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Decision makers often make snap judgments using fast-and-frugal decision rules called cognitive heuristics. Research into cognitive heuristics has been divided into two camps. One camp has emphasized the limitations and biases produced by the heuristics; another has focused on the accuracy of heuristics and their ecological validity. In this paper we investigate a heuristic proposed by the first camp, using the methods of the second. We investigate a subset of the representativeness heuristic we call the “similarity” heuristic, whereby decision makers who use it judge the likelihood that an instance is a member of one category rather than another by the degree to which it is similar to others in that category. We provide a mathematical model of the heuristic and test it experimentally in a trinomial environment. In this environment, the similarity heuristic turns out to be a reliable and accurate choice rule and both choice and response time data suggest it is also how choices are made. We conclude with a theoretical discussion of how our work fits in the broader “fast-and-frugal” heuristics program, and of the boundary conditions for the similarity heuristic. Copyright © 2009 John Wiley & Sons, Ltd.
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WP : 112 ISSN : 1749-3641 (online)
The Similarity Heuristic
Daniel Read
Durham Business School
Mill Hill Lane, Durham DH1 3LB, United Kingdom
Tel: +44 19 1334 5454 Fax: +44 19 1334 5201
Yael Grushka-Cockayne
London Business School
Regent’s Park, London NW1 4SA, United Kingdom
Tel: +44 20 7000 8837 Fax: +44 20 7000 7001
The Similarity Heuristic
Decision makers are often called on to make snap judgments using fast-and- frugal decision
rules called cognitive heuristics. Although early research into cognitive heuristics
emphasized their limitations, more recent research has focused on their high level of
accuracy. In this paper we investigate the performance a subset of the representativeness
heuristic which we call the similarity heuristic. Decision makers who use it judge the
likelihood that an instance is a member of one category rather than another by the degree to
which it is similar to others in that category. We provide a mathematical model of the
heuristic and test it experimentally in a trinomial environment. The similarity heuristic turns
out to be a reliable and accurate choice rule and both choice and response time data suggest it
is also how choices are made.
Keywords: heuristics and biases, fast-and-frugal heuristics, similarity, representative design,
base-rate neglect, Bayesian inference
A heuristic is a decision rule that provides an approximate solution to a problem that
either cannot be solved analytically or can only be solved at a great cost (Rozoff, 1964).
Cognitive heuristics are analogous ‘mental shortcuts’ for making choices and judgments. Two
familiar examples are the availability heuristic (judge an event frequency by the ease with
which instances of the event can be recalled; Kahneman and Tversky, 1973), and the
recognition heuristic (if you recognize only one item in a set, choose that one; Goldstein and
Gigerenzer, 2002). Cognitive heuristics work by means of what Kahneman and Frederick
(2002) call attribute substitution, by which a difficult or impossible judgment of one kind is
substituted with a related and easier judgment of another kind. The recognition heuristic, for
instance, substitutes the recognition of only a single option in a pair for the more costly
process of searching for, selecting and evaluating information about both options. A central
feature of cognitive heuristics is that while they are efficient in terms of time and processing
resources, they achieve this at some cost in accuracy or generality. As an example, when
events are highly memorable for reasons unrelated to frequency, the availability heuristic can
overestimate their probability.
Early research into cognitive heuristics emphasized how they could produce systematic
biases (Kahneman, Slovic & Tversky, 1982). Indeed, these biases were often the primary
evidence that the heuristic was being used. Later research has emphasized the adaptive nature
of heuristics, emphasizing their capacity to quickly and efficiently produce accurate
inferences and judgments (Gigerenzer & Todd and the ABC research group, 1999; Samuels,
Stich & Bishop, 2002). To use the term introduced by Gigerenzer and Goldstein (1996),
heuristics are ‘fast-and-frugal’: they allow accurate decisions to be made quickly using
relatively little information and processing capacity.
As Gilovich and Griffin (2003) observe, however, this new emphasis has not been
applied to the ‘classic’ heuristics first described by Kahneman and Tversky (1973). One
reason is that the two approaches to heuristics come from different research traditions that
have asked different questions, and adopted correspondingly different methods. The modal
question asked by the earliest researchers was ‘do people use heuristic X?’, while those in the
fast-and-frugal tradition started with ‘how good is heuristic X?’. These two questions are
answered using different research strategies. The first strategy is a form of what Brunswik
(1955) called a systematic design, the second related to what he called a representative
design. In a systematic design the stimuli are chosen to permit the efficient testing of
hypotheses; in the representative design the stimuli are literally a representative sample, in the
statistical sense, drawn from the domain to which the results are to be generalized (Dhami,
Hertwig & Hoffrage, 2004).
If misinterpreted, the use of a systematic design can exaggerate the importance of
atypical circumstances. The experimental conditions tested are usually chosen so that
different judgment or choice rules predict different outcomes, and since one of those rules is
usually the normatively optimal rule, and the purpose of the experiment is to show that a
different rule is in operation, the experiment invariably reveals behavior that deviates from the
normative rule. For instance, studies of the availability heuristic are designed to show that,
whenever using the heuristic will lead to systematic under- or over-estimation of event
frequency, this is what occurs. Many early observers concluded that such findings showed
evidence of systematic and almost pathological irrationality (e.g. Nisbett & Ross, 1980;
Piatelli-Palmarini, 1996; Plous, 1993; Sutherland, 1992). The extent of the irrationality
observed, however, may have been the result of the use of a systematic design, combined with
an interpretation of the results from using that design as being typical
. If the goal is to
measure how well a decision rule or heuristic performs, a more representative design should
be used
In this paper we investigate the representativeness heuristic, one of the classic
heuristics first described by Kahneman and Tversky (1972), who defined it as follows:
A person who follows this heuristic evaluates the probability of an uncertain event, or a
sample, by the degree to which it is: [i] similar in essential properties to its parent
population; and [ii] reflects the salient features of the process by which it is generated.
(Kahneman & Tversky, 1972 p. 431)
The heuristic has two parts, one based on the similarity between sample and population, the
other based on beliefs about the sampling process itself (Joram & Read, 1996). The focus in
this paper is on one aspect of Part [i], which we refer to as the similarity heuristic
, according
to which the judged similarity between an event and possible populations of events is
substituted for its posterior probability. An example of this substitution is found in responses
to the familiar “Linda” problem (Tversky & Kahneman, 1982). Because Linda is more similar
to a ‘feminist bank-teller’ than a mere ‘bank-teller,’ she is judged to be more likely to be a
feminist bank-teller (Shafir, Smith and Osherson, 1990).
An important study, using a systematic design, of what we call the similarity heuristic
was conducted by Bar-Hillel (1974). Her subjects made judgments about sets of three bar
charts like those in Figure 1, labeled L, M and R for left, middle and right. The Similarity
group judged whether M was more similar to L or R. The Likelihood of populations group was
told that M represented a sample that might have been drawn either from population L or R,
and judged which population M was more likely to come from, and the Likelihood of samples
group was told that M represented a population that might have generated either sample L or
R, and judged which sample was more likely to be generated from M. If the similarity
heuristic is used, all three judgments would coincide. Bar-Hillel systematically designed the
materials so that this coincidence could easily be observed. All the triples had the following
1. Every bar in M was midway in height between the bars of the same color in L and R.
2. The rank-order of the bar heights in M coincided with those in either L or R, but not
3. When M was interpreted as describing a population and L and R were interpreted as
samples, then the sample with same rank-order as M was the least probable.
4. Likewise, when L and R were interpreted as populations and M as a sample, then M was
less likely to be drawn from the population whose rank-order it matched.
In other words, the stimuli were systematically designed to ensure that, under both
interpretations of likelihood, the objective odds favored the same chart, which was not the
chart with the same rank-order as M. In Figure 1, sample M is more likely to be drawn from
population R, and sample R is more likely to be drawn from population M, although the rank-
order of the bar-heights in M is the same as that of L. Bar-Hillel correctly anticipated that
both similarity and likelihood judgments would be strongly influenced by rank-order.
—Figure 1 about here –
Although this study is very elegant, for our purposes it has two shortcomings, both
related to the fact that the stimuli were highly unusual
. First, the stimuli all had the same
atypical pattern, which may have suggested the use of judgment rules that would not have
been used otherwise. For instance, the rule ‘choose the one with the same rank-order’ was
easy to derive from the stimuli, and could then be applied to every case – in other words, the
attribute ‘rank-order’ rather than ‘similarity’ could have been substituted for likelihood.’
This possibility is enhanced by the presentation of stimuli as bar charts rather than as
disaggregated samples, and the use of lines to connect the bars. Both features make rank-
order extremely salient.
