# Pseudocertainty

How would you rate this post? (1 votes, average: 5.00 out of 5)

Human beings tend to underweight high probability events but appropriately weight events that are certain. If an event has a probability of 0.1 or 0 probability, we tend to evaluate it accurately. However, if the event has a high probability (e.g. 0.83) we tend to respond as the expected utility framework would expect us to respond of less then 0.83. This is called pseudo-certainty (Kahneman and Tversky, 1979).

Any protective action that reduces the probability of harm from 0.1 to 0 will be valued more highly than an action that reduces the probability of the same harm from 0.1 to 0.2 (Slovic, 1982). People value creation of certainty over an equally valued shift in the level of uncertainty.

## Example

Perception of certainty can be manipulated. Assume an insurance company advertises a disaster protection policy that covers fire but not flood. First choice is “full protection agains fire” and the second one is “overall probability of loss from natural disasters”. People are more likely to choose the “full protection” policy because it reduced perceived uncertainty for loss from fire to zero. In contrast, overall disaster policy reduces perceived uncertainty to some incremental amount to a value that is still above zero.

Full protection framing is an example of pseudo-certainty because it provides assurance regarding a subset of relevant uncertainties. A reduction of the probability of an outcome has more importance when the outcome has initial certainty versus probability. However, it is objectively similar problems.

## Conclusion

The certainty and pseudo-certainty effects lead us to judgemental inconsistencies. The certainty effects makes us more apt to be interested in reducing the likelihood of certain events than uncertain one. Under pseudo certainty effect, we are more likely to favour options that assure us certainty than those that only reduce uncertainty.

Rationally thinking, any constant reduction of risk in an uncertain situation should have the same value for decision maker. For instance: the risk of cancer from 20% to 10% and from 0% to 10%. Manipulation in pseudo-certainty can be applied to many designs of communication about medical treatments, personal insurance, corporate liability protection and other.