Psychology 811
2011. Vol.2, No.8, 811-816
Copyright © 2011 SciRes. doi:10.4236/psych.2011.28124
Decision Making Styles and Adaptive Algorithms
for Human Action
Mauro Maldonato1, Silvia Dell’Orco2
1University of Basilicata, Potenza, Italy;
2University of Macerata, Macerata, Italy.
Received May 11th, 2011; revised July 18th, 2011; accepted September 21st, 2011.
Without human beings’ ability to chooseand in such a way give order to a universe which, in the beginning,
must have presented itself as a chaotic mass of data without clear structures and regularityevolution would
have been unthinkable, even more inconceivable if one considers the fact that the adaptation to that universe
must have taken place on the basis of incomplete, fragmentary information and above all starting from limited
cognitive capacities and restricted time limits. In order to respond to the challenges of the environment, an indi-
vidual had to first of all be quick: quick in the reaction to the attack of a predator and in the gaining of an escape
route, in deciding how to pursue pray, in obtaining gains from territory that others were using at that same mo-
ment, in the selection of a partner and of a place in which to take refuge and so forth. Therefore, if it is true that
evolutionary pressure urged the human mind to accumulate information by means of a significant quota of ra-
tional decisions, the vast majority of human choices have been favoured by ecological decision making strate-
Keywords: Naturalistic Decision Making, Ecological Rationality, Fast and Frugal Heuristics, Decision Making
In the last half century, a growing amount of experimental
research on decision making behaviour (Hastie & Dawes, 2001)
has investigated the systematic deviations from the axioms of
neo-classical economic theory, which is based on the hypothe-
sis of perfect or instrumental rationality (Bernoulli, 1738/1954;
von Neumann & Morgenstern, 1947). Around the middle of the
1900’s, while many economists were embracing Friedman’s as
if perfectionist thesis (1953)according to which individuals
behave as though they were able to perform the complex calcu-
lations required by the normative modelSimon opened up a
gateway in psychological and economic reflection by remark-
ing upon the implausibility of an abstract rationality which
denies both the limits of the external surroundings (task envi-
ronment) and the imperfect cognitive structure of human beings.
A decision, according to Simon, is not the mere algorithmic
processing of a set of data, but rather an adaptive process which
allows one to reach a dynamic balance between an efficient,
quick and economical course of action, the progressive adjust-
ments of the solution and, finally, the configuration that reality
should have following the solution of a problem. Simon’s ideas
would open up the way to a vast series of experimental research
projects on the “deviations” of individual behaviours from the
predictions of neo-classical economic theory: research that
would be powerfully relaunched by Kahneman and Tversky,
who would show with precision the non-causality and sys-
tematicity of such deviations. As is known, at the foundation of
Simon’s bounded rationality is the idea that human decisions
are not governed by logico-formal procedures, but by heuristics:
cognitive devices potentially the cause of distortions (biases),
but extremely efficient systems used by the human mind to
reduce the cognitive load and respond quickly and efficiently to
problems presented by the environment (Hamilton & Gifford,
1976; Nisbett & Ross, 1980). They are, in other words, un-
planned informal reasoning strategies, which allow individuals
to make sustainable choices for the computation and the proc-
essing of information, choices fitting with the complexity of the
If neo-classical theory considers information to be a scarce
and negotiable good, by the same standards as any other ge-
neric good or factor of production, several authors (Marschak &
Radner, 1972; Hey, 1979) point out instead that information is
not always scarce but above all that, in conditions of overabun-
dance, it could go unperceived and remain unprocessed by de-
cision makers. Overabundance, complexity, heterogeneousness
and limited subjective interpretative capacities call the neo-
classical analysis of information back into question (Stigler,
1961), according to which information can be measured in
terms of a cost-benefit analysis. Given that incoherency in as-
pirations and cognitive incompletenessgenerated by the scar-
city or the excess of the information to be processedchara-
cterize the most wide-ranging contexts of individual choice, the
decision maker must adapt to flexible conditions using learning
operations which reduce the complexity of the calculations
required in order to make a decision. In the descriptive ap-
proach to decision making individual choices are determined
not only by a few complete and coherent objectives and by the
properties of the external world, but also by the knowledge that
decision makers have of the world, by their ability to call up
such knowledge at the right moment, to formulate the conse-
quences of their own actions, to foresee the course of events, to
face uncertainties (including those deriving from the possible
reactions or responses of other actors), and finally by their abil-
ity to choose between their own various competing needs
(Simon, 2000). In this sense, because of the high adaptive value
of the forms of reasoning which determine it, bounded rational-
ity cannot be considered irrational, nor can it be called upon
solely to explain human error. As Selten (1998) observed, it is
possible to construct theories of limited rationality in which
behaviour, while not being optimal, is anything but irrational. If
in an absolute rational order the alternatives are given, in a
limited rational order they must be invented each time by the
agent, in a process which generates many possible courses of
action (Simon, 1997). Whether dealing with a company, a bio-
logical species or an individual, adaptation to one’s environ-
ment, according to Simon, always depends on a heuristic search
and on forms of local optimization or satisficing (Simon, 1983).