Moreover, the use of a systematic design means the study does not indicate how
accurate the similarity heuristic is relative to the optimal decision rule, even for bar charts
connected by lines. When the majority similarity judgment is used to predict the majority
choice in the Likelihood of Populations group, the error rate was 90%. But since only a tiny
proportion of cases actually meet the four conditions specified above, this number is
practically unrelated to the overall accuracy of the heuristic. Indeed, the fact that respondents
make errors in Bar-Hillel’s study is highly dependent on the precise choice of stimuli. In the
illustrative stimuli of Figure 1, if the bar heights in L are slightly changed to those indicated
by the dashed lines (a 5% shift from yellow to green), then the correct answer changes from L
to R (the probability that R is correct changes from .41 to .65).
One goal of the experiment described in this paper is to address the issues implied by
this analysis. First, we elicit choices and judgments of similarity in an environment in which
the relationship between sample and population varies randomly. Second, because we
examine a random sample of patterns in this environment, we are able to assess the efficiency
of the similarity heuristic. Our method was deliberately designed to find a point of contact
between the two traditions of research in heuristics the early tradition exemplified by
Kahneman and Tversky’s work, and the later tradition exemplified by the work of Gigerenzer
and Goldstein (1996). Our research shows there is no fundamental divide between these
traditions. As a first step, we describe a precise and testable model of the similarity heuristic.
A Model of the Similarity Heuristic
The similarity heuristic is a member of what is perhaps the broadest class of decision
rules, those in which the decision to act on (or to choose, or to guess) one hypothesis rather
than another is based on the relative value of a decision statistic computed for each
hypothesis. In the most basic version of this class, one hypothesis is chosen because the
decision statistic favors that hypothesis more than any other and, if two or more hypotheses
share the same maximum decision statistic, one is chosen using a tie-breaking procedure. In
the context of such models, a wide range of decision statistics have been proposed. Some of
these are objective relationships between the data and the hypotheses. Amongst these are the
likelihood, and the posterior probability computed from Bayes’ rule. These decision statistics
are particularly important because they constitute the theoretical benchmark for the
performance of a decision rule. Several other “objective” decision statistics are those
discussed recently by Nilsson, Olsson and Juslin (2005) in the context of probability
judgment. Indeed, two of these are operationalizations of ‘similarity’ based on Medin and
Schaffer’s (1978) context theory of learning, comprising an adaptation of one interpretation of
the representativeness heuristic originating in Kahneman and Frederick (2002), and the other
is their own exemplar-based model. The decision statistic can also be and indeed when
making choices typically is a subjective relationship between data and hypothesis.
Recognition is such a subjective relationship, where the recognition of an object can be used
as the basis for making a judgment such as ’the object is large.’ The feeling or judgment of
similarity between data and hypothesis is another subjective relationship, and the one we
focus on.
We will illustrate with a simple decision problem. Imagine you are birdwatching in a
marshy area in South England, and hear a song that might belong to the redshank, a rare bird
whose song can be confused with that of a common greenshank. You must decide whether or
not to wade into the marsh in hope of seeing a redshank. In normative terms, your problem is
whether the expected utility of searching (s) for the redshank is greater than that of not
searching (
p r d u s r p g d u s g p r d u s r p g d u s g
+ > +
, (1)
p r d
is the probability it is a redshank given the data (i.e., the song),
p g d
is the
probability it is a greenshank given the data,
u s r
is the utility of searching given that it is
a redshank, and so on. The probabilities are evaluated with Bayes’ rule, which draws on
likelihoods and the prior probability of each hypothesis,
p r
. If we substitute
the multiplication posterior = prior
likelihood into (1), and rearrange terms, the decision
rule is to search if
( ) ( )
( ) ( )
p r p d r u s g u s g
p g p d g u s r u s r
If all the utilities are equal, this reduces to searching if
p r p d r p g p d g
When using the similarity heuristic, the probabilities are replaced with similarity
s d r
s d g
: respectively, the similarity of the song to the redshank’s and
the greenshank’s. According to the similarity heuristic, you should search if
, , .
s d r s d g
That is, search if the birdsong you have just heard sounds (to you) more similar to that of the
redshank than that of the greenshank.
Within a given environment, the theoretical performance of a decision rule can be
estimated by computing the proportion of times it yields the correct answer, relative to the
same proportion for the optimal decision rule. We show how to estimate the performance of
the similarity heuristic against the Bayesian benchmark.
The decision model begins with a vector of decision statistics. For the similarity
heuristic, these statistics are judgments of similarity between the sample or case (the data) and
the population from which it might have been drawn
. For each of the n possible hypotheses,
, 1,..., ,
h i n
= and the data,
the decision maker generates a similarity judgment
, .
s d h
The set of n judgments form a similarity vector
1 2
[ , ,..., ]
j j nj
s s s
, where
ij j i
s s d h
Given the similarity vector, the next step is to pick out the maximum value from this
vector, which is done by assigning 1 if
takes the maximum value within
, and 0
otherwise, yielding the maximum similarity vector, with the same dimensions as
1 2
1 max
[ , .... ], where 0
j j nj ij
if s
ms ms ms ms
= =
In the simplest decision rule, h
is chosen if the maximum similarity vector contains
only a single value of 1 in the i-th position. If there is more than one such value, meaning that
more than one hypothesis ties for maximum decision statistic, each candidate hypothesis has
an equal chance of being chosen. The operation of this rule is implemented in the decision
1 2
[ , ,..., ], where
j j nj ij
i n
ds ds ds ds
= =
, (5)
The value of ds
, therefore, is the probability the choice rule will select hypothesis h
To calculate the probability that, for a given piece of evidence, this choice rule will
select the correct option, we pre-multiply the decision vector by the vector of corresponding
posterior probabilities (
) computed using Bayes’ rule:
( )
( )
( )
1 2
[ , .... ], where
i j i
j j nj ij i j
i j i
i n
p h p d h
pl pl pl pl p h d
p h p d h
= = =
Hence, given a set of hypotheses
, 1,..., ,
H h i n
= =
a choice rule
, prior probabilities p,
and evidence
, the accuracy of the choice rule, meaning the probability of making a correct
decision, is given by:
, , ,
j ij ij
i n
A H d pl ds
= =
j j j
s p pl ds
Next, we determine the performance of the choice rule given this hypothesis set and all
possible evidence that might occur. The evidence could be, for instance, every bird song that
might be heard. If the evidence is discrete (e.g., we might hear one of a finite number, m, of
possible sounds) the corresponding mean accuracy is:
( )
1 1
, , m n
j ij ij
j i
A H pd pl ds
= =
S p
, (8)
is the
n m
matrix representing the similarity of each piece of evidence to each
hypothesis, and
denotes the probability of obtaining evidence d
Just as the evidence can vary, so can the prior probabilities associated with a given set
of hypotheses. For instance, you might be in a situation where house sparrows are rare and
Spanish sparrows are common, or the reverse. To obtain the mean accuracy of the decision
rule we need to carry out the summation in Eq. (8) over the entire space of possible prior
probability distributions:
( ) ( )
1 1 1
, | , r m n
k k k
j ij ij
k j i
A H E Correct H pp pd pl ds
= = =
= =
, (9)
where H is the hypothesis set. The superscript k is added to the probabilities of obtaining
evidence d
, and to the posterior probabilities, to indicate that their values assume a specific
vector k of possible priors. The summation is carried out over the discrete set of prior
probability vectors, while multiplying by the probability of each prior probability vector,
denoted by
Note that while the operation of the similarity heuristic (although not its
performance) is independent of the distribution of prior probabilities, other rules need not be.
To model Bayes’ rule, for instance, ds
in Eq. (9) is replaced by
The above analysis focuses on deterministic choice rules. Although this is not the
place to develop theories of stochastic choice, they can be modeled by means of Monte Carlo
simulations of A(S,H) in which the vectors (e.g., s΄, ms΄, ds) are changed in the relevant
fashion. The role of error, for instance, can be modeled by laying a noise distribution over the
similarity vector (s΄), bias by systematically changing some values of the same vector, and a
trembling hand by random or even systematic changes to the decision vector (ds)
We illustrate our analysis and some of its implications with a simulation of the
likelihood heuristic, for which likelihoods,
p d h
, rather than similarity judgments, are the
decision statistic. Likelihoods are often taken as a proxy for similarity (Villejoubert &
Mandel, 2002; Nilsson, Olsson & Juslin, 2005) and the representativeness heuristic has even
been interpreted as being equivalent to the likelihood heuristic (Gigerenzer & Murray, 1987)
This analysis, therefore, can provide us with some expectations about when the similarity
heuristic is likely to perform well, and when it will perform poorly.
