Advertisement

Heuristics, Biases and Methods for Debiasing

Part of the Contributions to Economics book series (CE)

Prediction is involved in many recurrent tasks individuals are confronted with and can be seen as the result of the interaction between “judgement, intuition, and educated guesswork.” Even when forecasts rely on mathematical methods the central role of intuition cannot be denied as it supervises e.g. the choice of variables that belong to the model, their initial value and their functional specification.

This chapter deepens a central aspect for human cognition and problem-solving, namely that individuals make use of bounded rational heuristics for taking decisions under uncertainty. Heuristics are simplified procedures for assessing probabilities. They are based on rules of thumb. They rely on mental clues which selectively orient the search process and enable the individual to reach her goals when time, informational and computational capabilities are constrained.

Although in some cases bounded rational heuristics can be made responsible for the sub-optimality of outcomes and for behavioural biases. In some other cases it represents an essential support for carrying on inference when complexity overloads the individual cognitive and computational capabilities. It enables the individual to reach better solutions than otherwise.

There are mainly two different approaches to subjective judgement and bounded rational heuristics, namely the “heuristics and biases” approach, pioneered by Kahneman and Tversky, and the “ecological rationality” approach, with Gigerenzer as one of its most influential proponents.

Keywords

Subjective Judgement Individual Judgement Hindsight Bias Ecological Rationality Rational Heuristic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Physica-Verlag Heidelberg 2009

Personalised recommendations