The search for alternatives ends with the alternative that, ac-
cording to the circumstances, best satisfies our objectives and
needs. In this sense, an evolutionary theory of rationality must
include a search theory which does not adhere to the rules of
“normative arrest” (March, 1994)according to which the
search for alternatives ends only after having reached an ideal
optimizing resultbut rather one that is concentrated on per-
sonal aspiration levels.
The Paradigm Shift: Heuristics and Biases
Behavioural Economics, which originated in Simon’s re-
search, attempts to integrate the classical theory of rational
choice with new hypotheses coming from psychologyin par-
ticular from experimental psychology (Mullainathan & Thaler,
2000)thus shifting the attention from substantive rationality
to procedural rationality. Search and satisficing (Simon, 1979)
are the key terms of the limited-rationality decision making
process: agents review, one after another, the decisional alter-
natives available and stop when such a search reaches a certain
threshold of satisfaction (even if this is only an implicit one).
When faced with an economic decision, an individual behaves
like a chess player who has to choose his next move. Both an
economist and a chess player reason according to procedures.
The winning strategy is however constructed gradually and not
in advance, according to a tree-shaped schema, and reformu-
lated at each step of the game based on the adversary’s coun-
termoves. Moreover, in economics, as in the game of chess,
success is often due to the fact that human beings are simply
equipped with good intuition and efficient judgement (Simon,
1983). It is no coincidence that the famous Russian chess player
Kasparov (2007) maintains that in a game of chess, as in real
life, it is not always possible to analytically assess every single
possible action. Every move can in fact lead to an infinity of
possible positions, each one of which is the result of a chain of
cause and effect which must be carefully examined. In many
cases, especially in situations where time is limited, emotion
and instinct can cloud even the sharpest strategy and suddenly
transform a chess match into a game of fortune. With the Heu-
ristics and Biases Approach research program, developed by
Kahneman and Tversky in the 1970’s, the concept of heuristics,
already introduced by Simon, took on experimental validity,
becoming the mainstay of a realistic model of the rational agent.
This program consists of experimentally subjecting decision
making problems, opportunely concocted, to samples of indi-
viduals in order to verify whether or not they reason and make
decisions according to rational criteria. In the eyes of Kahne-
man, Slovic and Tversky (1982) heuristic judgement is the only
practical way to evaluate uncertain elements. Unlike formal
calculation, the heuristic evaluation of probability is based in
general on immediate solutions which consider only some of
the factors at play: the particular characteristics of the object
under evaluation, the way in which the problem is formulated,
the clarity with which the situation is described and so forth
factors which influence separately or in a combined way on
decision making behaviour. If the results of such studies have,
on the one hand, supplied important clues as to the nature of
human cognitive processes (contributing to the de-construction
of the homo oeconomicus model), they have on the other hand
given the concept-term “heuristics” negative connotations be-
cause of its strong connections with the term bias. Bias, in fact,
is commonly defined as the difference between human judge-
ment and a rational “norm”, often considered as a logical or
statistical law of probability; almost as though, once the biases
within human reasoning are avoided, one could reach optimal
decisions. The double process theory of Kahneman and Tver-
sky is based on the idea that people, when expressing an opin-
ion or a decision, use two different cognitive systems: intuitive
processes (system 1) and analytical processes (system 2) (Kah-
neman & Frederick, 2002; Stanovich, 1999). System 1 is primi-
tive, fast and associative; system 2 is slow, serial and deductive.
