Heuristics, a method of coping with the large

Heuristics, in psychology, can be
defined as mental shortcuts, which enable people to solve problems and allow
them to problem solve and make judgements at a quick and efficient rate (Lewis,
2018). We use heuristics as a method of coping with the large quantities of
information we encounter, and these rule-of-thumb strategies allow us to
simplify decision-making, rather than spending time over-analysing details. There
are a number of heuristics that we use on a day-to-day basis, a type being fast-and-frugal
heuristics, which are task-specific decision strategies and are useful for
simplifying the making of difficult decisions (Luce, 2000). Gigerenzer proposed
the adaptive toolbox, which is encompassed by three heuristic strategies – the
minimalist, take the last and the take the best heuristic. These heuristics can
be used to make judgements about the population of two cities, even if we were
only given limited information about them. However, although fast, these mental
shortcuts can be criticised. This essay will discuss many heuristics and how
they could be used to complete the previously discussed task, along with how
Gigerenzer’s adaptive toolbox is influenced by these heuristics.


The first
heuristic that could be used is the representativeness heuristic, described in
Tversky & Kahneman (1971) as a tool that can be used while making a
judgement about the probability of an event when an individual is uncertain.
This heuristic involves referring to the most representative mental stereotype
when presented with the present situation and comparing the two. In reference
to using this heuristic to make judgments and comparisons of the two cities –
the list of features could include that one of the cities has more modern
infrastructure. Most individuals would assume that cities with more modern
infrastructure tend to be more developed and therefore it is likely that it
will be inhabited by a large amount of people, so they could assume that that
particular city is larger than the other. This method of decision making was
demonstrated in Tverksy & Kahneman (1973), in which participants were given
vague personality descriptions of individuals, and asked the probability of
whether they were a lawyer or an engineer. Despite two different conditions
with different ratios of the professions, people based their judgements of what
they were more familiar with and whether the description was representative of
the two stereotypes. Although this method is quick and efficient and relies on
using previous knowledge to make a judgement, it does come with disadvantages.
In relation to the most recent Tverksy & Kahneman study discussed; this
heuristic does not take into account prior probability/base rate frequency.
This is why which of the two professions is more prevalent in society was not
taken into account, simply which description was more representative. Furthermore,
when observing how representative a feature may be, the size of the sample is
ignored, and just the description of the sample is. This has been criticised in
Dawes (1975) as leading to cognitive biases and has brought about the idea of
conservatism – the underestimation of the impact of evidence in the
representativeness heuristic.

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heuristic that can be used in this situation is the availability heuristic,
which is defined as the ease of which to bring forward an example of an event
(Folkes, 1988). This is a judgement heuristic, and relies on using the
immediate examples that come to your mind when presented with a situation. In
reference to the title question, when comparing the two cities, you may refer
to the immediate example of your own home town, as it is probably the example
of a city that is most available to you. Therefore, if one city is described to
having similar features to the one you live in, then you will describe that
city as having a population similar to your own. When using this heuristic
individuals base their judgements on information they see as being of more
importance, as other information is not as easily recalled, which is shown in
Khanman & Tversky (1973). In this study, participants were asked the
probability of a word would begin with the letter k or the third letter would
be k if the word was chosen at random. Participants believed it was more likely
that the word with begin with k, as thinking of letters with k as the third
letter would use up more cognitive resources. This method also comes with
criticisms, as it can causes illusory correlation – a bias in judgement of
frequency that two events occur, which is based on the strength of the bond
between them. This could be an issue when comparing populations, as if a city
is described as being run-down, an individual may judge it as having a high
level of unemployment when that may not be the case, which is supported by
Chapman & Chapman (1969). Another disadvantage to using this heuristic is
that there could be biases due to some examples being more easily retrieved
than others (Richards, 1967). This could occur in this situation if one city is
described as having features similar to a city you are familiar with, then you
will have poorer judgement when judging the other one as you are not as


The third heuristic I will discuss is
anchoring, which can be defined as relying largely on the first piece of
information you are given and making your judgement based on that. That initial
piece of information will be focused on, and thus all further judgements will
be consequently impacted from that. In the comparison of two cities, a feature
of one city may be that it is rural; if using the anchoring heuristic, the
individual will focus on this bit of information – it will be the anchor. After
this, the availability heuristic will then be used to gather any information
they can find regarding rural cities, and this will influence the judgement of
other features. Price et al., (2015) supports this, investigating how anchors
inclined how confident a participant would be when using anchors. When
predicting an outcome, those that had anchor exposure had a higher level of
correct predictions, therefore supporting the notion that anchoring can be
positive in regard to decision making.


