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Heuristic decision making in medicine


Abstract and Figures

Can less information be more helpful when it comes to making medical decisions? Contrary to the common intuition that more information is always better, the use of heuristics can help both physicians and patients to make sound decisions. Heuristics are simple decision strategies that ignore part of the available information, basing decisions on only a few relevant predictors. We discuss: (i) how doctors and patients use heuristics; and (ii) when heuristics outperform information-greedy methods, such as regressions in medical diagnosis. Furthermore, we outline those features of heuristics that make them useful in health care settings. These features include their surprising accuracy, transparency, and wide accessibility, as well as the low costs and little time required to employ them. We close by explaining one of the statistical reasons why heuristics are accurate, and by pointing to psychiatry as one area for future research on heuristics in health care.
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ccompanied by his anxious wife, a middle-aged
male patient arrives at a rural Michigan hospital. He suf-
fers from serious chest pain. The physician in charge, a
compassionate-looking woman, suspects acute ischemic
heart disease, but is not entirely sure. Should she assign
the patient to a regular nursing bed for monitoring? If it
really is acute ischemic heart disease, however, the patient
needs to be rushed immediately to the coronary care unit.
On the other hand, unwarrantedly sending the patient to
the care unit is not only expensive, but can also decrease
the quality of care for those patients who need it, while
those who do not are exposed to the risk of catching a
potentially harmful, hospital-transmitted infection.
How humans can solve this, and related complex deci-
sion-making dilemmas in the medical world, is the cen-
tral topic of this review article. For the emergency-room
situation outlined above, there are different approaches
to tackling the problem, rooted in different traditions in
the decision sciences.
Clinical research
Copyright © 2012 LLS SAS. All rights reserved
Heuristic decision making in medicine
Julian N. Marewski, PhD; Gerd Gigerenzer, PhD
medical decision making; fast-and-frugal heuristics; decision aids;
biases; ecological rationality; bounded rationality
Author affiliations: University of Lausanne, Lausanne, Switzerland (Julian N.
Marewski); Max Planck Institute for Human Development, Berlin, Germany
(Gerd Gigerenzer)
Address for correspondence: Julian N. Marewski, University of Lausanne, Faculty of
Business and Economics, Department of Organizational Behavior, Quartier UNIL-
Dorigny, Bâtiment Internef, Office 601, 1015 Lausanne, Switzerland
Can less information be more helpful when it comes to
making medical decisions? Contrary to the common
intuition that more information is always better, the
use of heuristics can help both physicians and patients
to make sound decisions. Heuristics are simple decision
strategies that ignore part of the available information,
basing decisions on only a few relevant predictors. We
discuss: (i) how doctors and patients use heuristics; and
(ii) when heuristics outperform information-greedy
methods, such as regressions in medical diagnosis.
Furthermore, we outline those features of heuristics
that make them useful in health care settings. These
features include their surprising accuracy, transparency,
and wide accessibility, as well as the low costs and little
time required to employ them. We close by explaining
one of the statistical reasons why heuristics are accu-
rate, and by pointing to psychiatry as one area for
future research on heuristics in health care.
© 2012, LLS SAS
Dialogues Clin Neurosci.
PAGES_12_AG_1006_BA.qxd:DCNS#52 10/03/12 12:46 Page 77
Clinical research
The first one is to leave all responsibility to the doctors.
Yet, in an actual rural Michigan hospital under study,
doctors sent 90% of patients with severe chest pain to
the coronary care unit; as a consequence, it became over-
crowded, quality of care decreased, and costs went up.
The second approach is to try to solve the complex prob-
lem with a complex algorithm. This is what a team of
medical researchers from the University of Michigan did.
They introduced the Heart Disease Predictive Instrument,
which consists of a chart with some 50 probabilities and
a logistic regression that enables the physician, with the
help of a pocket calculator, to compute the probability
that the patient should be admitted to the coronary care
unit. However, few physicians understand logistic regres-
sions, and charts and calculators tend to be dropped the
moment the researchers leave the hospital.
The third approach consists of teaching physicians effec-
tive heuristics. A heuristic is a simple decision strategy that
ignores part of the available information and focuses on
the few relevant predictors. Green and Mehr1developed
one such heuristic for treatment allocation. This so-called
fast-and-frugal tree ignores all probabilities and asks only
a few yes-or-no questions (Figure 1). Specifically, if a cer-
tain anomaly appears in the patient's electrocardiogram
(ie, an ST-segment change), the patient is immediately
sent to the coronary care unit. No other information is
considered. If there is no anomaly, a second variable is
taken into account, namely whether the patient’s primary
complaint is chest pain. If not, the patient is classified as
low risk, and assigned to a regular nursing bed. Again, no
additional information is considered. If the answer is yes,
a third and final question is asked to classify the patient.
Can following such a simple heuristic enable doctors to
make good allocation decisions? Figure 2 shows the per-
formance of all three approaches in their ability to pre-
dict heart attacks in the Michigan hospital. As can be
seen, the heuristic approach resulted in a larger sensi-
tivity (proportion of patients correctly assigned to the
coronary care unit) and a lower false-positive rate (pro-
portion of patients incorrectly assigned to the coronary
care unit) than both the Heart Disease Predictive
Instrument and the physicians. The heuristic approach
achieved this surprising level of performance by consid-
ering only a fraction of the information that the Heart
Disease Predictive Instrument used.
Views on rationality: from unbounded
rationality and irrationality to ecological
What to diagnose, whom to treat, what to eat, or which
stocks to invest in—our days are filled with decisions, yet
how do we make them, and how should we make them?
In the decision sciences and beyond, the answer to these
two questions depends on one’s view of human ratio-
nality. There are at least three views.
Figure 1. A simple heuristic for deciding whether a patient should be
assigned to the coronary care unit or to a regular nursing bed.
If there is a certain anomaly in the electrocardiogram (the so-
called ST segment) the patient is immediately sent to the
coronary care unit. Otherwise a second predictor is consid-
ered, namely whether the patient’s chief complaint is chest
pain. If not, a third question is asked. This third question is
a composite one: whether any of five other predictors is pre-
sent. This type of heuristic is also called a fast-and-frugal tree.
Fast-and-frugal trees assume that decision makers follow a
series of sequential steps prior to reaching a decision.
Abbreviations: NTG, nitroglycerin; MI, myocardial infarction;
T, T-waves with peaking or inversion
Adapted from ref 58 (based on Green and Mehr)1: Gigerenzer G. Gut
feelings: the Intelligence of the Unconscious. New York, NY: Viking
Press; 2007. Copyright © Viking Press 2007
care unit
Chief complaint of
chest pain?
care unit
no yes
no yes
Any one other factor?
(NTG, MI, ST , ST , T)
PAGES_12_AG_1006_BA.qxd:DCNS#52 10/03/12 12:46 Page 78
Unbounded rationality: optimization
The study of unbounded rationality asks the question, if
people were omniscient, that is, if they could compute
the future from what they know, how would they behave
and how should they behave? Optimization models such
as Bayesian inference and the maximization of subjec-
tive expected utility take this view.2When judging, for
instance, whom to treat, these models assume that deci-
sion makers will collect and evaluate all information,
weight each piece of it according to some criterion, and
then combine the pieces to maximize the chances of
attaining their goals (eg, treating the needy while saving
costs). Optimization under constraints, a sub-branch of
unboundedly rational optimization, refers to models that
do not assume full knowledge but take into account con-
straints, such as information costs. Optimization models
are common in fields such as economics or computer sci-
ence. The spirit of optimization is also reflected in the
workings of the Heart Disease Predictive Instrument,
which is a linear regression model that computes opti-
mal beta weights.
Irrationality: cognitive illusions and biases
According to the second view, human reasoning is not
characterized by optimization but by systematic devia-
tions from optimization, also called cognitive illusions,
errors, or simply irrationality. The heuristics-and-biases
framework3proposes that humans commit systematic
errors when judging probabilities and making decisions.
