Subjective Probability: A Judgment of Representativeness

Summary Report-  

 Subjective Probability: A Judgment of Representativeness

                                                                                                      By Daniel Kahneman & Amos Tversky

Heuristics are simple, efficient rules which people often use to form judgments and make decisions. They are mental shortcuts that usually involve focusing on one aspect of a complex problem and ignoring others. These rules work well under most circumstances, but they can lead to systematic deviations from logic, probability or rational choice theory.  These have been shown to affect people's choices in situations like valuing a house, deciding the outcome of a legal case, or making an investment decision. Heuristics usually govern automatic, intuitive judgments but can also be used as deliberate mental strategies when working from limited information.

This paper explains a heuristic-representativeness-according to which the subjective probability of an event, or a sample, is determined by the degree to which it - (i) is similar in essential characteristics to its parent population; and (ii) reflects the salient features of the process by which it is generated. This heuristic is explicated in a series of empirical examples demonstrating predictable and systematic errors in the evaluation of uncertain events. In particular, since sample size does not represent any property of the population, it is expected to have little or no effect on judgment of likelihood. This prediction is confirmed in studies showing that subjective sampling distributions and posterior probability judgments are determined by the most salient characteristic of the sample.

Representativeness heuristic.

The representativeness heuristic is used when making judgments about the probability of an event under uncertainty.  It is one of a group of heuristics (simple rules governing judgment or decision-making) proposed by psychologists Amos Tversky and Daniel Kahneman in the early 1970s. Heuristics are described as judgmental shortcuts that generally get us where they need to go – and quickly – but at the cost of occasionally sending us off course. Heuristics are useful because they use effort-reduction and simplification in decision-making.

Representativeness, like perceptual similarities is easier to access than to characterize. In this paper they did not scale representativeness. Instead they consider the events where the ordering the events according to representativeness appears obvious and show that people judge the more representative event to be more likely , whether it is or not.  Although representativeness may play major role in many varieties of probability judgments e.g. political forecasting and clinical judgment, the present treatment is restricted to essentially repetitive situations where objective probabilities are readily computable.  

When using the representativeness heuristic, people make judgments about probability based on how well it represents, or is similar to a stereotype they are familiar with. The closer it resembles the stereotype, the higher they consider the probability to be that it fits the stereotype. This heuristic is usually used when one is asked to judge the probability that an object or event belongs to a specific class or process.

Tversky and Kahneman defined representativeness as "the degree to which an event (i) is similar in essential characteristics to its parent population, and (ii) reflects the salient features of the process by which it is generated". When people rely on representativeness to make judgments, they are likely to judge wrongly because the fact that something is more representative does not actually make it more likely. The representativeness heuristic is simply described as assessing similarity of objects and organizing them based around the category prototype (e.g., like goes with like, and causes and effects should resemble each other).  This heuristic is used because it is an easy computation. The problem is that people overestimate its ability to accurately predict the likelihood of an event. Thus, it can result in neglect of relevant base rates and other cognitive biases.

Determinants of Representativeness

Certain factors of the judgment or decision to be made make the use of the representativeness heuristic more likely. In this section they discuss the characteristics of samples, or events, that make them representative, and demonstrate their effects on subjective probability. First, they describe some of the features that determine the similarity of a sample to its parent population. Then, they turn to the analysis of the determinants of apparent randomness.        

i. Similarity of sample to population

When judging the representativeness of a new stimulus/event, people usually pay attention to the degree of similarity between the stimulus/event and a standard/process. It is also important that those features be salient. Nilsson, Juslin, and Olsson (2008) found this to be influenced by the exemplar account of memory (concrete examples of a category are stored in memory) so that new instances were classed as representative if highly similar to a category as well as if frequently encountered. Several examples of similarity have been described in the representativeness heuristic literature. Research has focused on medical beliefs. People often believe that medical symptoms should resemble their causes or treatments. For example, people have long believed that ulcers were caused by stress, due to the representativeness heuristic, when in fact bacteria cause ulcers. In a similar line of thinking, in some alternative medicine beliefs patients have been encouraged to eat organ meat that corresponds to their medical disorder. Use of the representativeness heuristic can be seen in even simpler beliefs, such as the belief that eating fatty foods makes one fat. Even physicians may be swayed by the representativeness heuristic when judging similarity, in diagnoses, for example. The researcher found that clinicians use the representativeness heuristic in making diagnoses by judging how similar patients are to the stereotypical or prototypical patient with that disorder.

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