All three of these concepts have had
a significant influence on the heuristics proposed by Gigerenzer. The first
heuristic he proposed was the minimalist heuristic. This encourages judgement
by drawing available cues based on relevant features. When examining two
features, if one of these has a negative value, then the more positive value
will be chosen. Once again relating to the title question, one of the cities
may be described as having very accessible transport, whereas that is not
mentioned in the features of the other. If the minimalist heuristic is used,
the availability method will be used bringing any relevant information to mind,
and conclude that the city with accessible transport has a higher population,
as this would be the positive option to choose, as larger populations require
more public transport. As the other city has not triggered an association
regarding information, it is considered the negative option and is not
prioritised. In Sperber et al (1995), participants were required to explain why
they had chosen opposing scenarios out of a selection. This evidence supports
the existence of the minimalist heuristic, as the participants appeared to make
their judgements based on whether they had any positive memories relating to
the scenario in question. Obviously, this heuristic can lead to decisions
accurately, but this relies on previous information being held by the
individual, which is not always available. This can lead to cognitive biases
such as salience, which relating to the question would be if an individual had
recently visited a city that is similar to Also affected by salience, if
visited a city like that recently more available judgements, rather than city
with a description that is strange to them. Gigerenzer (1999) also provides
disadvantages to the minimalist method of decision making, in which
participants were required to predict whether two cities had a higher
population, like the question title. The cities were selected in a fashion so
that the ones that were more easily recognised had higher populations, meaning
available resources could be used to isolate which of the cities had the
largest population. This was done by manipulating variables however, therefore
we cannot reflect this in a real-life situation as we are not presented with
decision making that is within a controlled situation.


Another heuristic that was brought
forward by Gigerenzer is the take the last heuristic. What makes this heuristic
differ from the minimalist strategy is that the anchoring method is used,
rather than the availability heuristic, therefore the strategy that will be
used for decision making will simply be the one that most recently worked for
them. When two cities are being looked at comparatively, one may conclude that
a city has a smaller population because it has a low tourist rate. When using
the take the last heuristic, if using the method was successful previously,
they will use it again. Luchin (1942) provided evidence for this, as when
participants were told to measure how much water was in 3 different jars, they
relied on previous methods that were successful, despite easier, more efficient
options being available. As take the last is a fast and frugal heuristic, it
also comes with the negatives of such. There is not a continuum when
considering decision making – if a criterion was successful previously that
doesn’t mean that it will be again, which is known as conformation bias. This
is a theory that was shown in Watson (1960), in which triples of numbers were
presented, and participants had to identify rules relating to those, but they
were given one of the three. It was discovered that they relied too heavily on
the rule they were shown and thus were not successful at identify the correct


As we can infer from previous
discussion, there are a lot of negatives associated with the two Gigerenzer
heuristics already mentioned – however there is another, the take the best
heuristic. It reflects the representativeness heuristic massively, as probability
is a large factor in decision making. When comparing the populations of two
cities, if the individual is previously aware that cities with high employment
rates have large populations then they are likely to make their judgements
based on this already known information. Goldstein & Gigerenzer (1996)
supports this, with a task almost identical to the title scenario. 90% of the
decisions made were based on whether the city was recognised; but take the best
was the most useful heuristic used. Therefore, this infers that out of the
three, take the best is the most accurate heuristic in decision making. It is
not a perfect model however, and Hutchinson (2005) found conflicting evidence
to the reliability of take the best. When given the same city experiment as
mentioned previously, take the best was the most successful heuristic used.
When this was repeated with more participants however, take the best was not
the heuristic with the highest accuracy, so perhaps prior knowledge is required
to make an accurate decision.


In conclusion, Giferenzer’s three
heuristics stemmed from the already conceptualised representativeness,
availability and anchoring heuristics, which can all be used to make decisions
given limited information. The minimalist heuristic mirrors the availability
heuristic as it is based on already stored information, the take the last
heuristic is based on the anchoring method, and the take the best heuristic is
based on the representativeness method. These methods share a lot of the same
criticisms, but overall are useful for quick decision making, even with
cognitive biases. Evidence such as Goldstein & Gigerenzer (1996) supports
the notion that the take the best heuristic seems to be the most successful in
relation to the title question as it examines the information before making a
decision. However, as with any fast and frugal heuristic, there is no way to
assure accuracy, due to the quick nature they possess.


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