The heuristics and biases of judgement under uncertainty. Gigerenze's normative critique of Kahneman and Tversky.

This week our group has been given to read and discus article ‘A reply to Kahneman and Tversky (1996) by Gigerenzer, On Narrow norms and Vague Heuristics’. Thru the discussion of the article we had to form our understanding about the cognitive processes that produce both valid and invalid judgements.

Kahneman and Tversky (1974) described three heuristics that are employed in making judgments under uncertainty; (1) representativeness,  which  is  usually  employed  when  people  are  asked  to  judge  the  probability that  an  object  o r   event  A  belongs  to class or  process  B; (2)availability of  in- stances o r  scenarios, which is  often employed  when  people  are  asked  to  assess the  frequency of  a class or the plausibility  of  a  particular  development;  and (3)  adjustment  from  a n  anchor,  which is usually employed in numerical prediction  when  a  relevant  value  is  available ( Kahneman and Tversky, 1974).  They argued that these  heuristics  are  highly  economical and  usually  effective,  but  they  lead  to systematic  and  predictable  errors.  A better  understanding  of  these  heuristics and  of  the  biases  could  improve judgments  and  decisions in  situations  of  uncertainty. However, Gigerenzer, in his reply to Kahneman and Tversky, argued that some of the biases identified are unstable, for example in some cases their degree can be reduced by asking questions in terms of frequencies rather than in terms of probabilities. Secondly, Gigerenzer argued that, because Kahneman and Tversky’s heuristics are formulated by means of vague, l terms like “representativeness”, the appeal to these heuristics as generators of biases has limited explanatory power. Thirdly, he argued that it may be inappropriate to characterize some of the biases identified by Kahneman and Tversky as “errors” or “fallacies” for three reasons. (a) According to frequentists, no norms are appropriate for single-case judgements, because single-case probabilities are meaningless. (b) Even if single-case probabilities make sense, they need not to be governed by statistical norms because norms are “content-blind” and can conflict with conversational norms. (c) In some cases conflicting statistical norms exist (“statistics does not speak with one voice”) (Vranas, 1999).

Gigerenzer did not neglect the fact that Kahnemans and Tversky’s work gave a big impact to the field of decision making. However, he states:“In place of plausible heuristics that explain everything and nothing- not even conditions that trigger one heuristic rather than another- we will need models that make surprising ( and falsifiable) predictions and that reveal the mental processes that explain both valid and invalid judgment” ( Gigerenzer, 1996). 

References

A. Tversky and D.Kahneman (1974). Judgemnt under Uncertainty: Heuristics and Biases. Science, New Series, Vol. 185, No. 4157. (Sep. 27, 1974), pp. 1124-1131.

Peter B.M. Vranas (1999). Gigerenzer's normative critique of Kahneman and Tversky. Department of Philosophy, The University of Michigam, 2215 Angel Hall, Ann Arbor.