We consider a simple “binomial balls in urns” environment, such as the one adopted by
Grether (1980, 1992) and Camerer (1987). Imagine two urns (the hypotheses), denoted A and
B, each containing red and white balls in known proportions, denoted R
and R
, that is,
. The decision maker obtains a random sample of 5 balls drawn from an unseen
urn, and must then bet on whether it was drawn from urn A or B. Corresponding to each
possible sample, e.g.,
, and each hypothesis, there is a
ij j i
l p d h
, which can be computed from the binomial distribution. The
decision statistic vector is the vector of likelihoods
Aj Bj
l l
. Each such vector is
transformed, by means of Eq. (4) and (5), into a decision vector
, equal to
1 0 if
Aj Bj
l l
, 1 1
2 2 if
Aj Bj
l l
, and
0 1 if
Aj Bj
l l
. The probability of a correct
choice is obtained by pre-multiplying this decision vector by the posterior (Bayesian)
probability vector, to give
, , ,
A H d
l p
. The overall accuracy of the likelihood
, ,
L p
, is obtained by computing the probability of correct choices for each
sample, weighting each of these probabilities by the probability of obtaining the sample, and
then summing these weighted probabilities.
Table 1 shows the results of this analysis. The top row shows hypothesis sets, chosen to
represent a wide range of differences between populations. When
=the populations
have no distinguishing characteristics, while when
= they look very different. In
the identification of birds, a population of house sparrows and Spanish sparrows is close to
the first case, while house sparrows and sparrow hawks are like the second. The first column
in the table gives the prior probabilities for each urn,
p p
. The final row in the table
, ,
L p
, the average accuracy of the likelihood heuristic for each hypothesis set.
Because the likelihood heuristic, like the similarity heuristic, is not influenced by prior
probabilities this value is the same for all cells in its column. The values in the middle cells
show the incremental accuracy from using Bayes’ rule instead of the likelihood heuristic,
given each vector of priors, i.e.
, , , ,
B p L p
If the likelihood heuristic is a good proxy for the similarity heuristic, this analysis
indicates when the similarity heuristic is likely to perform well relative to Bayes’ rule, and
when it will perform poorly. These conditions were described formally by Edwards, Lindman
& Savage (1963). Roughly, they are that (a) the likelihoods strongly favor some set of
hypotheses; (b) the prior probabilities of these hypotheses are approximately equal; and (c)
the prior probabilities of other hypotheses never ‘enormously’ exceed the average value in
(b). In Table 1, condition (a) becomes increasingly applicable when moving from left to
right, and condition (b) when moving from bottom to top
. If we replace ‘likelihood’ in (a)
with ‘similarity’, then these are also the conditions in which the similarity heuristic is likely to
perform well. Likewise, when the conditions are not met, the similarity heuristic will do
-- Table 1 about here –
The Experiment
We investigated how well the similarity heuristic performs as a choice rule, and
whether people actually use it. In four experimental conditions, judgments or choices were
made about two populations and a single sample. Separate groups assessed the similarity of
the sample to the populations (a single estimate of
2 1
, ,
s d h s d h
), or chose the
population from which the sample was most likely to have been drawn.
The populations and samples were, like those in Bar-Hillel’s (1974) study, drawn from
a trinomial environment. Within this environment, we adopted a representative design. Two
populations (hypotheses) were generated using a random sampling procedure. The
populations used were the first 240 drawn using this procedure, which were randomly paired
with one another. A random sample was then drawn, with replacement, from one of the
populations in the pair, and the first sample drawn from each pair was the one used in the
experiment. The populations and samples were shown as separate elements arranged in
random order, as shown in Figure 2, and not in the form of summary statistics. We call each
set of populations and sample a triple.
-- Figure 2 about here --
We also considered the relationship between and the similarity heuristic and the use of
prior probability information. As discussed in section 2 above, the similarity heuristic makes
the same choice as Bayes’ rule whenever
1 2 1 1 2 2
sgn , , sgn
s d h s d h p h p d h p h p d h
. Since the similarity
heuristic disregards prior probabilities, it can lead to error when these are not
1 2
p h p h
. In the experiment we chose the population from which the sample
was chosen with a (virtual) throw of the dice, with prior probabilities of 1/6 and 5/6. One
choice group had knowledge of the prior probabilities, while another group did not.
We tested 160 participants, all members of the London School of Economics
community. In return for their participation, respondents received a £2 ($4) voucher for
The materials were based on 120 triples, each comprising two populations and one
sample of red, yellow and blue rectangles. The population generating algorithm was as
follows. First, we chose a number between 0 and 100 from a uniform distribution and
specified this as the number of blue rectangles (call it b); next, we generated a number
between 0 and (100-b) from a uniform distribution, and specified this as the number of green
rectangles (g). The number of yellow rectangles was therefore y=100-b-g. This yielded
populations with an average of 50 blue, 25 green and 25 yellow rectangles. In this way we
generated populations that were, on average, composed of a large number of blue rectangles.
This is analogous to many natural populations, in which the modal member is of one type, but
in which alternative types are also relatively abundant such as the ethnic composition of
European and North American cities, or bird populations pretty well everywhere.
For each question, we randomly generated a pair of populations, one of which was
assigned a high prior of 5/6, the other a low prior of 1/6. One population was chosen with
probability equal to its prior, and a sample of 25 rectangles was drawn (with replacement)
from this population. We used the first 120 stimuli sets generated, and they were presented in
the order generated.
Each respondent made judgments or choices for 30 triples, so the 120 triples comprised
four replications of the basic design. Within each replication, there were 10 participants in
each of four groups: The Similarity group were told nothing about the context, and simply
rated which of the larger sets of rectangles the small set was more similar to; the
Similarity/Population group made similarity judgments, this time with full knowledge that the
sets represented two populations and one sample; the Choice/No prior group guessed which
population the sample came from without knowledge of prior probabilities; and the
Choice/Prior group made the same choice but with this knowledge.
In all conditions, respondents first read an introductory screen which told them they
would be asked questions about ‘sets of rectangles’ and were shown an unlabelled example of
such sets. The instructions then diverged, depending on the experimental condition. Those in
the Similarity group read You will see two large sets and one small set like the following and
were shown a triple like that in Figure 2, with the three sets labeled, respectively, as Large Set
1, Small Set and Large Set 2. For each subsequent triple, they indicated which large set the
small set was more similar to, using a 9-point scale that ranged from Much more similar to
LS 1 to Much more similar to LS 2.
The instructions for the remaining groups included the following description of the task
We want you to consider the following procedure. First, we randomly generated two
populations of yellow, red and blue rectangles, which we call Population 1 and
Population 2. [Here the Choice/Prior group received information about prior
probabilities, as described later…]
Then we drew a sample of 25 rectangles from either Population 1 or Population 2.
[Here an example was shown, with the sets labeled as Population 1, Sample, and
Population 2.]
We drew the sample this way:
We randomly drew one rectangle and noted its color.
Then, we returned the rectangle to the population and drew another one, until we
had drawn 25 rectangles.
The sample could have been drawn from either Population 1 or Population 2.
Those in the Similarity/Population group then judged the similarity of the sample to
Population 1 or Population 2 using the 9-point scale, this time with the endpoints labeled
Much more similar to Population 1 and Much more similar to Population 2.
For those in the two choice groups the task was to indicate which population they
thought the sample came from. This was done by clicking one of two radio keys. The
instructions for the Choice/Prior group included the following information:
First [… as above].
Second, we rolled a die. If any number from 1 to 5 came up, we drew a sample of 25
rectangles from one population, while if the number 6 came up, we drew a sample of 25
rectangles from the other population.
In the following example we drew a sample from Population 1 if the numbers 1 to 5
came up, and drew a sample from Population 2 in the number 6 came up. [Here an
example was shown, with five dice faces above Population 1, and one above Population
2.] In the following example we drew a sample from Population 2 if the numbers 1 to 5
came up, and drew a sample from Population 1 if the number 6 came up. [Here the
example had one face above Population 1 and five above Population 2].
Once the population was chosen, we drew the sample this way [the standard
instructions followed, ending with …] The sample could have been drawn from
either Population 1 or Population 2, depending on the roll of the die.
For each triple in the Choice/prior group five dice faces were above the high prior population
and one face above the low prior population. The population number of the high prior
population was randomized.
In all conditions we recorded the time taken to make a choice or similarity judgment.
How reliable and consistent are judgments of similarity?
For similarity to be a reliable and valid basis for making probabilistic choices, there
must be some “common core” underlying the similarity judgments made by different people
and in different contexts. We measured this core by evaluating the inter-context and inter-
subject consistency of similarity judgments. There were four sets of 30 triples, each of which
received similarity judgments from 20 subjects, 10 each from the Similarity and
Similarity/Population groups. For each set of triples, we computed the mean inter-subject
correlation, both within and between experimental groups. These are shown in Table 2. As
can be seen, the mean inter-subject correlation was high (overall ranging from .71 to .79) and
there was no appreciable reduction in this value when attention was restricted to correlations
between subjects in different groups (ranging from .68 to .79).
-- Table 2 about here –
Given the high correlation between individual judgments, it is not surprising that the
correlation between the average similarity judgments for the 120 questions was extremely
high (.95). Moreover, even the mean similarity judgments in the two groups were almost
identical (5.06 vs 5.05), indicating that in both conditions the scale was used in the same way.