Although this framework differs therein from the opti-
mization view, it still takes optimization—such as maxi-
mization of expected utility—as the normative yardstick
against which to evaluate human decision making.
Decisions that deviate from this standard can be expli-
cated by assuming that people suffer from cognitive lim-
itations, such as a suboptimal information processing
capacity or insufficient knowledge. Following this view,
one might argue that the physicians’ large false-positive
rate and below-chance performance in making alloca-
tion decisions (Figure 2) reflect the workings of their
limited cognitive abilities.
Ecological rationality: fast and frugal heuristics
There is, however, an alternative to optimization and
irrationality. A couple of thousand journal articles (and
several years) before the heuristics-and-biases tradition
became popular, Herbert Simon, the father of what is
known as the bounded rationality view, stressed that opti-
mization is rarely possible in the real world, and thus a
theory of rationality needs to study how people make
decisions when optimization is out of reach.4Instead of
relying on unrealistic optimization models and striving
to compute optimal solutions for a given task, so he
argued, people use simple strategies, seeking solutions
that are good enough with respect to an organism’s
goals. He also stressed that behavior and performance
result from both cognition and an organism’s environ-
ment (Box 1): “Human rational behavior … is shaped by
a scissors whose two blades are the structure of task
Heuristic decision making in medicine - Marewski and Gigerenzer Dialogues in Clinical Neuroscience - Vol 14 .No. 1 .2012
Figure 2. The performance of a decision tree for coronary care unit allo-
cations, compared with that of the Heart Disease Predictive
Instrument, and physicians’ judgments. The x-axis represents
the proportion of patients who were incorrectly assigned to
the coronary care unit (false positive rate), and the y-axis
shows the proportion of patients who were correctly assigned
to the coronary care unit (sensitivity). The diagonal line rep-
resents chance level, the area to the left of the diagonal bet-
ter-than-chance. Note that the Heart Disease Predictive
Instrument’s allocation decisions depend on how sensitivity is
traded off against the false-positive rate. This is why several
data points are shown for this instrument.
Adapted from ref 58 (based on Green and Mehr)1: Gigerenzer G. Gut
feelings: the Intelligence of the Unconscious. New York, NY: Viking
Press; 2007. Copyright © Viking Press 2007
.2 .3 .4 .5
Proportion of patients incorrectly assigned to the coronary care unit
Proportion of patients correctly assigned to the coronary care unit
.6 .7 .8 .9 1
Heart Disease Predictive Instrument
Fast-and-frugal tree
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environments and the computational capabilities of the
actor” (p 7).5
Embracing this emphasis on simple decision strategies
and their fit to the environment, the fast-and-frugal
heuristics framework6,7 has developed an ecological view
of rationality through which it tries to understand how
and when people’s reliance on simple decision heuristics
can result in smart behavior. In this view, heuristics can
be ecologically rational with respect to the environment
and the goals of the actor. Here, being rational means
that a heuristic is successful with regard to some outside
criterion, such as making a decision accurately and
quickly when a patient is rushed into the emergency
room. Hammond8called such outside criteria corre-
spondence criteria, as opposed to coherence criteria,
which are based on unboundedly rational optimization
models as a normative yardstick for rationality.
For instance, while physicians’ decisions in Figure 2
appear to be systematically biased towards mistakenly
assigning healthy patients to the coronary care unit,
these decisions might in fact be viewed as ecologically
rational, as the following court trial illustrates. In 2003,
Daniel Merenstein,9a family physician in Virginia,
USA, was sued because he had informed a patient
about the pros and cons of PSA (prostate-specific anti-
gen) tests, instead of just ordering one. Given that
there is no evidence that the test does more good than
harm, he had followed the recommendations of lead-
ing medical organizations and informed his patient,
upon which the man declined to take the test. The
patient later developed an incurable form of prostate
cancer, and Merenstein was sued. The jury at the court
exonerated him, but found his residency liable for $1
million. After that, Merenstein felt he had no choice
other than to overdiagnose and overtreat patients,
even at the risk of causing unnecessary harm. This is
exactly what a vast majority of US physicians seem to
do: 93% of over 800 surgeons, obstetricians, and other
specialists at high risk of litigation reported practices
of recommending a diagnostic test or treatment that is
not the best option for the patient, but one that pro-
tects the physician against the patient as a potential
plaintiff, including, for instance, unnecessary CT scans,
biopsies, and MRIs, and more antibiotics than med-
ically indicated.10 Similarly, in the rural Michigan hos-
pital discussed above, of about 90% of the patients
who were referred to the coronary care unit, only
roughly 25% actually had a myocardial infarction. In
environments where risk of being sued is high if a
patient is mistakenly diagnosed and/or treated as
healthy and where physicians seek to avoid potential
lawsuits, it is ecologically rational for them to follow
the defensive heuristic “err on the safe side,” being
overcautious and prescribing more diagnostic tests and
treatments than necessary. This defensive heuristic is
not the same as an irrational reasoning error or a cog-
nitive illusion, caused by people’s mental limitations.
But precisely because of this, as we will discuss next,
there is room for change: by changing the environ-
ment, physicians can be led to rely on heuristics that
are more beneficial to the patient.
The science of fast-and-frugal heuristics
Doctors and other humans cannot foresee the future,
and cannot know if a diagnosis is correct for certain, or
if a treatment will cure a patient for certain. Rather, they
have to make decisions under uncertainty and often
under the constraints of limited time. According to the
fast-and-frugal heuristics research program, these deci-
sions can nevertheless be made successfully, because
people can rely on a large repertoire of heuristics—an
adaptive toolbox—with each heuristic (ie, each tool)
being adapted to a specific decision-making environ-
ment. By relying on a heuristic that is well adapted to a
particular environment, a person can make sound deci-
sions, often based on very little information in little time
(hence “fast-and-frugal”).
There are different sets of mechanisms that help people
to choose among the heuristics. The first depends on the
workings of basic cognitive capacities, such as memory.11
The interplay of these capacities with the environment
creates for each heuristic a cognitive niche in which it
Clinical research
Box 1
In the literature, a connection between the heuristics-
and-biases view and Simon’s concept of bounded
rationality is often invoked. However, although
Kahneman et al3credited Simon in the preface to
their anthology (“Judgment under uncertainty:
heuristics and biases”), their major early papers,
which appear in the same volume, do not cite Simon’s
work on bounded rationality. Thus, the connection
between heuristics-and-biases and bounded rational-
ity was possibly made in hindsight.61
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can be applied. For instance, the frequency and recency
with which we have encountered information in our
environment influences what information we remem-
ber, and how quickly we remember it. What information
comes to the mental stage, and how quickly it arrives
there, in turn determines what heuristics are applicable
to solve a given task. A second set of mechanisms for
selecting heuristics includes social and individual learn-
ing processes that can make people more prone to
choose one applicable heuristic over another.12
Importantly, by changing the environment people can
be led to rely on different heuristics. For instance, in
environments with a lower risk of being sued, doctors
may rely on different medical heuristics. In Switzerland,
where litigation is less common, only 41% of general
practitioners and 43% of internists reported that they
sometimes or often recommend a PSA test for legal
Past research on fast-and-frugal heuristics
The heuristics in the adaptive toolbox can be classified
along several nonexclusive categories. These categories
include: (i) how the heuristic processes information (eg,
assigning different importance to different predictor
variables by ordering them sequentially, as in Figure 1);
(ii) whether the heuristic is applicable to the social
domain (eg, to doctor-patient interactions or bargaining
at the bazaar); (iii) whether the heuristic is a model of
inductive inference about unknown quantities and
future events (eg, in medical diagnosis or weather fore-
casting); or (iv) whether the heuristic represents a model
for decisions that are based exclusively on the contents
of one’s memories (eg, in quiz shows or under time pres-
sure in a medical emergency).
