HEURISTIC REASONING – OR TAKING
A SHORT CUT
Thinking, understanding and decision-making take place in the real world, where there are usually time pressures and rarely a full range of information available to support a complete appraisal of the problem at hand.
For instance, suppose you are buying a new washing machine. A good basis for the decision might include comparative data on reliability, ease of servicing, servicing and repair costs, ease of use, even noise levels during operation. The list could go on and on. Although sometimes data of this sort might be available, and sometimes it might be published in magazines, it is more likely that you will have to cut corners. In other words, you might not be able to obtain a machine that fulfils all of your desirable features, but you will instead settle for the closest that is available. Kahneman, Slovic and Tversky (1982) popularized the term heuristic reasoning for thinking and decision making that involves these types of short cuts. They also suggested that these short cuts are so common that they should be considered part of the machinery of thought itself.
Availability
Perhaps the simplest kind of heuristic reasoning is availability. The availability heuristic is a method of estimating the likelihood of something based on how easily it comes to mind. For instance, we might assess the divorce rate by thinking of its prevalence amongst people we know personally. Or when buying a car, we might estimate reliability from comments made by acquaintances and colleagues. Because there will generally be a correspondence between what comes to mind easily and the likelihood of the underlying event, this heuristic can be useful. Kahneman et al. (1982) point to two mechanisms that come under the availability rubric: ease of recalling relevant instances and ease of constructing representations. For instance, someone’s estimate of how many flower names they know will directly depend on how many they can think of in a short time – say, two minutes (Tversky & Kahneman, 1973). In this case, there is generally a good correspondence between initial rate of retrieval and the total number known. But this is not always the case. For instance, it is easier to recall the names of famous people than ordinary people. So if participants hear lists of names containing equal numbers of famous and non-famous names, they ill typically believe that there are more famous people on the list than ordinary ones (Tversky & Kahneman, 1973). Here, the heuristic leads to a biased outcome. Another example of bias occurs through the construction of representations. Consider the following problem: A group of ten people want to form a committee with only two people in it. How many possible committees are there? Now try this: A group of ten people want to form a committee with eight people in it. How many possible committees are there? Most people produce a higher figure for the first question than for the second, even though they are actually equivalent questions (because 8 + 2 = 10, so for every committee of 2 that is formed there is an equivalent committee of 8 formed from among the same group of 10 people). Tversky and ahneman argue that this is because it is easier to imagine several committees of two than several committees of eight. (This seems reasonable if we suppose that it is easier to form and manipulate a mental model with two rather than eight tokens in it.) The availability heuristic has been used to explain many, many phenomena. In risk perception, for example, people tend to overestimate car accidents, tornadoes and homicide as causes of death, and underestimate death from complications due to diabetes, stroke and smallpox vaccination. Furthermore, studies show a good correlation between the prevalence of these events in news reports (availability) and estimated likelihood as a cause of personal death (Slovic, Fischhoff & Lichtenstein, 1979). Social psychology research has established that individuals tend to think that they initiated arguments with significant others more than 50 per cent of the time, and that they did more than 50 per cent of the work in domestic situations. This applies to both partners! It s argued that this is because we each have ready access to information about our own contributions in these situations, so we are more likely to register and remember these than our partner’s contributions (because of the higher availability of the former) (Ross, 1981; Ross & Sicoly, 1979).
[Daniel Kahneman (1934– ) has conducted highly influential work over the last several decades into human reasoning, specifically regarding the role of heuristics (i.e. reasoning short cuts, using strategies that generally work but are not guaranteed to work). To a large extent, heuristic reasoning overlaps considerably with the everyday idea of intuition. Kahneman and colleagues have suggested that these heuristic short cuts are so common that they should be considered part of the machinery of thought itself. For example, the availability heuristic is a method of estimating the likelihood of something based on how easily it comes to mind. The representativeness heuristic is based on the principle that we can estimate the likelihood of something by seeing how well it fits a prototype of which it may be an exemplar. For his body of work investigating human judgement and decision-making under conditions of uncertainty, Kahneman was awarded the Nobel Prize in 2002.]