Finally, to anticipate the next section, the proportion of correct choices predicted by both
measures of similarity was almost identical. We conducted two logistic regressions, using
similarity ratings to predict the optimal Bayesian choice (we will call this BayesChoice). The
percentage of correct predictions was 86% for both Similarity groups, and these were
distributed almost identically across both Populations 1 and 2. Because the two similarity
measures are statistically interchangeable, we usually report results from combining the two
Overall, these analyses show that the judgments of similarity in both contexts contained
a substantial common core. We conclude, therefore, that similarity judgments are reliable. We
next turn to the question of their validity as a basis for probabilistic choice.
How accurate is the similarity heuristic?
We simulated the performance of the similarity heuristic in two ways. First, we
examined the correlation between the 9-point similarity rating and the option that would be
chosen by an optimal application of Bayes’ rule (denoted BayesChoice). Figure 3 shows the
proportion of times BayesChoice equals Population 2, for each level of Similarity. This
proportion increases monotonically in an S-shaped pattern, with virtually no Population 2
options predicted when Similarity=1 and almost 100% when Similarity=9. The correlation
between individual similarity judgments and BayesChoice is .76.
—Figure 3 about here --
We also compared the accuracy of the similarity heuristic with that achieved using
Bayes’ rule and the likelihood heuristic (BayesChoice and LKChoice). We simulated the
heuristic using the principles described previously: if the Similarity rating was less than 5
(i.e., implying
1 2
, ,
s d h s d h
>) then predict a choice of Population 1, if it is equal to 5 then
predict either population with probability of .5, otherwise predict Population 2 (we use
SimChoice to denote these individual simulated choices). Simchoice correctly predicted the
population from which the sample was drawn 86% of the time, compared to 94% for
LKChoice and 97% for BayesChoice.
Because similarity is a psychological judgment it is, unlike likelihoods and prior
probabilities, prone to error. To obtain a low-error judgment of similarity, we took the mean
similarity judgment for each question and applied our decision rule to this mean (i.e., if mean
Similarity < 5 choose Population 1, etc.). We denote these choices Simchoice/A (for
aggregate). Relative to Simchoice, using Simchoice/A increased the correlation between the
similarity heuristic and BayesChoice from .76 to .85, and increased overall accuracy from
86% to 92%.
In this context, therefore, the similarity heuristic achieves a high level of accuracy when
making probabilistic choices. But this does not demonstrate that people actually take the
opportunity to use similarity when making choices. This is what we evaluate next.
Do people use the similarity heuristic?
Similarity/Choice agreement. For each respondent in the two choice groups, we
compared the choices they made to the predictions of Simchoice/A. Figure 4 shows, for each
respondent in the Choice/No prior and Choice/Prior groups, the proportion of correct
predictions. There was an extremely good fit between actual and predicted choices: an
average of 89% predictions in the No prior group (Median 92%), and 86% in the Prior group
(Median 90%).
—Figure 4 about here --
This is not an irrefutable demonstration that people use the similarity heuristic, since
both choice and similarity judgments are also highly correlated with BayesChoice, leaving
open the possibility that the similarity/choice relationship might not be causal (i.e., similarity
determines choice), but merely due to the use of another choice rule (or rules) that is
correlated with both similarity and Bayes rule. We therefore conducted two additional
analyses to consider whether the similarity heuristic predicts choice beyond that predicted by
BayesChoice. First, we conducted a logistic regression in which individual choices (in both
the Choice/No prior and Choice/Prior conditions) was regressed on the mean Similarity
rating, the normalized likelihood ratio (NLKR) defined as
( )
p d h
p d h
+, and the prior
probability of Population 2. The model was chosen using a forward selection procedure
(probability for entry = .10., for removal = .15). In both analyses, mean Similarity was the
most significant predictor in the final model. The logits (log odds) for the final models were:
Choice/No-prior: 4.03 – 0.63 Similarity – 2.32 NLKR
Choice/Prior: 5.51 – 0.89 Similarity – 2.10 Prior
All coefficients were highly significant (p-value for Wald statistic < .0001), and classification
accuracy was 88% for the No prior group and 87% for the Prior group. This is strong
evidence that the similarity heuristic was being used by both groups. Separate regressions
including only Similarity as an explanatory variable supported this view classification
accuracy was reduced by less than 1% in both groups.
Finally, to provide the strongest possible test we conduct a further analysis relating
individual similarity judgments to individual choices. Because we did not collect similarity
judgments and choices from the same respondents, we created “quasi-subjects,” simply by
placing the individual responses in all four conditions into four columns of our data file, and
then analyzing the relationships between conditions as if they had been collected from the
same respondent. We lined up, for instance, the response from the first respondent who made
a similarity judgment to one item, with the first respondent who made a choice to that item,
and so forth. Our reasoning was that if the similarity heuristic is robust to being tested under
these unpromising circumstances, it will surely be robust to tests when both choices and
similarity judgments come from the same respondent.
-- Table 3 about here --
We conducted two correlational analyses of these data, as shown in Table 3. First, we
looked at the first order correlation between Simchoice, Simchoice/Pop, Choice/Prior and
Choice/No prior. These were, as can be seen in Table 3, moderately high ( .6) and
overwhelmingly significant. This indicates that the relationship found with the aggregate
similarity judgments does not vanish when they are disaggregated. We then conducted the
same analysis, but this time partialling out three alternate choice predictors: LKChoice,
BayesChoice, and the Prior these predictors are all highly intercorrelated but we included
them to squeeze out the maximum predictive power. The partial correlations were reduced,
but all remained positive and significant. Thus, individual similarity judgments made by one
respondent were able to robustly predict the individual choices made by another respondent
Response times. A further line of evidence that choice is based on the similarity
heuristic comes from the pattern of response times (RTs), which suggest that both choices and
similarity judgments are driven by the same psychological process. Figure 5 is a boxplot
showing the distribution of median RTs for each triple, for all four conditions. This shows the
average RT and its distribution and its distribution, is approximately the same for all
conditions, an observation supported by a non-significant ANOVA
(3, 357) 1.7, .15
F p
= >
—Figure 5 about here –
Table 4 shows correlations between median RTs for all triples. All the relationships
are highly significant (
.0001, 120
p n
< =
) and, more importantly, correlations within
response categories (Similarity with Similarity/Population, and Choice/No prior with
Choice/Prior, Mean r = .70) are close to those between categories (Similarity with Choice,
Mean r=.65). This occurs despite an undoubted level of method variance due to the different
response formats in the two categories (a choice between two radio keys versus rating on a 9-
point scale).
-- Table 4 about here –
Moreover, choice response times show a relationship that should be expected if
similarity judgments are the basis for choice. When the sample is equally similar to the two
populations (i.e., similarity judgments are close to the scale midpoint) it also takes longer to
choose which population it came from. Figure 6 plots the median response time for all 120
questions against the average Similarity judgment for each question, along with the best
fitting quadratic function. In both cases this function revealed the expected significant
inverted-U function
-- Figure 6 about here --
Overall, therefore, analysis of the responses made and the time taken to make them
closely fit what we would expect if choices are based on the similarity heuristic.
How is prior probability information used?
Consistent with much earlier research (e.g., Gigerenzer, Hell & Blank, 1988; Fischhoff,
Slovic & Lichtenstein, 1979), we found that prior probabilities influenced choice in the right
direction but were underweighted. Respondents in the Choice/Prior condition were
significantly more likely to choose the high prior item than were those in the Choice/No Prior
condition (76% versus 71%; 2
(1, 119) 20.4, .146, .001
F p
= = < ), although they still chose
it at a lower rate than the actual prior probability (83%, or 5/6). Our design enabled us to go
further and determine whether knowledge of prior probabilities improved choice, and more
generally whether the knowledge was used strategically.
Knowledge of priors did not increase accuracy, which was 86.3% in the Choice/Prior
condition and 86.1% in the Choice/No prior condition (
(1, 119) 1
). This suggests that
knowledge about prior probabilities was used inefficiently. This is illustrated in Figure 7,
which shows, for both choice groups, the proportion of times the correct choice was made
when the sample was drawn from high prior population versus when it was drawn from the
low prior population (we will say, when the prior is consistent and inconsistent). When the
prior was consistent, the Choice/Prior group was a little more accurate than the Choice/No
prior group (90% versus 87%), but when it was inconsistent, they were much less accurate
(74% versus 82%). This was reliable result: an ANOVA with the group as a within-triple
factor, and consistency of priors as a between-triple factor, revealed a highly significant
interaction, 2
(1, 118) 17.7, .131, .001
F p
= = < . Since the prior was consistent 83% of the
time, the small benefit it gave when consistent was counterbalanced by the larger cost when it
was inconsistent.