Corresponding models of heuristics have been studied
in diverse domains, including applied ones, such as
enforcing proenvironmental behaviour or forecasting
customers’ activities in business, as well as in the basic
sciences, ranging from animal behavior to the law,
finance, or psychology.14,15 At the same time, a number of
heuristics for very different tasks have been proposed:
heuristics for mate search,16 inferences about politicians,17
and choices between risky alternatives,18 to name a few.
In the applied world, heuristics have been used to pre-
dict, for example, the performance of stocks,19 the out-
comes of sports competitions,20 or the results of political
Heuristics in health care?
Although the science of fast-and-frugal heuristics has
started to make an impact in the medical community,22
the heuristics-and-biases perspective still dominates as
of today.23 For instance, Elstein24 refers to heuristics as
“mental shortcuts commonly used in decision making
that can lead to faulty reasoning or conclusions” (p 791),
citing them as a source of many errors in clinical rea-
Some medical researchers, however, recognize the
potential of fast-and-frugal heuristics to improve deci-
sions. For example, as McDonald25 writes, “admitting the
role of heuristics confers no shame” (p 56). Rather, the
goal should be to formalize and understand heuristics so
that their use can be effectively taught, which could lead
to less practice variation and more efficient medical care.
Similarly, Elwyn et al26 state that “The next frontier will
involve fast-and-frugal heuristics; rules for patients and
clinicians alike” (p 574). In what follows, we will discuss
different ways in which the study of heuristics can
inform medical decision making.
How practitioners and patients make decisions
In medical decision making and beyond, the science of
fast-and-frugal heuristics focuses on at least three main
questions. The first question is descriptive: what heuris-
tics do doctors, patients, and other stakeholders use to
make decisions? The second question is closely interre-
lated with the first one, and deals with ecological ratio-
nality: to what environmental structures is a given
heuristic adapted—that is, in which environments does
it perform well, and in which does it not? The third ques-
tion focuses on practical applications: how can the study
of people’s repertoire of heuristics and their fit to envi-
ronmental structures aid decision making?
Let us begin with the descriptive question of how prac-
titioners and patients make decisions. Here, fast-and-
frugal heuristics differ from traditional, information-
greedy models of medical decision making, such as
expected utility maximization, Bayesian inference, or
logistic regression.
How physicians make diagnostic decisions is potentially
modelled by fast-and-frugal trees, a branch of heuristics
that assumes decision makers to follow a series of
sequential steps prior to reaching a decision. Such trees
ask only a few yes-or-no questions and allow for a deci-
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Clinical research
sion after each one. Like most other heuristics, fast-and-
frugal trees are built around three rules; one that speci-
fies in what direction information search extends in the
search space (search rule); one that specifies when infor-
mation search is stopped (stopping rule), and one that
specifies how the final decision is made (decision rule).
In their general form, fast-and-frugal trees can be sum-
marized as follows:
Search rule: Look up predictors in the order of their
Stopping rule: Stop search as soon as one predictor
variable allows it.
Decision rule: Classify according to this predictor vari-
Fast-and-frugal trees are characterized by the limited
number of exits they have; only a few predictors can be
looked up, but they will always lead to a decision. For
instance, the heuristic shown in Figure 1 represents one
such fast-and-frugal tree with four exits. Specifically, a
fast-and-frugal tree has n+ 1 exits, where nis the num-
ber of binary predictor variables. In comparison, more
information-greedy approaches have many more exits;
Bayes’ rule, for example, can be represented as a tree
with 2nexits. Contrary to more information-greedy
approaches, fast-and-frugal trees make themselves effi-
cient by introducing order—which predictors are the
most important ones?—making themselves efficient.
A number of fast-and-frugal trees have been identified
as potential descriptive models of behavior. Dhami and
Harries,27 for example, compared a fast-and-frugal tree
to a regression model on general practitioners' decisions
to prescribe lipid-lowering drugs for hypothetical
patients. Both models fitted the prescriptions equally
well (but see Box 2). Similar results were obtained by
Backlund et al28 for judgments regarding drug treatment
of hyperlipidemia as well as for diagnosing heart failure,
and by Smith and Gilhooly for describing antidepressant
medication.29 Fast-and-frugal trees, rather than full deci-
sion trees, are also routinely used in HIV testing and
cancer screening,30 and have been identified as descrip-
tive models of behavior in other areas beyond medicine,
including the law.31
What about the patients? Even patients with higher edu-
cation often rely on a simple heuristic when it comes to
their own health, even when it contradicts their acade-
mic viewpoint. For instance, although most economists
subscribe to neoclassical theories of unboundedly ratio-
nal models and advocate weighing all pros and cons of
alternatives in their research, when surveyed about their
own real-life decisions about whether to participate in
PSA screening, 66% of more than 100 American econo-
mists said that they had not weighed any pros and cons
of PSA screening, but simply trusted their doctor’s
advice. They presumably followed the heuristic ‘‘If you
see a white coat, trust it.Another 7% indicated that
their wives or relatives had influenced their decision.32
The simple social heuristic “trust your doctor” is eco-
logically rational in environments where physicians
understand health statistics, do not rely on defensive
decision heuristics for fear of litigation, and have no con-
flicts of interest, such as earning money, a free dinner, or
another kind of gratification for prescribing certain med-
ications or for using certain diagnostic techniques. Yet,
in the American health care system, where none of these
factors holds, reliance on this heuristic can become
potentially maladaptive.
Saving lives by changing the environment
Not only in the United States, but also in other countries,
can changing health care environments pay off, and
sometimes even save lives. Consider the following exam-
ple. Numerous Germans and Americans die each year
while waiting for an organ donor.33 Even though expen-
sive advertising campaigns are conducted to promote
organ donation, relatively few citizens sign a donor card:
according to Johnson and Goldstein,34 a study published
in 2003, about 12% in Germany and 28% in the US. In
contrast, about 99.9% of the French are potential donors
(Box 3). These dramatic differences among Western
countries can be explained by the interplay between the
legal environment and people’s reliance on the default
heuristic. According to this social heuristic, a person
should not act if a trustworthy institution has made an
implicit recommendation: “If there is a default, do noth-
Box 2
A heuristic’s ability to account for behavioral data
should not only be tested by assessing its fit to those
data, with fit meaning that relevant parameters can
be adjusted to the data. It should also be assessed how
well the heuristic predicts (ie, generalizes to) new
data, with all relevant parameters being fixed and not
adjustable to these data.62 Data fitting does not pro-
vide a good test; the real test is in prediction.
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ing about it.” By German law, no one is a donor without
their or their family's explicit consent. In France, in con-
trast, the default is that everyone is an organ donor
unless they explicitly opt out. Depending on the legal
environment, the same simple heuristic produces very
different behavior, with very different outcomes for the
general public and those who urgently need an organ.34
In short, the descriptive study of practitioners’ and
patients’ use of heuristics as well as the fit between these
heuristics and the environment can help in understand-
ing not only how health care decisions are made, but
how they can be improved. This leads us to the third—
the applied—question.
Can less be more?
Heuristics have various general features that render
them especially suitable tools to improve applied med-
ical decision making. Let us point out just some of these.
As numerous studies have shown, when used in the cor-
rect environment, simple decision heuristics can surpass
the accuracy of more sophisticated, information-greedy
classification and prediction tools, including that of
regression models or neural nets. Brighton,35,36 for exam-
ple, compared the performance of heavy-weight com-
putational machineries such as classification and regres-
sion trees (CART37) or the decision tree induction
algorithm C4.538 to that of a heuristic called take-the-
best.39 This heuristic resembles the fast-and-frugal tree
shown in Figure 1; it bases a decision on just one good
reason. Take-the-best simplifies decision making by
searching sequentially through binary predictor vari-
ables that can have positive values (1) or not (0) and by
stopping after the first predictor that discriminates. In
contrast to more complex (eg regression) models that
assign optimal (eg, beta) weights to the various predic-
tor variables they integrate, take-the-best simply orders
predictors unconditionally according to their validity v,
with v= C/(C+W) where Cis the number of correct
inferences when a predictor discriminates, and Wthe
number of wrong inferences.