System 1 produces a rapid response which can subsequently be
approved, corrected or substituted by system 2 (even though
this is rather infrequent). During the message comprehension
process the attributes highly accessible to system 1 (similarity,
availability, affection) become heuristic attributes for the final
decision. In other words, intuitive judgement comes about if
one uses a very accessible attribute (processed by perception or
by system 1), and if the inspection by system 2 fails. In other
terms, rational behaviour can still be defined as such if, and
only if, it conforms to the laws of logic and to the theory of
Towards an Ecological Rationality
The recognition of these limits has fostered the conditions for
a different approach to the study of decisions. At the end of the
last century Naturalistic Decision Making (NDM) was born, a
paradigm with the objective of studying the way in which peo-
ple make decisions and perform complex cognitive functions
when dealing with real world problems: namely in situations
characterized by time limits, incomplete knowledge of the al-
ternatives, emotional tension, uncertainty, poorly defined ob-
jectives, high stakes and decision makers with various levels of
experience. The study of decision making does not pertain to
the mere choice among the available alternatives on the basis of
their Expected Utility, but to the natural procedures followed by
decision makers before carrying out an action. Such procedures
are composed of three basic principles:
The decisions are made based on a holistic evaluation of
the potential actions, an evaluation performed on the basis
of the available options as well as on the comparison be-
tween the specific characteristics of those options (Lip-
shitz et al. 2001);
The decision maker chooses to act not on the basis of the
search for and the detailed processing of the alternatives,
but through a process of situation recognition (the recog-
nition-based heuristic) which is carried out by comparing
the alternatives and the potential courses of action among
themselves (pattern-matching) based on a few criteria of
The decision makers do not look for an optimal solution,
but adopt a satisficing choice criterion (Klein & Calder-
wood, 1991).
Gigerenzer (2001) has pointed out how homo heuristicus
loses out, in terms of efficient behaviour, in comparison with
homo oeconomicus only when the axioms and the standards of
normative rationality are involved. The capacity of individuals
to make adaptive decisionsmodifying their own cognitive
strategies in relation to the context and to the structural muta-
tions of the decisional problemproposes to the decision
maker a framework that is sufficiently optimistic in terms of the
rationality of the behaviour (Payne, Bettman, & Johnson, 1993).
This inspired Gigerenzer to propose a revision of the classical
concept of heuristics. If for Kahneman and Tversky heuristics
are cognitive strategies (the cause of biases which compromise
the making of correct decisions with regard to normative stan-
dards) for the German psychologist heuristics are perfectly
adaptive fast and frugal rules which function within the con-
straints of the environment (limited time, insufficient informa-
tion etc.) and the cognitive-computational limits of the decision
maker (Tietz, 1992). The correspondence between the decision
maker’s mind and the environment is the turning point for an
ecological redefinition of rationality (Todd & Gigerenzer,
2000). Fast and frugal heuristics consist of three fundamental
The search rule: this defines the principle according to
which heuristics guide the search for information and for
decisional alternatives within a limited timeframe (the
search is not extensive as in the theory of rational choice)
and without performing calculations;
The stopping rule: this includes the principles which
specify how and when the search procedure must come to
an end. In line with Simon’s theory of bounded rationality,
given the cognitive and environmental limits of real world
problems, the search is ended on the basis of satisficing
processes (Richardson, 1998) and not optimizing ones;
Heuristic principles: fast and adaptive decision making
procedures which, despite their frugal nature, can be very
accurate compared to classical algorithmic computation.
The so-called Take the Best heuristic, proposed by Gigeren-
zer (1997), outlines a satisficing choice criterion, although it is
mostly used for decisions between two specific objects. This
procedure is represented by a grid (Figure 1) in which the col-
umns indicate the alternatives and the horizontal lines indicate
the criteria or cues.
The alternatives (a, b, c, d) are examined two at a time
through criteria or cues organized in a decreasing order on the
basis of the validity that the agent assigns to them. The basic
criterion is called recognition and is subjective. The following
cues (in the example: cues 1-2-3-4-5) are of an ecological na-
ture and are related to the specific context of the choice. In the
example the values assigned to the cues are: positive (+), nega-
tive () or uncertain (?). The procedure works in the following
way. Let us suppose that an agent must choose which company,
A or B, has the greatest number of employees. On the basis of
the first criterion (recognition) the agent must only “recognize
Figure 1.
Take the best algorithm. Source: Gigerenzer e Goldstein, 1996.
