Adaptive bias

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.

Haselton & 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