Search rule: Search through predictors in order of their
Stopping rule: Stop on finding the first predictor that
discriminates between the alternatives (eg, possible
predictor values are 1 and 0).
Decision rule: Infer that the alternative with the posi-
tive predictor value (1) has the higher criterion value.
Brighton35,36 showed that, across many data sets from dif-
ferent real-world domains, it was the rule rather than the
exception that take-the-best outperformed sophisticated
computational machineries in predicting new (eg, yet
unknown) data. In the past years, a number of studies
have striven to make similar comparisons between
heuristics and information-greedy tools in medical deci-
sion making. One of the most recent of these attempts,
for example, focuses on fast-and-frugal trees for diag-
nosis of mental disorders such as depression.40
Because heuristics are simple, they are transparent and
generally easy to teach and to use in applied settings.
Consider, once more, the tree shown in Figure 1: in order
to make an accurate decision quickly, the doctor has to
ask at most three simple yes-or-no questions. The deci-
sion-making process is completely transparent and can be
easily communicated to a patient if needed. In contrast,
dealing with the various probabilities and symptoms cov-
ered by the Heart Disease Predictive Instrument is more
cumbersome and complicated. As a result, the decision-
making process seems less transparent and is likely more
difficult to explain to a patient.
Teaching simple, transparent heuristics to doctors can
also help them to better understand health statistics, that
is, the information on which informed medical diagnoses
and treatment decisions should be based. Unfortunately,
there is evidence that many doctors do not know how to
correctly interpret such statistics. For instance,
Gigerenzer et al41 gave 160 gynecologists the statistics
needed for calculating that a woman with a positive
breast cancer screening mammogram actually has can-
cer: a sensitivity of 90%, a false-positive rate of 9%, and
a prevalence of 1%. The physicians were asked what they
would tell a woman who tested positive about her
chances of having breast cancer. The best answer is about
Heuristic decision making in medicine - Marewski and Gigerenzer Dialogues in Clinical Neuroscience - Vol 14 .No. 1 .2012
Box 3
As of writing this article, the numbers reported by
Johnson and Goldstein34 in 2003 have changed. For
instance, in 2010 Germany had about 17% potential
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1 out of 10 women; the results for the remaining 9 out of
10 are false alarms (false positives). As it turns out, 60%
of the gynecologists believed that 8 or 9 out of 10 women
who tested positive would have cancer, and 18% thought
that the chances were 1 in 100. A similar lack of under-
standing among physicians has been reported in diabetes
prevention studies,42 the evaluation of HIV tests,43 and
other medical tests and treatments.44-48 Making health sta-
tistics transparent can help doctors to understand them.
One very simple heuristic, for instance, is to change the
mathematical format in which the relevant numbers are
represented. To illustrate this, consider the case of mam-
mography screening once more. It is easy to teach physi-
cians to translate the given probabilities into what is
called natural frequencies, and to draw a corresponding
tree to visualize the numbers. As Figure 3 shows, all the
physicians have to do is to think of 1000 women. Ten of
these women are expected to have breast cancer (= 1%
prevalence). Of these 10 women, 9 will test positive (=
90% sensitivity). Of the 990 women who do not have
cancer, roughly 89 will still test positive (= 9% false pos-
itive rate). When the format was changed to such natural
frequencies, most of the gynecologists (87%) understood
that 9+89 = 98 will test positive. Of these 98, only 9 will
actually have breast cancer, equaling roughly 1 out of 10
(= 10%).
Quick applicability is another important feature of well-
functioning heuristics, particularly in emergency situa-
tions. After the attacks of September 11, 2001, the
Simple Triage and Rapid Treatment, START,49 a heuristic
that can be categorized into the branch of fast-and-
frugal trees,50 allowed paramedics to rapidly split the vic-
tims into main groups, including those who required
immediate medical treatment and those whose treat-
ment was not as urgent.
Accessibility and costs
Well-functioning heuristics can be made easily accessi-
ble and help treatment and diagnosis even in situations
where access to technology is restricted. For instance, for
macrolide prescription in young children with commu-
nity-acquired pneumonia, a tree with only two predictor
variables—age and duration of fever—was developed as
a decision aid (Figure 4).51 This frugal decision aid turned
out to be only slightly less accurate than a scoring sys-
tem based on logistic regression (72% versus 75% sen-
sitivity), but using it does not require expensive tech-
nology. As a result, this decision aid can be made easily
accessible to millions of children worldwide, even in
poor countries.
Simple heuristics can also aid in saving costs in rich,
developed countries, as the following example illustrates.
In the US, there are about 2.6 million emergency room
visits each year for dizziness or vertigo.52 Emergency
Clinical research
Figure 3. A simple tree to represent probabilities as natural frequencies,
designed to help pyhsicians and patients understand health
no cancer
Figure 4. A fast-and-frugal tree for making decisions about macrolide
prescriptions, proposed by Fisher et al51 (see also
Katsikopoulos et al58 for an in-depth discussion). Macrolides
are the first-line antibiotic treatment of community-acquired
pneumonia. The fast-and-frugal tree signals that first-line
macrolide treatment may be limited to individuals with com-
munity-acquired pneumonia who have had fever for more
than 2 days and who are older than 3 years.
no yes
no yes
Duration of fever 2 days?
risk low
risk moderate
risk high
Age 3 years?
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Heuristic decision making in medicine - Marewski and Gigerenzer Dialogues in Clinical Neuroscience - Vol 14 .No. 1 .2012
room personnel need to detect the rare instances where
such dizziness is due to a dangerous brain stem or cere-
bellar stroke. MRI with diffusion-weighted imaging can
help doctors to make this challenging diagnosis. Another
diagnostic tool, a simple bedside exam, was developed
by Kattah et al.52 An alarm is raised if at least one of
three simple tests indicates a stroke.
This bedside exam represents a tallying heuristic. In con-
trast to fast-and-frugal trees and take-the-best, which
assign more or less importance to specific predictor vari-
ables by ordering them, tallying treats all predictors
equally, for example, by simply counting them. In its gen-
eral form, tallying can be described as follows.
Search rule: Search through predictors in any order.
Stopping rule: Stop search after mout of a total of M
predictors (with 1 < mM). If the number of positive
predictors is the same for both alternatives, search for
another predictor. If no more predictors are found,
Decision rule: Decide for the alternative that is favored
by more predictors.
As it turns out, Kattah et al’s52 simple bedside exam
yields a larger sensitivity than MRI, while the false-pos-
itive rate is only slightly larger than that of the MRI,
which did not raise any false alarms. In contrast to the
MRI, which can take up to 5 to 10 minutes plus several
hours of waiting time, entails costs of more than $1000,
and is not available everywhere, the bedside exam takes
little time, is less cost-intensive, and can be conducted
In short, relying on heuristics as a tool for medical deci-
sion making can help practitioners to make accurate,
transparent, and quick decisions, often while depending
on little technology and few financial resources. Less
information, complexity, time, and technology can be
more efficient, even when it comes to medical decision
Why heuristics work
One reason for the surprising performance of heuristics
is that they ignore information. As we have explained
above, this makes them quicker to execute, easier to
understand, and easier to communicate. Importantly, as
can be shown by means of mathematical analysis and
computer simulations,36,53 it is also this feature that dri-
ves part of the predictive power of heuristics. Let us illus-
trate this with a simplifying, fictional story.