(+) or not recognize () the object”. We will suppose, as in the
example, that the agent recognizes both of the objects. In such a
case, on the first line, recognition, we will have two + signs. At
this point, because the criterion of identification does not allow
the agent to distinguish between the two objects, she moves on
to consider the first “ecological” cue (cue 1): “the company has/
does not have sub-units”. The agent is aware of the fact that
company A has sub-units and that company B does not (on the
line cue 1 we will have one + and one –). In such a case the
agent does not need to proceed further: company A has more
employees than company B. In other words, only four values
were considered (the grey shaded area in Figure 1) out of
twelve. Suppose at this point that we have to repeat the whole
procedure for the objects B and C. As in the first case, both
pass the recognition test (two + signs on the first line). The
agent thus moves on to the first cue (cue 1). In this case she
knows that company B does not have subunits, but does not
know whether company C has any or not (?). In such a case she
proceeds to the second cue: for example “the company invests/
does not invest in the retraining of its personnel”. The agent
knows that company B satisfies this cue (+) and that company
C does not (). At this point the criterion allows for the agent to
distinguish between the two objects and the process stops. Six
values have been considered (the area within the dotted line)
out of twelve. If, for example, for the first criterion the agent
had not recognized one of the two objects (C and D) the choice
would have fallen to the recognized object and the process
would have ended there. It is evident that if the agent does not
recognize the object she cannot act on any cue (the column
under the object C is in fact made up of only?). The decision
making process is therefore governed by an identification heu-
ristic: the only condition necessary for making a choice is that
one of the two available options has not been recognized. A
criterion that allows for the differentiation between two objects
is the best compared to other criteria.
The distance between this approach and the inferential ap-
proach in which all of the available attributes are considered in
a compensatory way is evident. Here, instead, there are no
mathematical calculations to be performed or averages to be
calculated, because the characteristics of each option are con-
sidered in a non-compensatory way. This type of “fast and fru-
Figure 2.
Flow diagram of a fast and frugal heuristic: take the best.
gal” heuristic search is based on a stopping rule called one rea-
son, according to which the choice of an option is based on
only one cue and on information that satisfies an optimal crite-
rion for the decision maker (Figure 2). In such a case one can
speak of ignorance-based decision making, which generally
represents the first phase of all of the one reason type decisions.
The importance of this family of heuristics resides in the very
type of ecological rationality implicit in their process. In line
with Simon’s structure, in particular with regards to the adap-
tive and procedural aspect with which agents choose their own
courses of action, Gigerenzer proposes the metaphor of the
Adaptive Toolbox (Gigerenzer, 2001). This is a sort of “tool-
box” composed of a repertory of evolved heuristics which pos-
sess the following characteristics:
They are specialized for certain tasks;
From a computational point of view they are simple, fru-
gal and fast;
They do not have the problem of formal coherence, but
rather that of adaptive efficiency;
They resolve here and now problems related to the chal-
lenges presented by the environment (obtaining food,
avoiding predators, finding a partner and a secure refuge,
but also, at a higher level, exchanging goods, making a
profit and so on).
Every individual chooses to use, as necessary, the heuristic
best adapted to the task to be completed; during the completion
of the task, the heuristic may also be substituted. In order to
describe the nature of the “toolbox”, Gigerenzer (2001) uses the
image of a mechanic and sales person of used parts in an iso-
lated area, who possesses neither the tools, nor all of the neces-
sary spare parts, but who when faced with a problem tries to
find a solution with the tools that he has at his disposal. “These
evolved capacitiesexplains Gigerenzerare the metal from
which the tools are made. A gut feeling is like a drill, a simple
instrument whose force lies in the quality of its material” (2007,
p. 63). Such heuristics function well in natural situations, where
the presence of limits in terms of time, knowledge and compu-
tational capacity make the adoption of fast and efficient strate-
gies preferable. In reality, liberated from their traditional nega-
tive connotations, heuristic strategies have become more than
just deviations from a rational “norm”. They respond to an
ecological and adaptive rationality which allows individuals to
efficiently face situations of uncertainty, risk and missing in-
formation typical of the reality in which we live.