Representativeness
This heuristic is based on the principle that we can estimate the likelihood of something by seeing how well it fits a prototype of which it may be an exemplar. For instance, if you are trying to decide whether a person is a Christian, the more properties they have that fit your model of how Christians behave, and the fewer they have that do not fit, the more confident you would be that the person is a Christian. Like availability, representativeness is a double-edged weapon – it can lead to fallacious reasoning. Many of the examples Kahneman and Tversky (1972) give are about reasoning with distributions, such as the ‘Exact Birth Order Problem’: All families of six children in a city were surveyed. In 72 families, the exact order of boys and girls was GBGBBG. What is your estimate of the number of families found in which the exact order was BGBBBB?
The majority of participants thought that the first sequence was much more likely. In fact, the two orders are almost equally likely because, on any occasion, either a boy or a girl could be born with approximately equal probability. Both of these orders fulfil this requirement. From an intuitive viewpoint, the first seems much more likely because there is an equal number of girls and boys. But the equal number gives the impression of being more likely seemingly because it is judged to be more representative.
The impact of representativeness on exact order judgements can be seen even more clearly with the following: Which is more likely to occur: GGGBBB or GBBGBG? Most people think it is the latter, because it is more ‘randomlooking’ than the former. Yet on a random draw basis, both examples are equally likely. To make this clearer, draw out all the possible sequences that could occur using three boys and three girls. Although the sequences are all equally likely, there are more ‘mixed up’ ones like the second one above, and only one other (BBBGGG) that looks more extreme (and therefore less representative). Yet these possibilities are all equally likely. Another example shows how representativeness can apparently obscure the use of what is termed base-rate information. Consider the following scenario: 100 people, comprising 70 lawyers and 30 engineers, apply for a job. One of the applicants, Dick, is a 30-year-old man, married with no children. A man of high ability and motivation, he is likely to be quite succes ful in his field. He is well liked by his colleagues.
Is Dick more likely to be an engineer, a lawyer or equally likely to be either? Kahneman and Tversky (1972) found that the predominant answer given was ‘equally likely’ because the information does not discriminate between the two. Yet the prior odds are 70:30 in favour of Dick being a lawyer, so this should be the answer in the case where there is insufficient extra evidence in the description. In such cases, it is as if the representativeness of the description dominates the thinking of participants – a typical illustration of what is widely known as the ‘fallacy of ignoring the base-rate’.
A SHORT CUT
Thinking, understanding and decision-making take place in the real world, where there are usually time pressures and rarely a full range of information available to support a complete appraisal of the problem at hand.
For instance, suppose you are buying a new washing machine. A good basis for the decision might include comparative data on reliability, ease of servicing, servicing and repair costs, ease of use, even noise levels during operation. The list could go on and on. Although sometimes data of this sort might be available, and sometimes it might be published in magazines, it is more likely that you will have to cut corners. In other words, you might not be able to obtain a machine that fulfils all of your desirable features, but you will instead settle for the closest that is available. Kahneman, Slovic and Tversky (1982) popularized the term heuristic reasoning for thinking and decision making that involves these types of short cuts. They also suggested that these short cuts are so common that they should be considered part of the machinery of thought itself.
Availability
Perhaps the simplest kind of heuristic reasoning is availability. The availability heuristic is a method of estimating the likelihood of something based on how easily it comes to mind. For instance, we might assess the divorce rate by thinking of its prevalence amongst people we know personally. Or when buying a car, we might estimate reliability from comments made by acquaintances and colleagues. Because there will generally be a correspondence between what comes to mind easily and the likelihood of the underlying event, this heuristic can be useful. Kahneman et al. (1982) point to two mechanisms that come under the availability rubric: ease of recalling relevant instances and ease of constructing representations. For instance, someone’s estimate of how many flower names they know will directly depend on how many they can think of in a short time – say, two minutes (Tversky & Kahneman, 1973). In this case, there is generally a good correspondence between initial rate of retrieval and the total number known. But this is not always the case. For instance, it is easier to recall the names of famous people than ordinary people. So if participants hear lists of names containing equal numbers of famous and non-famous names, they ill typically believe that there are more famous people on the list than ordinary ones (Tversky & Kahneman, 1973). Here, the heuristic leads to a biased outcome. Another example of bias occurs through the construction of representations. Consider the following problem: A group of ten people want to form a committee with only two people in it. How many possible committees are there? Now try this: A group of ten people want to form a committee with eight people in it. How many possible committees are there? Most people produce a higher figure for the first question than for the second, even though they are actually equivalent questions (because 8 + 2 = 10, so for every committee of 2 that is formed there is an equivalent committee of 8 formed from among the same group of 10 people). Tversky and ahneman argue that this is because it is easier to imagine several committees of two than several committees of eight. (This seems reasonable if we suppose that it is easier to form and manipulate a mental model with two rather than eight tokens in it.) The availability heuristic has been used to explain many, many phenomena. In risk perception, for example, people tend to overestimate car accidents, tornadoes and homicide as causes of death, and underestimate death from complications due to diabetes, stroke and smallpox vaccination. Furthermore, studies show a good correlation between the prevalence of these events in news reports (availability) and estimated likelihood as a cause of personal death (Slovic, Fischhoff & Lichtenstein, 1979). Social psychology research has established that individuals tend to think that they initiated arguments with significant others more than 50 per cent of the time, and that they did more than 50 per cent of the work in domestic situations. This applies to both partners! It s argued that this is because we each have ready access to information about our own contributions in these situations, so we are more likely to register and remember these than our partner’s contributions (because of the higher availability of the former) (Ross, 1981; Ross & Sicoly, 1979).