Imagine two doctors. One doctor, let’s call him Professor
Complexicus (PhD), is known for his scrutiny—he takes
all information about a patient into account, including
the most minute details. His philosophy is that all infor-
mation is potentially relevant, and that considering as
much information as possible benefits decisions. The
other physician, Doctor Heuristicus, in contrast, relies
only on a few pieces of information, perhaps those that
she deems to be the most relevant ones. We can think of
the two doctors’ decision strategies as integration mod-
els. One of Professor Complexicus’ models might read
like this: y= w1x1 a1+ w2x2a2+ w3x3a3+ w4x4a4+ w5x5a5+
wixiai+ z. A simpler model of Doctor Heuristicus could
throw away some of the free parameters, wi, ai, and z, as
well as some of the predictor variables, xi, such that y=
w1x1+ z. The criterion both doctors wish to infer could
be the number of days different patients will need to
recover from a medical condition, y. The predictor vari-
ables, xi, could be the type of condition the patients suf-
fer from, the patients’ overall physical constitution or
age, or the number of times loving family members have
visited the patients in the hospital thus far.
In a formal, statistical analysis, a comparative evaluation
of these two models would entail computing R2or some
other goodness-of-fit index between the models’ esti-
mations and the observed number of days it took the
patients to recover. Such measures are based on the dis-
tance between a model’s estimate and the criterion y.
And indeed, fitting Professor Complexicus’ strategy of
paying attention to more variables and weighting them
in an optimal way (ie, minimizing least squares) to obser-
vations about past patients (ie, the ones where one
already knows how many days they needed to recover),
will always lead to a larger R2than fitting Doctor
Heuristicus’ simpler strategy to these observations. Put
differently, when it comes to explaining past observa-
tions from hindsight, Professor Complexicus will do the
more convincing job. Given how well Professor
Complexicus does in explaining the time patients
needed to recover in the past, it seems intuitive that his
estimations should also fare better than those of Doctor
Heuristicus when it comes to predicting future patients’
time to recover.
Yet this is not necessarily the case. Goodness-of fit mea-
sures alone cannot disentangle the variation in the
observations due to the relevant variables from the vari-
ation due to random error, or noise. In fitting past obser-
vations, models can end up taking into account such
PAGES_12_AG_1006_BA.qxd:DCNS#52 10/03/12 12:46 Page 85
noise, thus mistakenly attributing meaning to mere
chance. As a result, a model can end up overfitting these
Figure 5 illustrates a corresponding situation in which
one model, Model A (thin line) overfits already existing,
past observations (filled circles; eg, old patients) by chas-
ing after noise in those observations. As can be seen, this
model fits the past observations perfectly but does a rel-
atively poor job of predicting new observations (filled
triangles; eg, new patients). Model B (thick line), while
not fitting the past observations as well as Model A, cap-
tures the main trends in the data and ignores the noise.
This makes it better equipped to predict new observa-
tions, as can be seen from the deviations between the
model’s predictions and the new observations, which are
indeed smaller than the deviations for Model A.
Importantly, the degree to which a model is susceptible
to overfitting is related to the model’s complexity. One
factor that contributes to a model’s complexity is its
number of free parameters. As is illustrated in Figure 5,
the complex, information-greedy Model A overfits past
observations; Model B, in turn, which has fewer free
parameters and which takes into account less informa-
tion, captures only the main trends in the past observa-
tions, but better predicts the new observations. The same
is likely to hold true with respect to Professor
Complexicus’ and Doctor Heuristicus’ strategies:
Professor Complexicus’ complex strategy is likely to be
more prone to overfitting past observations than Doctor
Heuristicus’ simple one. As a result, Dr. Heuristicus’
strategy is likely to be better able to predict new obser-
vations than Professor Complexicus’ strategy.
In short, when data are not completely free of noise,
increased complexity (eg, integrating as much informa-
tion as possible) makes a model more likely to end up
overfitting past observations, while its ability to predict
new ones decreases (although see Box 4). But what mat-
ters in many applied medical settings is less the ability to
explain (ie, fit) past observations than to make accurate
inferences about future, unknown observations, such as
about new, yet unseen patients.
Summary and outlook for future research
Rationality has many meanings. Most theories assume
that the future can be known with certainty, including
the probabilities, for instance, for weighting different
pieces of information, so that unboundedly rational opti-
mization methods can define rational choice. There are
two variants of these: those that assume that people’s
behavior can actually be modeled by this form of
unboundedly rational optimization, and those that
assume that people’ behavior systematically deviates
from it, manifesting irrational cognitive illusions, biases,
and errors. This article dealt with a third perspective,
which asks how people make decisions when the condi-
tions for optimization are not met. That is the case for
most real-world decisions, including in medicine. In
Clinical research
Figure 5. Illustration of how two models fit past observations (filled cir-
cles) and how they predict new observations (triangles). The
complex Model A (thin line) overfits the past observations and
is not as accurate in predicting the new observations as the
simple Model B (thick line).
Adapted from ref 60: Pitt MA, Myung IJ, Zhang S. Toward a method for
selecting among computational models for cognition. Psychol Rev.
2002;109:472–491. Copyright © American Psychological Association 2002
Past observations
New observations
Model B
Model A
Box 4
Obviously, ignoring too much information and too
many parameters can also be detrimental. A well-
functioning model needs to achieve a balance
between both extremes. As is known in the model
selection literature, decreasing a model’s complexity
can eventually lead to underfitting; thus, in an uncer-
tain world, there is often an inversely U-shaped func-
tion between model complexity and predictive
power.60 Moreoever, besides the number of free para-
meters a model has, other factors also contribute to
model complexity, such as a model’s functional form
and the extension of the allowable parameter space.64
PAGES_12_AG_1006_BA.qxd:DCNS#52 10/03/12 12:46 Page 86
uncertain worlds, people tend to rely on heuristics that
can make better and faster decisions than complex,
information-greedy strategies.
What are promising areas of future research on heuris-
tic decision making in medicine, and in health care? For
instance, while the neuronal basis of a number of heuris-
tics has started to be explored,54 comparatively little
research on fast-and-frugal heuristics in the clinical
branch of the neurosciences, and in psychiatry more gen-
erally, has been carried out. We have mentioned only
one of the few existing applications of heuristics to these
fields, namely a comparison of a heuristic with a more
complicated tool in diagnosing depression.40 Others
include attempts to investigate whether patients with
mental disorders or impaired mental functioning rely on
fast-and-frugal heuristics. Glöckner and Moritz,55 for
example, reported that under high stress induced in a
laboratory task, schizophrenia patients seemed to rely
on tallying heuristics. Pachur et al,56 in turn, investigated
the impact of cognitive aging on people’s reliance on
heuristics. They found that older adults are more likely
to rely on a particularly simple heuristic based on recog-
nition memory in a potentially maladaptive way. Similar
results have also been reported by Mata et al,57 who pro-
vide evidence that older adults’ limited cognitive abili-
ties can lead them to rely on certain heuristics indepen-
dent of whether the environment favors their use or not.
Future research could build on these findings, address-
ing questions such as how environments should be
designed for people who suffer from a mental disorder
or otherwise impaired cognitive functioning. We hope
that this review article contributes to stimulating what
we take to be a promising route of future research and
applications of the science of ecologically rational, fast-
and-frugal heuristics.
Acknowledgements: We thank Rona Unrau for editing the manuscript.
We thank Dorothee Schmid for helpful comments.
Heuristic decision making in medicine - Marewski and Gigerenzer Dialogues in Clinical Neuroscience - Vol 14 .No. 1 .2012
1. Green L, Mehr DR. What alters physicians’ decisions to admit to the
coronary care unit? J Fam Pract. 1997;45:219–226.
2. Edwards W. The theory of decision making. Psychol Bull. 1954;51:380–417.
3. Kahneman D, Slovic P, Tversky A, eds. Judgment Under Uncertainty:
Heuristics and Biases. Cambridge, UK: Cambridge University Press; 1982.
4. Simon HA. Rational choice and the structure of the environment.
Psychol Rev. 1956;63:129–138.
5. Simon HA. Invariants of human behavior. Annu Rev Psychol. 1990;41:1–
6. Gigerenzer G, Todd PM, the ABC Research Group. Simple Heuristics That
Make Us Smart. New York, NY: Oxford University Press; 1999.
7. Marewski JN, Gaissmaier W, Gigerenzer G. Good judgments do not
require complex cognition. Cogn Process. 2010;11:103–121.