-- Figure 7 about here --
A strategic way to combine knowledge of prior probabilities with similarity data is to
go with the high prior option when the sample is equally similar to both populations, but to go
with similarity when it is highly similar to only one population. This can be seen by referring
to Table 1: knowledge of priors is less useful when the environment is represented by the
columns to the right, when the two hypotheses are highly distinguishable, than when it is
represented by the columns to the left. We investigated to what degree respondents were
strategically putting more weight on priors when they found themselves in situations like the
left rather than the right columns. The fact that performance was not improved by
knowledge of priors suggests they were not using the information strategically, and we
confirmed this by examining the difference between the proportion of time the high prior item
was chosen in the Choice/Prior versus Choice/No prior groups, as a function of similarity
judgments. We define PrEqHi and NoPrEqHi as, respectively, the proportion of times the
Choice/Prior and Choice/No prior groups chose the high prior option for each triple, and then
computed a proportional shift statistic (PSS) for each triple, which was an index of the
increase in choices of the high prior item in response to having that information.
PrEqHi NoPrEqHi if PrEqHi NoPrEqHi
PrEqHi NoPrEqHi if PrEqHi NoPrEqHi
The subscript i indexes the triple. PSS ranges from -1 to 1, the difference between the
proportion of choices of the high prior option in the two choice conditions, divided by the
maximum possible proportion of such choices. For example, if for one triple 90% of the
Choice/Prior group chose the high prior item, as opposed to 80% of the Choice/No prior
group, then PSS
would be
.9 .8
1.0 .8
. On the other hand, if 90% in the Choice/No prior
group chose the high prior item while only 80% in the Choice/Prior group did, then PSS
.5). Because PSS cannot be computed if both PrEqHi and NoPrEqHi are equal to 1, which
occurred in 33 cases, we obtained 87 usable values of PSS, with a mean value of .13
(SD=.62). The fact that the number is positive indicates respondents were more likely to
choose the high prior item when they knew which one it was, and the specific value obtained
can be interpreted as follows: for the average triple, if the high prior item was chosen by a
proportion p of those in the Choice/No prior group, then it was chosen by
.13 1
p p
those in the Choice/Prior group.
Figure 8 shows the 87 values of PSS as a function of the mean similarity rating for each
triple, along with the best fitting quadratic function. If knowledge of prior probabilities was
being used strategically, this best-fitting function would have an inverse-U shape, indicating
that prior probabilities had their greatest influence when the sample was equally similar to
both populations. In fact, the quadratic function has the opposite shape to this hypothesized
inverse-U, although it accounts for relatively little of the variance in PSS (R
=.021). That is,
while knowledge of population prior probability did increase the tendency to choose the high
prior item, it did so indiscriminately respondents in the Choice/Prior condition put equal
weight on the prior when similarity was undiagnostic (when knowledge of the prior would be
useful) than when it was diagnostic (and the knowledge was relatively useless).
—Figure 8 about here –
Willard Quine famously described the problem of induction as being a question about
the use of what we call the similarity heuristic:
For me, then, the problem of induction is a problem about the world: a problem of how
we, as we now are (by our present scientific lights), in a world we never made, should
stand better than random or coin-tossing chances of coming out right, when we predict
by inductions which are based on our innate, scientifically unjustified similarity
standard. (Quine, 1969, p. 127).
Our research can be viewed as an investigation into just how much better than ‘random’ are
these predictions, and our findings are that they are, at least in one context, very much better.
In the environment in which our respondents found themselves, individual similarity
judgments were able to come out right 86% of the time, compared to coin-tossing chances of
50%. Moreover, we also found strong evidence that people were using a shared, if not
necessarily innate, similarity standard to make their choices – the similarity judgments made
by one group proved to be an excellent predictor of both the similarity judgments and the
choices made by other groups.
As we noted earlier, although the similarity heuristic is a subset of the
representativeness heuristic first described by Kahneman and Tversky (1972), we modeled
our approach on the program of a different school of researchers. This program, well-
summarized in Goldstein and Gigerenzer’s (2002) seminal article on the recognition heuristic,
is to:
design and test computational models of [cognitive] heuristics that are (a) ecologically
rational (i.e., they exploit structures of information in the environment), (b) founded in
evolved psychological capacities such as memory and the perceptual system, (c) fast,
frugal and simple [and accurate] enough to operate effectively when time, knowledge
and computational might are limited, (d) precise enough to be modeled
computationally, and (e) powerful enough to model both good and poor reasoning.
In the rest of this discussion we comment on the relationship between this program and our
own investigations.
Ecological rationality
The concept of ecological rationality is best described by the means of the lens model
of Brunswik (1952, 1955; c.f. Dhami et. al, 2004), a familiar modernized version of which is
shown in Figure 9 (e.g., Hammond, 1996). The judge or decision maker seeks to evaluate an
unobservable criterion, such as a magnitude or probability. While she cannot observe the
criterion directly, she can observe one or more fallible cues or indicators (denoted I in the
figure) that are correlated with the criterion. Judgments are based on the observable
indicators, and the accuracy (or ‘ecological rationality’) of those judgments is indexed by
their correlation with the unobservable variable. For the recognition heuristic, the judgment is
recognition (“I have seen this before”), which is a valid predictor of many otherwise
unobservable criteria (e.g., size of cities, company earnings), because it is itself causally
linked to numerous indicators of those criteria (e.g., appearance in newspapers or on TV).
-- Figure 9 about here –
The ecological rationality of the similarity heuristic arises for similar reasons. Although
researchers do not yet have a complete understanding of how similarity judgments are made,
we do know that the similarity between a case x and another case or class A or B is a function
of shared and distinctive features and characteristics (see Goldstone & Son, 2005, for a
review). Likewise, the probability that x is a sample from a given population is closely
related to the characteristics that x shares and does not share with other members of that
population. It is perhaps not surprising, therefore, that similarity turns out to be such a
reliable and valid index of class membership.
Evolved psychological capacities
Both the recognition and similarity heuristics work through a process of attribute
substitution (recognition substituted for knowledge of magnitude, similarity substituted for
knowledge of posterior probabilities), and are effective because of the strong correlation
between the attribute being substituted for and its substitution. The reason for this high
correlation is because both the capacity to recognize and the capacity to detect similarity are
both products of natural selection.
The ability to assess the similarity between two objects, or between one object and
the members of a class of objects, is central to any act of generalization (e.g., Attneave, 1950;
Goldstone & Son, 2005). As Quine (1969) observed, to acquire even the simplest concept
(such as ‘yellow’) requires ’a fully functioning sense of similarity, and relative similarity at
that: a is more similar to b than to c’ (p. 122). Some such ‘sense of similarity’ is undoubtedly
innate. Children are observed making similarity judgments as early as it is possible to make
the observations (e.g., Smith, 1989), and it is one of the ‘automatic’ cognitive processes that
remain when capacity is limited by time pressure or divided attention (Smith & Kemler-
Nelson, 1984; Ward, 1983). Like recognition and recall, therefore, the ability to judge
similarity is a skill we are born with and can deploy at minimal cognitive cost whenever it can
serve our purposes. The similarity heuristic, like other fast-and-frugal heuristics, operates by
‘piggy-backing’ on this innate ability when probability judgments are to be made.
Although we have spoken blithely about ‘similarity judgments’ we recognize that these
judgments are embedded in specific contexts. For instance, if asked to judge the similarity
between a celery stick, a rhubarb stalk and an apple, the judgment s(apple, rhubarb) will be
greater than s(celery, rhubarb) if the criterion is ‘dessert’ than if it is ‘shape.’ Indeed, the
concept of similarity has been widely criticized because of this. Medin, Goldstone and
Gentner (1993) give a concise summary of this critique:
The only way to make similarity nonarbitrary is to constrain the predicates that apply or
enter into the computation of similarity. It is these constraints and not some abstract
principle of similarity that should enter one's accounts of induction, categorization, and
problem solving. To gloss over the need to identify these constraints by appealing to
similarity is to ignore the central issue. (p. 255).
This criticism is related to the question of whether the concept of similarity can be fully
defined is a context free manner. It is likely that it cannot. The criticism does not, however,
bear on the question of whether people make similarity judgments, nor whether those
judgments are reliable. It is clear that people do and the judgments are. In our study, the
correlation between average similarity judgments in different contexts was extremely high
(.95), but this is not an isolated result even in studies designed to distinguish between
theories of similarity, similarity judgments are highly correlated across conditions. For
instance, in a study using a systematic design to demonstrate asymmetry in similarity
judgments, Medin et. al. (1993) obtained the expected asymmetries, yet the correlation
between the average similarity judgments for the same pairs in different contexts was .91 (see
their Table 1 for data; studies reported in Tversky and Gati, 1978, all yield the same
conclusions). It appears that however people make their judgments of similarity these
judgments are (a) highly consistent across contexts and across people, (b) good predictors of
the likelihood that a sample comes from a population, and (c) actually used to make these
judgments of likelihood.
Fast, frugal, simple and accurate
These criteria concern the relative performance of heuristics. We can readily suggest
ideal benchmarks for each criterion, but the standard that must be reached for us to say that
the heuristic is frugal or fast or accurate is a matter for judgment and context. We will give an
account of the performance of the similarity heuristic on some measures of these criteria,
along with an indication of our own opinion about whether the heuristic reaches one standard
or another.