Decision Making Styles
Another model developed in the domain of NDM is known
as the recognition-primed model. Analyzing the decisions of
experts (doctors, military commanders, fire fighters, pilots and
others) Klein and colleagues (1993) have shown how in critical
situations these experts do not follow normative models. In
such contexts the decision making process is characterized by
drastic time limits and, in the case of a missed or erroneous
decision, by grave consequences. If, for example, in an emer-
gency situation the head fire fighter does not decide efficiently
and in a few seconds what to do, he risks putting the lives of
many people in jeopardy. Often the objectives are not clear
(save the people in the building or quickly put out the fire?), the
information is uncertain (the firefighter does not have a clear
idea of the building’s floor plan or of the material contained in
the building) and the intervention procedures are not always
codified (one needs to use one’s imagination in order to find a
way to free a wounded person from inside a vehicle after an
accident). Experts of all fields make decisions by quickly refer-
ring to well-known situations and past experience. In particular,
they promptly identify the objectives to be pursued, the most
important cues to observe and monitor, the possible situational
developments and the plans of action to be followed. In other
words, the assessment of the efficiency of a selected course of
action (or, better yet, of one automatically recalled by memory)
does not come about through a comparison with other actions,
but by directly discovering a plausible, and therefore satisfac-
tory, solution. The decision making models based on recogni-
tion (Klein, 1998) are inspired by counter-intuitive observation.
Experts make decisions without analytically assessing the pros
and cons of each option: beginners or individuals without ex-
perience are in fact the ones who make decisions on an analyti-
cal-comparative basis.
However, individual differences in decision making behav-
iour are not only related to the decision maker’s level of exper-
tise, but also to other variables such as cognitive and motiva-
tional styles, age, sex, socio-economic status and still others.
Some authors, for example, have hypothesized the existence of
veritable decision making styles (Scott & Bruce, 1995), which
define styles of individual reaction within given contexts. Now,
if it is true that decision making styles exist whereby individu-
als use some with more frequency than others, it is just as true
that these styles are not rigid and unchangeable (Glaser & We-
ber, 2005), but rather flexible and modifiable in response to
specific situations (Driver, Brousseau, & Hunsaker, 1990).
Numerous decision making styles have been identified and
described. The simplest ones follow the model of a sort of bi-
polarity corresponding to specific decision making styles. The
deliberative-intuitive dimension (Epstein et al. 1996) is an ex-
ample of this. This dimension distinguishes between individu-
als who usually decide in an analytical and reflective manner
and others who, instead, decide in a quick and intuitive way.
Other typologies of more detailed decision making styles in-
stead describe multiple dimensions. Scott and Bruce (1995)
identify five different decision making styles:
The rational style: characterized by a complete search for
information, by the consideration of the possible alterna-
tives and by the assessment of their consequences;
The intuitive style: based on the attention to global as-
pects more than to the systematic processing of informa-
tion and, in addition, on the tendency to decide on the ba-
sis of intuition and feelings;
The dependent style: typical of people who prefer to re-
ceive suggestions before making any choice at all;
The evasive style: typical of individuals who tend to put
off or avoid decisions;
The spontaneous style: characterized by the propensity to
decide as fast as possible.
In order to measure these decision making styles, Scott and
Bruce developed the General Decision Making Style Inventory
(GDMS) for defining individual decisional profiles. A different
approach to distinguishing between the diverse ways of deci-
sion making was proposed by Schwartz and his group (2002)
and includes, rather than the identification of a specific decision
making style, the tendency of an individual to look for the best
possible result (the “optimizer”) or to settle for a sufficiently
good alternative (the “satisficer”). The Maximization Scale
(Schwartz et al., 2002) is a tool for distinguishing those who,
always looking for the best option, tend to base their own deci-
sions on the comparison with others, and then prove to be un-
satisfied with the choice made; from those who, instead, in
settling for an option which is good enough, and therefore not
necessarily the best, show a fair level of satisfaction with re-
spect to their decision. It must also be said that more than a
century ago James had already outlined a profile of the decision
making types.