8. Hammond KR. Human Judgment and Social Policy: Irreducible Uncertainty,
Inevitable Error, Unavoidable Injustice. New York, NY: Oxford University
9. Merenstein D. Winners and losers. JAMA. 2004;7:15-16.
10. Studdert DM, Mello MM, Sage WM, et al. Defensive medicine among
high-risk specialist physicians in a volatile malpractice environment. JAMA.
11. Marewski JN, Schooler LJ. Cognitive niches: an ecological model of
strategy selection. Psychol Rev. 2011;118:393-437.
12. Rieskamp J, Otto PE. SSL: A theory of how people learn to select
strategies. J Exp Psychol Gen. 2006;135:207–236.
13. Steurer J, Held U, Schmidt M, Gigerenzer G, Tag B, Bachmann LM.
Legal concerns trigger prostate-specific antigen testing. J Eval Clin Pract.
14. Gigerenzer G, Engel C. Heuristics and the Law. Cambridge, MA: MIT
Press; 2006.
15. Gigerenzer G, Hertwig R, Pachur T, eds. Heuristics: the Foundations of
Adaptive Behavior. New York, NY: Oxford University Press; 2011.
16. Todd PM, Miller GF. From pride and prejudice to persuasion: realistic
heuristics for mate search. In: Gigerenzer G, Todd PM, the ABC Research
Group, eds. Simple Heuristics That Make Us Smart. New York, NY: Oxford
University Press; 1999;287–308.
17. Marewski JN, Gaissmaier W, Schooler LJ, Goldstein DG, Gigerenzer G.
From recognition to decisions: extending and testing recognition-based
models for multi-alternative inference. Psychon Bull Rev. 2010;17:287-309.
18. Brandstätter E, Gigerenzer G, Hertwig R. The priority heuristic: mak-
ing choices without trade-offs. Psychol Rev. 2006;113:409–432.
19. Ortmann A, Gigerenzer G, Borges B, Goldstein DG. The recognition
heuristic: a fast and frugal way to investment choice? In: Plott CR, Smith
VL, eds. Handbook of Experimental Economics Results. Volume 1. Amsterdam,
NL: North-Holland; 2008:993–1003.
20. Pachur T, Biele G. Forecasting from ignorance: the use and usefulness
of recognition in lay predictions of sports events. Acta Psychol.
21. Gaissmaier W, Marewski JN. Forecasting elections with mere recogni-
tion from lousy samples. Judgm Decis Mak. 2011;6:73-88.
22. Wegwarth O, Gaissmaier W, Gigerenzer G. Smart strategies for doc-
tors and doctors in training: heuristics in medicine. Med Educ. 2009;43:721–
23. Croskerry P. A universal model of diagnostic reasoning. Acad Med.
24. Elstein AS. Heuristics and biases: selected errors in clinical reasoning.
Acad Med. 1999;74:791–794.
25. McDonald C. Medical heuristics: the silent adjudicators of clinical prac-
tice. Ann Intern Med. 1996;124:56–62.
26. Elwyn G, Edwards A, Eccles M, Rovner D. Decision analysis in patient
care. Lancet. 2001;358:571–574.
27. Dhami MK, Harries C. Fast and frugal versus regression models of
human judgment. Think Reasoning. 2001;7:5–27.
28. Backlund LG, Bring J, Skaner Y, Strender LE, Montgomery H.
Improving fast and frugal in relation to regression analysis: test of 3 mod-
els for medical decision making. Med Decis Mak. 2009;29:140–148.
29. Smith L, Gilhooly K. Regression versus fast and frugal models of deci-
sion-making: the case of prescribing for depression. Appl Cogn Psychol.
30. Gigerenzer G. Calculated Risks: How to Know When Numbers Deceive You.
New York, NY: Simon & Schuster; 2002.
31. Dhami MK. Psychological models of professional decision making.
Psychol Sci. 2003;14:175–180.
PAGES_12_AG_1006_BA.qxd:DCNS#52 10/03/12 12:46 Page 87
Clinical research
32. Berg N, Biele G, Gigerenzer G. Does consistency predict accuracy of
beliefs?: economists surveyed about PSA. MPRA Paper 26590, University
Library of Munich, Germany; 2010. Available at: Accessed
February 2012.
33. Die Zahl der Organspenden erhöhen – Zu einem drängenden Problem
der Transplantationsmedizin in Deutschland. Available at: .
Berlin, Germany: Nationaler Ethikrat. Accessed September 21, 2011.
34. Johnson EJ, Goldstein DG. Do defaults save lives? Science.
35. Brighton H. Robust inference with simple cognitive models. In:
Lebiere C, Wray B, eds. Between a rock and a hard place: cognitive science prin-
ciples meet AI-hard problems. Papers from the AAAI spring symposium (AAAI
Tech. Rep. No. SS-06-03). Menlo Park, Calif: AAAI Press. 2006;17–22.
36. Gigerenzer G, Brighton H. Homo heuristicus: why biased minds make
better inferences. Top Cogn Sci. 2009;1:107–143.
37. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and
Regression Trees. Belmont, CA: Wadsworth International Group. 1984.
38. Quinlan JR. C4.5: Programs For Machine Learning. San Mateo, CA:
Morgan Kaufmann; 1993.
39. Gigerenzer G, Goldstein DG. Reasoning the fast and frugal way: mod-
els of bounded rationality. Psychol Rev. 1996;104:650-669.
40. Jenny M, Pachur T. Can a fast and frugal tree predict depression?
Poster presented at: Annual Meeting of the Society for Judgment and
Decision Making; November 2009; Boston, MA.
41. Gigerenzer G, Gaissmaier W, Kurz-Milcke E, Schwartz LM, Woloshin S.
Helping doctors and patients make sense of health statistics. Psychol Sci
Public Interest. 2007;8:53-96.
42. Mühlhauser I, Kasper J, Meyer G. FEND: Understanding of diabetes
prevention studies: questionnaire survey of professionals in diabetes care.
Diabetologia. 2006;49:1742-1746.
43. Gigerenzer G, Hoffrage U, Ebert A. AIDS counselling for low-risk
clients. AIDS Care. 1998;10:197- 211.
44. Casscells W, Schoenberger A, Grayboys T. Interpretation by physicians
of clinical laboratory results. N Engl J Med. 1978;299:999-1001.
45. Eddy DM. Probabilistic reasoning in clinical medicine: problems and
opportunities. In: Kahneman D, Slovic P, Tversky A, eds. Judgment Under
Uncertainty: Heuristics and Biases. Cambridge, UK: Cambridge University
Press; 1982;249-267.
46. Ghosh AK, Ghosh K. Translating evidence-based information into
effective risk communication: current challenges and opportunities. J Lab
Clin Med. 2005;145:171-180.
47. Hoffrage U, Lindsey S, Hertwig R, Gigerenzer G. Communicating sta-
tistical information. Science. 2000;290:2261-2262.
48. Young JM, Glasziou P, Ward JE. General practitioners’ self rating of
skills in evidence based medicine: a validation study. BMJ. 2002;324:950-
49. Cook L. The World Trade Center attack. The paramedic response: an
insider’s view. Crit Care. 2001;5:301–303.
50. Gigerenzer G, Gaissmaier W. Heuristic decision making. Annu Rev
Psychol. 2011;62:451–482.
51. Fischer JE, Steiner F, Zucol F, et al. Use of simple heuristics to target
macrolide prescription in children with community-acquired pneumonia.
Arch Pediatr Adolesc Med. 2002;156:1005–1008.
52. Kattah JC, Talkad AV, Wang DZ, Hsieh YH, Newman-Toker DE. HINTS
to diagnose stroke in the acute vestibular syndrome. Stroke. 2009;40:3504–
Toma de decisiones heurísticas en medicina
Es posible que menos información sea más útil
cuando hay que tomar decisiones médicas?