[Daniel Kahneman (1934– ) has conducted highly influential work over the last several decades into human reasoning, specifically regarding the role of heuristics (i.e. reasoning short cuts, using strategies that generally work but are not guaranteed to work). To a large extent, heuristic reasoning overlaps considerably with the everyday idea of intuition. Kahneman and colleagues have suggested that these heuristic short cuts are so common that they should be considered part of the machinery of thought itself. For example, the availability heuristic is a method of estimating the likelihood of something based on how easily it comes to mind. The representativeness heuristic is based on the principle that we can estimate the likelihood of something by seeing how well it fits a prototype of which it may be an exemplar. For his body of work investigating human judgement and decision-making under conditions of uncertainty, Kahneman was awarded the Nobel Prize in 2002.]
Representativeness
This heuristic is based on the principle that we can estimate the likelihood of something by seeing how well it fits a prototype of which it may be an exemplar. For instance, if you are trying to decide whether a person is a Christian, the more properties they have that fit your model of how Christians behave, and the fewer they have that do not fit, the more confident you would be that the person is a Christian. Like availability, representativeness is a double-edged weapon – it can lead to fallacious reasoning. Many of the examples Kahneman and Tversky (1972) give are about reasoning with distributions, such as the ‘Exact Birth Order Problem’: All families of six children in a city were surveyed. In 72 families, the exact order of boys and girls was GBGBBG. What is your estimate of the number of families found in which the exact order was BGBBBB?
The majority of participants thought that the first sequence was much more likely. In fact, the two orders are almost equally likely because, on any occasion, either a boy or a girl could be born with approximately equal probability. Both of these orders fulfil this requirement. From an intuitive viewpoint, the first seems much more likely because there is an equal number of girls and boys. But the equal number gives the impression of being more likely seemingly because it is judged to be more representative.
The impact of representativeness on exact order judgements can be seen even more clearly with the following: Which is more likely to occur: GGGBBB or GBBGBG? Most people think it is the latter, because it is more ‘randomlooking’ than the former. Yet on a random draw basis, both examples are equally likely. To make this clearer, draw out all the possible sequences that could occur using three boys and three girls. Although the sequences are all equally likely, there are more ‘mixed up’ ones like the second one above, and only one other (BBBGGG) that looks more extreme (and therefore less representative). Yet these possibilities are all equally likely. Another example shows how representativeness can apparently obscure the use of what is termed base-rate information. Consider the following scenario: 100 people, comprising 70 lawyers and 30 engineers, apply for a job. One of the applicants, Dick, is a 30-year-old man, married with no children. A man of high ability and motivation, he is likely to be quite succes ful in his field. He is well liked by his colleagues.
Is Dick more likely to be an engineer, a lawyer or equally likely to be either? Kahneman and Tversky (1972) found that the predominant answer given was ‘equally likely’ because the information does not discriminate between the two. Yet the prior odds are 70:30 in favour of Dick being a lawyer, so this should be the answer in the case where there is insufficient extra evidence in the description. In such cases, it is as if the representativeness of the description dominates the thinking of participants – a typical illustration of what is widely known as the ‘fallacy of ignoring the base-rate’.
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