The first may be called the reasonable type. It is that of those
cases in which the arguments for and against a given course
seem gradually and almost insensibly to settle themselves in the
mind and to end by leaving a clear balance in favor of one al-
ternative, which alternative we then adopt without effort or
constraint [...]. A “reasonable” character is one who has a store
of stable and worthy ends, and who does not decide about an
action till he has calmly ascertained whether it be ministerial or
detrimental to any one of these [...]. In the second type of case
our feeling is to a certain extent that of letting ourselves drift
with a certain indifferent acquiescence in a direction acciden-
tally determined from without, with the conviction that, after all,
we might as well stand by this course as by the other, and that
things are in any event sure to turn out sufficiently right. In the
third type the determination seems equally accidental, but it
comes from within, and not from without [...]. There is a fourth
form of decision, which often ends deliberation as suddenly as
the third form does. It comes when, in consequence of some
outer experience or some inexplicable inward change, we sud-
denly pass from the easy and careless to the sober and strenu-
ous mood, or possibly the other way [...]. All those “changes of
heart”, “awakenings of conscience”, etc., which make new men
of so many of us, may be classed under this head [...]. In the
fifth and final type of decision, the feeling that the evidence is
all in, and that reason has balanced the books, may be either
present or absent. But in either case we feel, in deciding, as if
we ourselves by our own wilful act inclined the beam: in the
former case by adding our living effort to the weight of the
logical reason which, taken alone, seems powerless to make the
act discharge; in the latter by a kind of creative contribution of
something instead of a reason which does a reason’s work
(James, 1950, pp. 796-798).
Empirical and theoretical research developed within the do-
main of the psychology of decision making suggests that cogni-
tive strategies follow paths that are often different from those
postulated by economic rational choice. According to the model
of the adaptive decision maker developed by Payne, Bettman
and Johnson (1993), the decision making process is a highly
contingent form of processing information with which indi-
viduals use adaptive decision making strategies and heuristics
in response to their limited capacity for processing information
as well as to the complexity of decisional tasks. A decision
making strategy is a sequence of conative and cognitive mental
operations (actions on the environment) used in order to trans-
form the state of initial knowledge into final knowledge in
which the decision maker considers the decisional problem to
be resolved. Cognitive strategies are selected in relation to a
series of factors: the way in which the information in presented,
the complexity of the problem, the decision making context and
the characteristics of the decision maker (Hastie & Dawes,
2001). Such variables, regardless of the values of the alterna-
tives, influence the selection of the strategies by modifying the
cognitive effort necessary for implementing them (Bettman,
Everyday experience shows that, when facing various situa-
tions, we make decisions in a non-stereotypical way. A funda-
mental characteristic of our cognitive system is in fact the ex-
traordinary flexibility of the decision making strategies at our
disposal. First of all, when “deciding how to decide”, individu-
als consider accuracy and cognitive effort not as absolute at-
tributes connected to a strategy, but rather as properties de-
pendent on a specific situation. Such an assessmentestab-
lished either beforehand (top-down style) or during the accom-
plishment of the task (bottom-up style) and the processing of
the decision itselfcan influence the choice of the various
decision making strategies at one’s disposal. The strategy cho-
sen will be the one that allows the decision maker to make a
good decision with the least possible effort. The most frequent
simplification strategies (Payne, Bettman, & Johnson, 1993) are
commonly classified as compensatory and non compensatory.
Compensatory strategies require a quantitative judgement and
are applied when the options or the attributes which describe
the various decisional alternatives are commensurable on the
basis of their attractiveness/utility values. In other words, an
individual chooses the alternative having an attribute that com-
pensates for the sacrifice that she is willing to make by re-
nouncing the consideration of other appreciable attributes.
Non compensatory strategies, instead, are used for those de-
cision making problems in which options and criteria are inc-
ommensurable and the limited attractiveness of an option in
relation to a certain criterion cannot be compensated by the
greater attractiveness of the same option in relation to another
criterion. Individuals often have to mediate between accuracy
and effort in the selection of a strategy according to the re-
quirements of the task: in such cases a certain flexibility is nec-
essary in the use of the strategies to be adopted. The decision
making process, considered as a limited capacity cognitive
activity, in fact aims at satisfying several objectives, such as for
example minimizing emotional strain due to the presence of
conflictual values among alternatives (Hogart, 1987), reaching
socially acceptable and justifiable decisions, and making accu-
rate decisions which maximize advantages and minimize the
cognitive effort required for acquiring and processing informa-
tion (Simon, 1978). Minimizing cognitive effort is defined on
the basis of the amount of time and the type of mental operation
required for putting a certain decision making strategy into
action. Zipf (1949) proposes the principal of minimal cognitive
effort, according to which a strategy is chosen that ensures the
minimum effort in the reaching of a specific desired result. The
strategies that involve more accurate choices are often those
that entail more effort and this indicates how the choice of
strategies is the result of a compromise between the desire to
make the most correct decision and the desire to use the small-
est amount of effort (Johnson & Payne, 1985). Conclusions
In the next few years, and with the ever more accurate con-
tribution of the cognitive neurosciences, we could have further
elements to reflect upon in this difficult field. However, we can
certainly already affirm that without high performance decision
making devices like those studied in the paradigm of Naturalis-
tic Decision Making the building of civilization, and perhaps
even the evolution of the species, would have been impossible.