When measuring the speed of a decision process, the optimum time is always 0
seconds. No actual process can achieve this, but the time taken to make a judgment of
similarity was typically about 6 seconds (as shown in Figure 5). Although we cannot
benchmark this time against other tasks, we suggest it is very little time given that it involved
two similarity judgments, a comparison between them, and a physical response on a 9-point
We can assess simplicity and frugality by comparing the similarity heuristic to the
process of making judgments by means of Bayes’ rule. A quantitative estimate can be
derived by drawing on the concept of Elementary Information Process (EIP), introduced by
Payne, Bettmann and Johnson (1993), to measure the effort required to perform a cognitive
task. An EIP is a basic cognitive transformation or operation, such as making comparisons or
adding numbers. Consider the simple case, as in our experiment, of a choice between two
hypotheses given one piece of data. The similarity heuristic, as described in Eq. (3), requires
three EIPs: two judgments of similarity, and one comparison between them. To apply Bayes’
rule, in contrast, requires seven EIPs, as in the reduced form of Eq. (2): four calculations (two
priors and two likelihoods), two products (multiplication of priors by likelihoods) and one
comparison (between the products). Using this measure, Bayes’ rule is more than twice as
costly as the similarity heuristic
. Moreover, not all EIPs are equal: if it is harder to multiply
probabilities and likelihoods than to make ordinal comparisons, and harder to estimate
likelihoods than to make judgments of similarity, then the advantage of the similarity heuristic
grows. Clearly, the similarity heuristic is frugal relative to the Bayesian decision rule.
The similarity heuristic also performed much better than chance and proved to be a
reliable choice rule. It is worth observing here that the location of one source of disagreement
between researchers in the two heuristics ‘traditions’ is exemplified by the contrast between
the accuracy achieved in our study, and that achieved by the earlier study of Bar-Hillel. Bar-
Hillel (1974) observed accuracy of 10%, based on group data, while the corresponding value
in our study is 92% (for group data, 86% for individual judgments). Moreover, this value of
92% is achieved despite the complicating factor of a prior probability not known to those
making similarity judgments, and to a less transparent way of presenting information (as
disaggregated populations and samples rather than graphs). The difference in studies is found
in the choice of design. We drew on the ideals of the representative design described by
Brunswik (1955), and argued for by Gigerenzer and Goldstein (1996). Once we established a
random sampling procedure, we did not further constrain our samples to have any specific
properties. Bar-Hillel (1974), on the other hand, deliberately chose items for which the
theorized decision-rule and Bayes’ rule would yield different choices. If we took Bar-
Hillel’s study as providing a test of the accuracy of the similarity heuristic, we would
conclude that it was highly inaccurate. This would obviously be an illegitimate conclusion
(and one that Bar-Hillel did not draw).
There is an additional methodological lesson to be drawn from a comparison between
Bar-Hillel’s (1974) study and ours. Although the normative performance of the similarity
heuristic differed greatly between studies, the degree to which the heuristic predicted choice
did not. Bar-Hillel reported her data in the form of a cross-tabulation between choices based
on the average similarity judgment for each triple (in her case a two-point scale) and the
majority choice for triples. In Table 5 we show her original data and compare it to the same
analysis conducted for our data. The patterns of results are readily comparable, and lead to
the same conclusions not just about whether the similarity heuristic predicts choice, but even
about the approximate strength of the relationship between choice and judgment.
-- Table 5 about here –
Precise enough to be modeled computationally
The similarity heuristic is also precise enough to be modeled computationally. In an
earlier section we provided a general mathematical model of the similarity heuristic. It was
not the only possible model; in fact, it was the simplest one. However, it turned out to be a
very good model in the context of our experiment. When similarity judgments made by one
group are used to predict the choices of another group, they predict those choices remarkably
Powerful enough to model both good and poor reasoning
All heuristics have a domain in which their application is appropriate, and when they
step outside that domain they can go wrong. We have already considered the performance of
the likelihood heuristic as a proxy for the similarity heuristic, and suggested the similarity
heuristic will be most accurate when the likelihood heuristic is, and inaccurate when it is not.
Specifically, and as shown formally by Edwards et al. (1963), the similarity heuristic can go
wrong when some hypotheses have exceedingly low priors, and when the similarity
judgments s(d,h) do not strongly differentiate between hypotheses.
A fascinating recent case in which the ideal conditions are not met, and the similarity
heuristic (probably coupled with some wishful thinking) leads to some unlikely judgments is
found in the scientific debate surrounding the identification of some observed woodpeckers,
which might be of the ivory-billed or pileated species (White, 2006; Fitzpatrick et al, 2005).
The two birds are very similar. Careful scrutiny can distinguish them, although to the
untutored eye they would be practically identical. The prior probabilities of the two
hypotheses, however, are not even remotely close to equal. The pileated woodpecker is
relatively common, but the last definite sighting of the ivory billed woodpecker was in 1944,
and there is every reason to believe it is extinct (i.e., prior 0). It is interesting to observe,
however, that the debate over whether some reported sightings of the ivory-billed woodpecker
are genuine involves a ‘scientific’ application of the similarity heuristic (focusing on issues
like the size of the bird and wing patterns), with little explicit reference to prior probabilities,
even by skeptics
The ivory-billed woodpecker case is, however, uncharacteristic and understates the
power of the similarity heuristic even when priors are extremely low. In the case of the ivory
billed woodpecker, prior probabilities should play such a large role because of a conjunction
of two factors: similarity is practically undiagnostic (only very enthusiastic observers can
claim that the poor quality video evidence looks a lot more like an ivory-billed than pileated
woodpecker), and the least-likely hypothesis has a very low prior probability. The situation is
therefore like that in the bottom left-hand cell of Table 1.
But suppose the situation were different, and while the prior probability is very close to
zero, similarity is very diagnostic. You are out strolling one day in a dry area a long way
from water, an area in which you know there are no swans, which only live on or very near
water. Yet you stumble across a bird that is very similar to a mute swan: It is a huge white
bird with a black forehead and a long gracefully curved neck; its feet are webbed, it does not
fly when you approach but raises its wings in a characteristic ‘sail pattern’ revealing a
wingspan of about 1.5 meters. Even though the prior probability of seeing a swan in this
location is roughly 0 (i.e., this is what you would say if someone asked you the probability
that the next bird you saw would be a swan), you will not even momentarily entertain the
possibility that this is one of the candidates having a very high prior (such as a crow, if you
are in the English countryside). We suggest that most everyday cases are like the swan rather
the woodpecker – similarity is overwhelmingly diagnostic, and is an excellent guide to choice
and decision even in the face of most unpromising priors. This is why, to return to Quine, we
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Table 1: Accuracy from using the likelihood heuristic and incremental
accuracy from using Bayes’ rule
p: Prior
H: Hypothesis set
{.5,.5} {.6,.4} {.7,.3} {.8,.2} {.9,.1}
[.5,.5] .00 .00 .00 .00 .00
[.6,.4] .10 .00 .00 .00 .00
[.7,.3] .20 .06 .00 .00 .00
[.8,.2] .30 .13 .04 .00 .00
[.9,.1] .40 .23 .09 .03 .00
[.95,.05] .45 .27 .12 .04 .00
, ,
L p
.50 .68 .84 .94 .99
To obtain Bayesian accuracy for each cell, add the incremental
accuracy to
, ,
L p
. For instance, when
H=, and
=p, the accuracy of the likelihood heuristic is .68 and the
accuracy of Bayes’ rule is
, , .68 .13 .81
A H = + =B p .
Mean inter-subject correlation between similarity judgments,
both intra- and inter-context
Set Similarity Similarity/
1 .79 .69 .68 .71
2 .67 .76 .72 .72
3 .85 .73 .79 .79
4 .76 .69 .72 .72
Correlations between individual choices by “quasi-subjects” in the four
conditions (N=1200). P<.001 except *p<.01.
Population Choice/No
Prior Choice/
Similarity 0.67 0.66 0.61
Similarity/Population -- 0.61 0.59
Choice/No Prior -- 0.61
PrChoice and
partialled out
Similarity 0.26 0.21 0.11
Similarity/Population -- 0.12 *0.07
Choice/No Prior -- 0.12
Correlations between median RTs in the four conditions
Population Choice/No
Prior Choice/
Similarity 0.66 0.51 0.68
Similarity/Population -- 0.63 0.76
Choice/No Prior -- 0.74
A cross-tabulation between choices based on the average similarity
judgment and the majority choice for triples, in Bar Hillel’s 1974 study and in ours
Bar-Hillel (1974)
Our data
Choice Choice
Pop L Pop R Pop 1 Pop 2
Pop L 11 0 Similarity Pop 1 54 3
Pop R 4 13 Pop 2 3 60
φ = .75 φ = .90
Figure captions
Figure 1: Typical stimuli used by Bar-Hillel (1974). The dashed line in Panel L is
not in the original.
