Introduction

In this chapter, we will be focusing on judgement and decision making. The two areas of research are similar (and also resemble human reasoning in some ways, see Chapter 16), but can be distinguished. In essence, judgement research is concerned with the processes used in drawing conclusions from the knowledge and evidence available to us. In contrast, decision making is concerned with choosing among options, and can involve choices of personal significance.

Research on judgement and decision making has been concerned largely with statistical judgements. For example, many judgements are based on uncertain or ambiguous information, and so we necessarily deal with probabilities rather than with certainties. Most of the early research on judgements of utility and intuitive statistics was led by theories of optimal decision making and statistics developed by philosophers and economists (e.g., Coombs, Dawes, & Tversky, 1970; Edwards, 1954). These normative theories describe how one should go about determining the best possible course of action, given one's knowledge about the world and what one wants. They describe, given certain strong assumptions, how we can make optimal judgements and decisions.

An important reason why decision making is often difficult is because we live in a world full of uncertainty. For example, you might want to choose a career in which you would be well paid. However, the fact that it is well paid now does not guarantee that that will still be the case in 20 years' time. As a result of such uncertanties, accounts of decision making (whether involving optimal decision making or not) typically emphasise the role of subjective probabilities.

An approach based on optimal judgements and decisions does not generally apply well to human reasoning. There are many reasons for this. First, people often do not have access to all the information needed to make optimal judgements or decisions. Second, they may exaggerate the importance of some of the available information and minimise the importance of other information. Third, individuals who are very anxious or depressed are unlikely to make optimal decisions, because their emotional state makes it difficult for them to think clearly about the various options available to them. However, emotional factors can readily be incorporated into theories of optimal decision making. For example, if someone is deciding whether or not to sell their old car, it is entirely appropriate for theories of decision making to take full account of any emotional attachment the owner has to his or her car.

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