes people to prefer avoiding losses to acquiring equivalent gains. Loss aversion is a powerful force in decision-making, and it can lead to irrational decisions in certain situations. To learn more about loss aversion, you can visit websites such as Investopedia, The Balance, and Behavioral Economics.
See Also
- Risk Aversion
- Prospect Theory
- Endowment Effect
- Status Quo Bias
- Anchoring Bias
- Regret Aversion
- Mental Accounting
- Framing Effect
- Choice Overload
- Confirmation Bias