Figure 2: Stimuli consisting of two populations of 100 rectangles and a sample of 25
Figure 3: The proportion of times that Population 2 would be chosen by Bayes’ rule,
as a function of the 9-point similarity scale.
Figure 4: The proportion of correct choice predictions for each respondent in the two
choice groups.
Figure 5: Boxplots of median RT in the four conditions.
Figure 6: Median response time plotted against average Similarity judgment for both
choice conditions.
Figure 7: Accuracy (BayesChoice) as a function of consistency between prior
probability and correct choice.
Figure 8: Proportional shift statistic (PSS) as a function of the mean similarity rating
for individual questions.
Figure 9: Lens model adapted from Brunswik.
Similarity to Population 2
Population 2 Correct
Choice/No prior Choice/Prior
Respondent (ranked by correct predictions)
Proportion correct predictions
Respondent (ranked by correct predictions)
Proportion correct predictions
Choice / PriorChoice/ No PriorSimilarity /
Response time (sec)
R Sq Quadratic =0.021
Ecological rationality of
Judgment process
This is a further demonstration of the availability heuristic in action. If the only probability judgments we can
remember are the ‘Linda’ or ‘Taxicab’ problem, then we might well overestimate the frequency with which such
erroneous judgments are made.
Gilovich & Griffin (2003, p.8) observe that ‘studies in this [heuristics and biases] tradition have paid scant
attention to assessing the overall ecological validity of heuristic processes…assessing the ecological validity of the
representativeness heuristic would involve identifying a universe of relevant objects and then correlating the
outcome value for each object with the value of the cue variable for each object… . This Herculean task has not
attracted researchers in the heuristics and biases tradition; the focus has been on identifying the cues that people use,
not on evaluating the overall value of those cues.’
The term has been used before. Medin, Goldstone and Gentner (1993) use it to refer to the use of similarity as a
guide to making ‘educated guesses’ in the face of uncertainty, a view which closely reflects our own. Kahneman
and Frederick (2002) used the term as an alternative label for the representativeness heuristic itself.
In a simulation study, we found only 0.3% of possible stimuli have all four properties of Bar-Hillel’s samples.
Similarity is a complex judgment and in this paper we do not consider how it is assessed.
For recent candidate
models of similarity judgment see Kemp, Bernstein and Tenenbaum, 2005, and Navarro and Lee, 2004.
The damping parameter adopted by Nilsson et al. (2005; see their Eq. (2)) can be incorporated by introducing a
further stage in the model, between the similarity vector and maximum similarity vector.
Condition (c) is always applicable to our analysis, since the prior probability of all hypotheses other than Urn A or
Urn B is 0.
This analysis cannot be interpreted as showing how much the similarity heuristic is contributing to choice.
Rather, similarity judgments work because they are highly correlated with the statistical basis for choice and
therefore when we partial out LKChoice and BayesChoice, we are also partialling out the factors that make it a good
decision rule. The analysis is rather a decisive demonstration that we cannot say respondents are “merely”
computing Bayesian posterior probabilities and responding accordingly.
The linear function accounted for none of the variance in median RT, and a cubic function yielded identical fit to
the quadratic.
This is a general result. If there are n hypotheses to be tested, the similarity heuristic calls on 2n-1 EIPs (n
calculations and n-1 comparisons), while the normative rule calls on 4n-1 EIPs (2n calculations, n products, and n-1
Much of the debate revolves around a fuzzy film in which a woodpecker is seen in the distance for 4 seconds (e.g.
Fitzpatrick et al., 2005). Given the extremely low prior probability that any ivory-billed woodpecker is alive, it
could be argued that even under its best interpretation this evidence could never warrant concluding that the
posterior probability is appreciably greater than zero.
... Novel food technologies could be evaluated by using heuristics, such as, similarity heuristics (Li & Chapman, 2012;Read & Grushka-Cockayne, 2011;Siegrist & Hartmann, 2020), affect heuristics (Finucane, Alhakami, Slovic, & Johnson, 2000;Nagaya & Shimizu, 2023;Siegrist & Sütterlin, 2016), and availability heuristics (Bode, Vraga, & Tully, 2021;Siegrist & Á rvai, 2020;Wang, 2021). The affect heuristic refers to the tendency to rely on emotional or affective responses when making judgments about technology (Lusk, Roosen, & Bieberstein, 2014;Siegrist & Sütterlin, 2016). ...
... The similarity heuristic, or representative heuristic (Kahneman & Tversky, 1972), is often used to judge the likelihood that, for example, a product is a member of one category rather than another by the degree to which it is similar to others in that category (Read & Grushka-Cockayne, 2011). The representative heuristic relying on correspondence of a novel technology to a conventional one could increase trust in the source of information and consumers acceptance of the technology (Siegrist, 2019). ...
... This could further have downstream effects on the subsequent experiential products coming from this technology as they will be evaluated more favourably if the experience matches the inferred representative information (Wilcox, Roggeveen, & Grewal, 2011). Since most everyday decisions are driven by innate similarity standards (Read & Grushka-Cockayne, 2011), evaluating a technology based on its resemblance to a conventional food technology category, particularly in terms of its crucial features, can serve as a natural guide for individuals when accepting that technology, due to enhanced trust (Macready et al., 2020). Similarly, if features of a novel food technology, such as precision fermentation, are associated with representative features of a conventional or familiar technology, like traditional fermentation, individual subsequent judgments may be influenced by the representative heuristic (Read & Grushka-Cockayne, 2011). ...
... (c) Agents could also not be a part of these coalition groups. The (a) and (b) occur when their opinion on the problem is similar to that of the other (Cartwright 1971;Read & Grushka-Cockayne 2011). Computationally this means that the distance between their proposed solution/opinion (not a proposed solution) and other members' solution/opinion are close such that they form a coalition group. ...
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Idea selection is crucial in design as it impacts the outcome of a project. A collaborative design activity could be considered as a social process where the interactions and individual states (such as the importance in the team and self-efficacy level) could affect decision-making. It is often seen in design teams that some individuals, referred to as 'influencers' in the article have more capacity to influence than others, hence they govern the team process for better or worse. Due to the limited work done in the past to study the effect of these influencers on design outcomes, the work aims at increasing the understanding by presenting some insights from its agent-based simulation. The simulation results show how different influencer team compositions affect design outcomes in terms of quality and exploration of the solutions. The idea selection starts with the agents who are ready with their solution in their 'mind'. The work presented in this article describes a framework for simulating decision-making during idea selection by considering the influencer and majority effect. The empirical study presented in the article verifies the model logic, that is, the presence of influencer and the majority during idea selection and supports the assumption that individuals' agreement on solutions proposed by other team members depends on the degree of influence and past agreement. The results of the simulation show that teams with well-defined influencers produced solutions with higher variety and had more uniform contributions from team members, but also produced solutions of lower quality.
... According to Read & Grushka (2011), when the investor makes the decision based on readily available information, then he ignores the fundamentals of the stocks and leaves the fundamental anomalies of the stock market. ...
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The literature of heuristic factors and investment performance is less confirmatory. That is why it is quite important to explore and quantify the mediation mechanism of stock market anomalies in a volatile environment. For this research, the data were collected through a survey to stock investors who are actively involved in investing. The findings of this study show that heuristic factors, availability, conservatism, and illusion of control have a significant and positive direct relationship to investment performance. For heuristic factors like anchoring and gambler’s fallacy, there has been an insignificant direct relationship to investment performance. While in indirect relationship to the investment performance, illusion of control and conservatism generate both fundamental and technical anomalies in the market, and they affect the investment performance via technical and fundamental anomalies. Anchoring bias, availability bias and gambler fallacy do not generate both technical and fundamental anomalies in market. Our study result indicates that stock market fundamental and technical anomalies have influenced individual investors’ investment performance.The findings show that there is a significant impact of fundamental anomalies on investment performance.
... The learning from the past, which could be positive or negative experience is different and have a different impact on the current situation (Wimmer & Shohamy, 2017) are described below. • The magnitude of learning from the positive experience (| | = S(d')) depends on the similarity between an agent's current solution in 'mind' and the recalled positive event (Read & Grushka-Cockayne, 2010). If the recalled event is similar (closer on solution space) to the solution 'in mind', the agent is more influenced by its previous experience than those that are far in distance (not so similar) (Gentner, 1989). ...