Contrariamente a la intuición habitual de que más
información siempre es mejor, el empleo de los heu-
rísticos puede ayudar a los médicos y a los pacien-
tes a tomar decisiones acertadas. Los heurísticos
consisten en estrategias simples de decisión que
ignoran parte de la información disponible y que se
basan solamente en unos pocos predictores rele-
vantes. En este artículo se discute: 1) ¿cómo
emplean los heurísticos los médicos y los pacientes?
y 2) ¿cuándo los heurísticos superan a los métodos
ávidos de información, como las regresiones, en el
diagnóstico médico? Además, se esbozan las carac-
terísticas de los heurísticos que permiten que sean
útiles en los ambientes clínicos. Estas características
incluyen la sorprendente precisión, transparencia y
amplia accesibilidad, como también los bajos costos
y el poco tiempo requerido para emplearlos. Se
concluye explicando una de las razones estadísticas
por las cuales los heurísticos son precisos, y se señala
que la psiquiatría es un área de investigación a
futuro acerca de los heurísticos en clínica.
Prendre une décision heuristique en médecine
Une information partielle peut-elle être plus utile
quand il s’agit de prendre une décision médicale ?
Contrairement à l’idée courante qu’il est toujours
mieux d’avoir plus d’information, l’utilisation des
heuristiques peut aider le médecin et le patient à
prendre les bonnes décisions. Les heuristiques sont
des stratégies de décision simples qui font fi de l’in-
formation disponible, fondant seulement les déci-
sions sur quelques prédicteurs pertinents. Nous exa-
minons : 1) la façon dont les médecins et les patients
utilisent les heuristiques ; et 2) les moments où les
heuristiques obtiennent de meilleurs résultats que
les méthodes avides d’information, comme les
modèles de régression dans le diagnostic médical. De
plus, nous soulignons ces caractéristiques des heuris-
tiques qui les rendent utiles dans le cadre des soins
de santé : leur précision étonnante, leur transparence
et leur grande accessibilité, ainsi que le faible coût et
le peu de temps qu’il faut pour les employer. Nous
terminons en expliquant une des raisons statistiques
de la précision des heuristiques, et en mettant en
avant la psychiatrie comme un domaine de recherche
future sur les heuristiques pour les soins de santé.
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Heuristic decision making in medicine - Marewski and Gigerenzer Dialogues in Clinical Neuroscience - Vol 14 .No. 1 .2012
53. Martignon L, Hoffrage U. Why does one-reason decision making
work? A case study in ecological rationality. In: Gigerenzer G, Todd PM,
The ABC Research Group, eds. Simple Heuristics That Make Us Smart. New
York; NY: Oxford University Press. 1999;119–140.
54. Volz KG, Schooler LJ, Schubotz RI, Raab M, Gigerenzer G, von Cramon
DY. Why you think Milan is larger than Modena: neural correlates of the
recognition heuristic. J Cogn Neurosci. 2006;18:1924–1936.
55. Glöckner A, Moritz S. A fine-grained analysis of the jumping-to-
conclusions bias in schizophrenia: data-gathering, response confidence,
and information integration. Judgm Decis Mak. 2009;4:587–600.
56. Pachur T, Mata R, Schooler LJ. Cognitive aging and the adaptive use
of recognition in decision making. Psychol Aging. 2009;24:901–915.
57. Mata R, Schooler LJ, Rieskamp J. The aging decision maker: cognitive
aging and the adaptive selection of decision strategies. Psychol Aging.
58. Gigerenzer G. Gut feelings: the Intelligence of the Unconscious. New York,
NY: Viking Press; 2007.
59. Katsikopoulos K, Pachur T, Machery E, Wallin A. From Meehl (1954) to
fast and frugal heuristics (and back): new insights into how to bridge the
clinical-actuarial divide. Theory Psychol. 2008;18:443-464.
60. Pitt MA, Myung IJ, Zhang S. Toward a method for selecting among
computational models for cognition. Psychol Rev. 2002;109: 472–491.
61. Lopes LL. Three misleading assumptions in the customary rhetoric of
the bias literature. Theory Psychol. 1992;2:231–236.
62. Marewski JN, Schooler LJ, Gigerenzer G. Five principles for studying
people’s use of heuristics. Acta Psychologica Sinica. 2010;42:72-87.
63. Ärzteblatt News. Available at:
news/news.asp?id=44490. Accessed September 21, 2011.
64. Marewski JN, Olsson H. Beyond the null ritual: formal modeling of
psychological processes. Z Psychol/J Psychol. 2009;217:49–60.
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... This is problematic because humans are limited in time, attention, energy, and analytical capacity. To overcome these limitations, humans often engage in heuristic thinking, using mental shortcuts to compensate for an inability to rigorously solve complex problems (8). This can produce clinical decisions that are opaque, irreproducible, and rooted in explicit and implicit biases (9). ...
Background: Methods of machine learning provide opportunities to use real-world data to solve complex problems. Applications of these methods in laboratory medicine promise to increase diagnostic accuracy and streamline laboratory operations leading to improvement in the quality and efficiency of healthcare delivery. However, machine learning models are vulnerable to learning from undesirable patterns in the data that reflect societal biases. As a result, irresponsible application of machine learning may lead to the perpetuation, or even amplification, of existing disparities in healthcare outcomes. Content: In this work, we review what it means for a model to be unfair, discuss the various ways that machine learning models become unfair, and present engineering principles emerging from the field of algorithmic fairness. These materials are presented with a focus on the development of machine learning models in laboratory medicine. Summary: We hope that this work will serve to increase awareness, and stimulate further discussion, of this important issue among laboratorians as the field moves forward with the incorporation of machine learning models into laboratory practice.
... A heuristics approach helps decision makers stop searching before the point where collecting more information provides no additional benefit: known as the 'satisficing' threshold (Djulbegovic et al., 2018). This approach also allows experts' consideration in the analysis of complex problems such as mental health care (Marewski and Gigerenzer, 2012;Wolpert and Rutter, 2018). The 10 areas described in this analysis include both well resourced, and less well resourced, areas in urban and rural care in Australia and show a consistent pattern of care provision. ...
Standard description of local care provision is essential for evidence-informed planning. This study aimed to map and compare the availability and diversity of current mental health service provision for children and adolescents in Australia. We used a standardised service classification instrument, the Description and Evaluation of Services and DirectoriEs (DESDE) tool, to describe service availability in eight urban and two rural health districts in Australia. The pattern of care was compared with that available for other age groups in Australia. Outpatient care was found to be the most common type of service provision, comprising 212 (81.2%) of all services identified. Hospital care (acute and non-acute) was more available in urban than in rural areas (20 services [9.7%] vs 1 [1.8%]). The level of diversity in the types of care available for children and adolescents was lower than that for the general adult population, but slightly higher than that for older people in the same areas. Standardised comparison of the pattern of care across regions reduces ambiguity in service description and classification, enables gap analysis and can inform policy and planning.
... As such, heuristics are regarded as foundational for human adaptive intelligence (Gigerenzer et al., 2011;Tversky and Kahneman, 1974). Their importance finds recognition in a broad range of scholarly discussions ranging from economic change (Nelson and Winter, 1982), medicine (Marewski and Gigerenzer, 2012), marketing (Wübben and Wangenheim, 2008) to strategy Eisenhardt, 2011, Maitland andSmmartino, 2014), to name only a few. Heuristics have been postulated to be some of the most effective, and at times, the only strategy to solve intractable decision-problems (Bettis, 2017). ...
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Heuristics are central to individual and organizational behavior. Yet there is surprisingly little research that directly connects the scholarly debate around heuristics with technological forecasting and social change. In this introduction, we discuss and bring together different perspectives on heuristics, and introduce the five articles included in the Special Issue on ‘Heuristics in Technological Forecasting and Social Change’. We propose different directions for further research and point to important, so far unexplored research questions that are likely to enrich future study of heuristics in the particular context of social and technological change.
