Error Management Theory
According to Error Management Theory, when making decisions under conditions of uncertainty, two kinds of errors need to be taken into account—"false positives", i.e. deciding that a risk or benefit exists when it does not, and "false negatives", i.e. failing to notice a risk or benefit that exists. False positives are also commonly called "Type I errors", and false negatives are called "Type II errors".
Where the cost or impact of a Type I error is much greater than the cost of a Type II error (e.g. the water is safe to drink), it can be worthwhile to bias the decision-making system towards making fewer Type I errors, i.e. making it less likely to conclude that a particular situation exists. This by definition would also increase the number of Type II errors. Conversely, where a false positive is much less costly than a false negative (blood tests, smoke detectors), it makes sense to bias the system towards maximising the probability that a particular (very costly) situation will be recognised, even if this often leads to the (relatively un-costly) event of noticing something that is not actually there. This situation is exhibited in modern airport screening—maximising the probability of preventing a high-cost terrorist event results in frequent, low-cost screening hassles for harmless travelers who represent a minimal threat.
Martie G. Haselton and David M. Buss (2003) state that cognitive bias can be expected to have developed in humans for cognitive tasks where:
- decision-making is complicated by a significant signal-detection problem (i.e. when there is uncertainty)
- the solution to the particular kind of decision-making problem has had a recurrent effect on survival and fitness throughout evolutionary history
- the costs of a "false positive" or "false negative" error dramatically outweighs the cost of the alternative type of error
Read more about this topic: Adaptive Bias
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