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Collaborative teams are getting more and more popular. There is a current need to understand how the complex and dynamic system formed by collaborative teams behave when system parameters are changed to see their impact on project outcomes. Research in the past has focused on studying the single elements of the collaborative design like design task, design team structure, design tools and design process (idea generation and idea selection). Understanding the complete system of the design team collaboration is challenging to the researchers as it increases complexity. Therefore, the purpose of this research is to increase the understanding of a collaborative system composed of teams, tasks and its collaboration environment through an agent-based model called MILANO (Model of Influence, Learning, and Norms in Organizations). This computational model is implemented using the Python programming language. MILANO is developed to mimic design team collaboration of the real world, hence it serves as a platform to study and simulate different scenarios of team dynamics that are challenging to control in a laboratory setting. The model is composed of agents that are analogous to humans in design teams who work on a design task by collaboratively generating and selecting solutions. Similar to the real world, the selected solutions are proposed to the controller agent (equivalent to a leader or manager to a problem-solving team), who provides feedback to the team. The research is broadly composed of three parts that fulfil the main purpose of the study. The first one is related to the common scenario where certain individuals who have high social influence (referred to as influencers) than others in the team, affect individual thinking during idea generation and selection. This is further investigated by varying the nature of the design task and the size of the team. The second part is related to the team compositions of experience and novices and their impact on the design outcome when changing the nature of the task. The last bit of the work is related to studying the impact of the collaboration environment (i.e., virtual vs face-to-face team collaboration) on the design outcome for various test cases (like teams with an experienced agent, half of the team with high self-efficacy, all agents with same self-efficacy and all agents with same self-efficacy working on a complex design task). Though most of the model formation is based on the past literature and theories, it also has some assumptions and has parts that needed logical validation. These assumptions were validated through empirical studies conducted in the real world. The empirical results also provide insights into the relationship between model parameters and verified the logic behind its foundation. Although agent-based modelling is an effective approach for simulating collaborative design teams, the validation of the entire model is difficult, especially if there are plenty of parameters to control in a real-world setting. Therefore, continuously validating and verifying the model rationale by means of empirical studies, adds to the strength of the model and its results. The extracted simulation results of the design task outcome were measured in terms of quality, exploration and other team performance parameters like the contribution of team agents. Broadly speaking, the model simulation results showed how varying the parameters of the collaboration design affects the outcomes of a design project. For example, different influencer- team composition has a significant difference in the generated solution quality of their team members. Moreover, having an experienced agent in a team of all novices can increase the quality of the solutions while reducing the variety. Likewise, having half of the team members as more influential, could results in a better outcome when the team collaboration is virtual. From the results, it is clear that a type of team that is effective in one situation might not perform well in other situations. Besides, studying the social, cognitive and environmental factors that were unaccounted for in the past literature, this research introduces a novel way to stimulate learning in agents and metrics for measuring design outcomes related to artificial design agents’ performance. Some of the research findings conform to the literature, hence suggesting that MILANO could be used to study collaboration in design teams and could provide meaningful insights into team formation and management. These findings could be useful in determining appropriate team and task management strategies to obtain near-optimal project outcomes in organizations during the early design phase. In academia, the model that artificially simulates human collaboration could be used as a faster approach to gain insights into different design team collaboration scenarios
... • The magnitude of learning from the positive experience (|v s | = S (d ′ )) depends on the similarity between an agent's current solution in "mind" and the recalled positive event (Read and Grushka-Cockayne, 2010). If the recalled event is similar (closer on solution space) to the solution "in mind", the agent is more influenced by its previous experience than those that are far in distance (not so similar) (Gentner, 1989). ...
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It is known that wherever there is human interaction, there is social influence. Here, we refer to more influential individuals as "influencers", who drive team processes for better or worst. Social influence gives rise to social learning, the propensity of humans to mimic the most influential individuals. As individual learning is affected by the presence of an influencer, so is an individual's idea generation. Examining this phenomenon through a series of human studies would require an enormous amount of time to study both individual and team behaviors that affect design outcomes. Hence, this paper provides an agent-based approach to study the effect of influencers during idea generation. This model is supported by the results of two empirical experiments which validate the assumptions and sustain the logic implemented in the model. The results of the model simulation make it possible to examine the impact of influencers on design outcomes, assessed in the form of exploration of design solution space and quality of the solution. The results show that teams with a few prominent influencers generate solutions with limited diversity. Moreover, during idea generation, the behavior of the teams with uniform distribution of influence is regulated by their team members' self-efficacy.
Background: Diabetes continues to be a major health problem around the globe, and low treatment adherence continues to hinder clinical outcomes. Risk perception has been considered to guide the decision-making processes, and in recent years, the bounded rationality theory and cognitive heuristics have proved to be a useful lens to gain insight into informal and irrational reasoning. In particular, heuristics for the estimation of event frequencies and probabilities are considered to be relevant to the field of health behaviors. It is possible that the risk perceptions that determine patients’ adherence could be generated by these heuristics. Aims: The purpose of this study is to analyze the role of the representativeness heuristic in risk perceptions related to heart attacks and coronary heart disease (CHD) in a sample of patients with type 2 diabetes (T2D). Method: Cross-sectional study with 342 patients attending a public hospital in an urban setting to treat their diabetes. Results: The heuristic was correlated through both its mechanisms with CHD and heart attack. Evidence suggests that the representativeness heuristic might play a role in probability judgments related to heart complications in patients with T2D. Limitations: Only CHD and heart attack were included in the study, and the heuristic could correlate with the risk perception of other complications as well as the perceived risks and benefits of the diabetes treatment. Conclusion: We call for researchers to further study these heuristics in the context of treatment adherence of T2D patients.
We apply the impression management theory and propose that firms located in more polluted areas have strong incentives to offset the negative perceptions of their local area pollution due to shareholders’ environmental concerns. In terms of dividend policy, we predict and find that location greenness (LG), a proxy for environmental image, negatively affects firms’ dividend payouts. The effect is more pronounced for firms with high information asymmetry and agency costs. The dividend payouts due to LG have a larger impact on agency cost reductions than regular dividends. Firms use dividends and social engagements as substitutes to enhance their reputation.
Patient advocates and activists are increasingly relying on online health information that can assist them to manage their health condition. Yet once online, they will confront diverse information whose veracity and utility are difficult to determine. This article offers a sociological analysis of the practical methods, or heuristics, that patient advocates and activists use when making judgements about the credibility and utility of online information. Drawing on the findings from interviews with fifty Australian patient advocates and activists, it is argued that these individuals' use of these heuristics reflects their hopes that information can help them manage their condition which may, in some cases, override fears and uncertainties that arise during searches. The article identifies the common ‘rules-of-thumb’—or what we call the ‘heuristics of hope’—that patient advocates/activists may use to make judgements and highlights the dangers of over-reliance on them, especially regarding clinically unproven, potentially unsafe treatments. Analyses of the heuristics of hope, we conclude, can assist in understanding the dynamics of decision-making and the role that affect plays in online patient communities which is crucial in an age characterised by the rapid circulation of emotionally charged messages, often based on hope.
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Game is one of the rapidly growing industries in everyday life. Currently the game is very popular in all circles, both among children, teenagers and adults. However, there are still many games that only prioritize addictive elements and ignore educational elements. Therefore, here the author will build a game that has an educational element as a learning medium. This study will apply AI to an early childhood educational game through a heuristic semilarity approach to the habits of early childhood in answering questions such as equating in everyday life. This research will produce a game that can improve intelligence, creativity and memory at an early age such as guessing pictures, counting and arranging words randomly.
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Humans and animals make inferences about the world under limited time and knowledge. In contrast, many models of rational inference treat the mind as a Laplacean Demon, equipped with unlimited time, knowledge, and computational might. Following Herbert Simon's notion of satisficing, this chapter proposes a family of algorithms based on a simple psychological mechanism: one-reason decision making. These fast-and-frugal algorithms violate fundamental tenets of classical rationality: It neither looks up nor integrates all information. By computer simulation, a competition was held between the satisficing "take-the-best" algorithm and various "rational" inference procedures (e.g., multiple regression). The take-the-best algorithm matched or outperformed all competitors in inferential speed and accuracy. This result is an existence proof that cognitive mechanisms capable of successful performance in the real world do not need to satisfy the classical norms of rational inference.
The Adaptive Decision Maker argues that people use a variety of strategies to make judgments and choices. The authors introduce a model that shows how decision makers balance effort and accuracy considerations and predicts which strategy a person will use in a given situation. A series of experiments testing the model are presented, and the authors analyse how the model can lead to improved decisions and opportunities for further research.
Many decisions are based on beliefs concerning the likelihood of uncertain events such as the outcome of an election, the guilt of a defendant, or the future value of the dollar. Occasionally, beliefs concerning uncertain events are expressed in numerical form as odds or subjective probabilities. In general, the heuristics are quite useful, but sometimes they lead to severe and systematic errors. The subjective assessment of probability resembles the subjective assessment of physical quantities such as distance or size. These judgments are all based on data of limited validity, which are processed according to heuristic rules. However, the reliance on this rule leads to systematic errors in the estimation of distance. This chapter describes three heuristics that are employed in making judgments under uncertainty. The first is representativeness, which is usually employed when people are asked to judge the probability that an object or event belongs to a class or event. The second is the availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development, and the third is adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available.