... An heuristics approach was used to identify patterns of care and gaps in service provision. This approach can be more useful than more complex analytical tools for comparing the evolution of complex mental health systems (Marewski and Gigerenzer, 2012;Wolpert and Rutter, 2018). Data on service provision were transformed into visual graphics, incorporated to the integrated atlases of mental health care in both regions, and provided online for stakeholders comments. ...
Objectives This paper compares the evolution of the psychosocial sector in two Australian regions pre and post introduction of the National Disability Insurance Scheme – a major reform to the financing, planning and provision of disability services in Australia, intended to create greater competition and efficiency in the market, and more choice for service users. Methods We used a standardised service classification instrument based on a health ecosystems approach to assess service availability and diversity of psychosocial services provided by non-government organisations in two Primary Health Network regions. Results We identified very different evolutionary pathways in the two regions. Service availability increased in Western Sydney but decreased in the Australian Capital Territory. The diversity of services available did not increase in either Primary Health Network 4 years after the reform. Many services were experiencing ongoing funding uncertainty. Conclusion Assumptions of increased efficiency through organisational scaling up, and a greater diversity in range of service availability were not borne out. Implications This study shows the urgent need for evaluation of the effects of the NDIS on the provision of psychosocial care in Australia. Four years after the implementation of the NDIS at vast expense key objectives not been met for consumers or for the system as a whole, and an environment of uncertainty has been created for providers. It demonstrates the importance of standardised service mapping to monitor the effects of major reforms on mental health care as well as the need for a focus at the local level.
... Well-known heuristic strategies include the recognition heuristic (Goldstein and Gigerenzer, 2002)-assuming a higher value for an option whose name is recognized-, the take-thebest-heuristic (Gigerenzer and Goldstein, 1996a)-compare two options by their most important criterion and ignore other criteria-and elimination by aspects (Tversky, 1972) that gets rid of alternatives that do not meet the criteria of a certain aspect. Over the years a considerable body of evidence has built up in the literature showing how human decision-makers rely on heuristic decision-making in many real-world scenarios, for example in the financial and medical professions (Julian and Gerd, 2012;Forbes et al., 2015). ...
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Bayes optimal and heuristic decision-making schemes are often considered fundamentally opposed to each other as a framework for studying human choice behavior, although recently it has been proposed that bounded rationality may provide a natural bridge between the two when varying information-processing resources. Here, we investigate a two-alternative forced choice task with varying time constraints, where subjects have to assign multi-component symbolic patterns to one of two stimulus classes. As expected, we find that subjects' response behavior becomes more imprecise with more time pressure. However, we also see that their response behavior changes qualitatively. By regressing subjects' decision weights, we find that decisions allowing for plenty of decision time rely on weighing multiple stimulus features, whereas decisions under high time pressure are made mostly based on a single feature. While the first response pattern is in line with a Bayes-optimal decision strategy, the latter could be considered as an instantiation of heuristic decision-making with cue discounting. When fitting a bounded rational decision model with multiple feature channels and varying information-processing capacity to subjects' responses, we find that the model is able to capture subjects' behavioral change. The model successfully reflects the simplicity of heuristics as well as the efficiency of optimal decision making, thus acting as a bridge between the two approaches.
Digital Twins are discussed as the new frontier of precision medicine. Using Artificial Intelligence (AI)-technologies, a virtual model of a patient’s organ, physiological structure, or whole body is built from individual health data. This virtual model can be used for drug testing, predictive analysis and risk assessment, disease modelling, or lifestyle improvement. The digital twin can feedback information and thus contribute to regulating physical processes or behavior. Most of the ethical research on digital twins is based on the assumption that they are simulations, representing structures, systems, functions, or behaviors of a given person. I suggest framing digital twins as a simulacrum, i.e. a projection of a construct, which is in itself artificial. By using simulacrum theory as introduced by Baudrillard, I investigate the connection between the epistemological and ethical implications of digital twins. The results of this analysis may be of high relevance for clinical practice, since they allow to assess the epistemological limits of digital twins, which in turn helps to evaluate the ethical risks as well as the appropriate areas of application in the light of best practice.
Patients with severe acute brain injury are left incapacitated, critically ill, and unable to make their own medical decisions. Surrogate decision-makers must make life-or-death decisions for patients and rely on clinicians' prognostication for guidance. No guidelines currently exist to guide clinicians in how to prognosticate; hence, neuroprognostication is still considered an "art" leaving room for high variability. This review examines the current literature on prognostication in neurocritical care, identifies ongoing challenges that exist in the field, and provides suggestions for future research with the goal to ameliorate variability and focus on scientific and patient-centered, rather than artistic approaches to prognostication.
Objectives There is a widely held but previously unsubstantiated belief that prescribers tend to consider and use a limited set of medications when making prescribing decisions. This study aimed to enhance understanding of the process of prescribing decision making in a real-world context. Methods Using constructivist grounded theory methodology, we conducted semi-structured interviews with 11 healthcare providers in Georgia state. The providers, most of whom are physicians of different specialties, shared their perspectives about prescribing decision making and their perceptions about using a limited set of medications in daily practice. Key findings Three themes emerged from the qualitative analysis: (1) prescribers recognized the existence of ‘small individual formularies’ and considered it helpful in simplifying prescribing decision making; (2) healthcare providers employed an algorithm to initiate and step up drug therapy for patients; (3) formulary and patient affordability played a vital role in prescribing. Conclusions Physicians and other prescribers consider and use a limited set of prescription drugs based on their internal prescribing behaviour algorithm. Strategies could be developed to help stakeholders use this information to improve medication use.
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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.
This literature review of decision making (how people make choices among desirable alternatives), culled from the disciplines of psychology, economics, and mathematics, covers the theory of riskless choices, the application of the theory of riskless choices to welfare economics, the theory of risky choices, transitivity of choices, and the theory of games and statistical decision functions. The theories surveyed assume rational behavior: individuals have transitive preferences ("… if A is preferred to B, and B is preferred to C, then A is preferred to C."), choosing from among alternatives in order to "… maximize utility or expected utility." 209-item bibliography. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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.
How do people make decisions when time is limited, information unreliable, and the future uncertain? Based on the work of Herbert A. Simon and with the help of colleagues around the world, the Adaptive Behavior and Cognition (ABC) Group at the Max Planck Institute for Human Development in Berlin has developed a research program on simple heuristics, also known as fast and frugal heuristics. These heuristics are efficient cognitive processes that ignore information and exploit the structure of the environment. In contrast to the widely held view that less complex processing necessarily reduces accuracy, the analytical and empirical analyses of fast and frugal heuristics demonstrate that less information and computation can in fact improve accuracy. These results represent an existence proof that cognitive processes capable of successful performance in the real world do not need to satisfy the classical norms of rationality. Thus, simple heuristics embody ecological rather than logical rationality. By providing a fresh look at how the mind works as well as the nature of rational behavior, the simple heuristics approach has stimulated a large body of research, led to fascinating applications in diverse fields from law to medicine to business to sports, and instigated controversial debates in psychology, philosophy, and economics. This book contains key chapters that have been previously published in journals across many disciplines. These chapters present theory, real-world applications, and a sample of the large number of existing experimental studies that provide evidence for people's adaptive use of simple heuristics.
Publisher Summary This chapter reports how stock portfolios that employed recognition heuristics fared relative to market indices, mutual funds, chance or “dartboard” portfolios, individual investment decisions, portfolios of unrecognized stocks and other benchmarks proposed by third parties.The surprising performance of recognition-based portfolios in both studies provides further evidence that simple heuristics can make accurate inferences about real-world domains. The stock market is a complex real-world environment in which lack of recognition is not completely random but systematic and simple heuristics such as recognition can exploit these regularities to make accurate inferences at little cost. The recognition heuristic does not rely on a sophisticated analysis of financial markets, the Capital Asset Pricing Model and the like. It is imperative to understand why and under what conditions this simple heuristic can survive in markets that are far removed from those situations where it served some evolutionary purpose.