Perhaps it is not paradoxical to think that decision making de-
vices developed starting from the cognitive limitations of hu-
man beings, revealing themselves to be flexible when faced
with unexpected situations and, above all, ecological in the use
of the environment’s resources. In this sense, if it is true that the
human mind has accumulated information and knowledge by
means of a significant quantity of rational decisions, the vast
majority of these decisions have been supported by a natural
logic whose rules have proved themselves to be advantageous
in an evolutionary sense.
Bernoulli, D. (1738). Specimen theoriae novae de mensura sortis. Com-
mentarii Academiae Scientiarum Imperialis Petropolitanae, 5, 175-
Bettman, J. R. (1993). ACR fellow’s award speech: The decision maker
who came in from the cold. In L. McAlister and M. Rothschild (Eds.),
Advances in consumer research (pp. 7-11). Provo, UT: Association
for Consumer Research.
Driver, M. J., Brousseau, K. R., & Hunsaker, P. L. (1990). The dynamic
decision maker. New York: Harper and Row Publishers.
Epstein, S., Pacini, R., Denes-Raj, V., & Heiner H. (1996). Individual
difference in intuitive-experiential and analytical-rational thinking
styles. Journal of Personality and S ocial Psychology, 71, 390-405.
Friedman, M. (1953). The methodology of positive economics. In M.
Friedman (Ed.), Essays in positive economics (pp. 2-43). Chicago, IL:
University of Chicago Press.
Gigerenzer, G. (1997). Bounded rationality: Models of fast and frugal
inference. Berlin: Max Planck Institute for Human Development.
Gigerenzer, G. (2001). The adaptive toolbox. In G. Gigerenzer and R.
Selten (Eds.), Bounded rationality: The adaptive toolbox (pp. 37-50).
Cambridge: MIT Press.
Gigerenzer, G. (2007). Gut feelings: The intelligence of the uncon-
scious. New York: Penguin Books.
Gigerenzer, G. & Goldstein, D. G. (1996). Reasoning the fast and fru-
gal way: Models of bounded rationality. Psychological Review, 103,
650-669. doi:10.1037/0033-295X.103.4.650
Glaser, M. & Weber, M. (2005). Overconfidence and trading volume.
C.E.P.R. Discussion Paper, 3941. London: C.E.P.R.
Hamilton, D. L. & Gifford, R. K. (1976). Illusory correlation in inter-
personal perception: A cognitive bases of stereotypic judgments.
Journal of Experimental and Social Psychology, 12, 136-149.
Hastie, R. & Dawes, R. M. (2001). Rational choice in an uncertain
world: The psychology of judgment and decision making. Thousand
Oaks: Sage.
Hey, J. D. (1979). Uncertainty in microeconomics. Oxford: Martin
Hogart, R. M. (1987). Judgement and choice: The psychology of deci-
sion. New York: Wiley.
James, W. (1950). The principles of psychology. New York: Dover
Johnson, E. J. & Payne, J. W. (1985). Effort and accuracy in choice.
Management Science, 31 , 394-414. doi:10.1287/mnsc.31.4.395
Kahneman, D. & Frederick, S. (2002). Representativeness revisited:
Attribute substitution in intuitive judgment. In T. Gilovich, D. Griffin,
& D. Kahneman (Eds.), Heuristics and biases: The psychology of
intuitive judgment (pp. 103-119). Cambridge: Cambridge University
Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment under un-
certainty: Heuristics and biases. Cambridge: Cambridge University
Kasparov, G. (2007). How life imitates chess. New York: Bloomsbury
Klein, G. A. (1993). Recognition-primed decisions. In W. B Rouse
(Ed.), Advances in man-machine systems research (pp. 47-92).
Greenwich: JAI Press.
Klein, G. A. (1998). Sources of power: How people make decisions.
Cambridge: MIT Press.
Klein, G. A. & Calderwood, R. (1991). Decision model: Some lessons
from the field. IEEE Transactions on Systems Man and Cybernetics,
21, 1018-1026. doi:10.1109/21.120054
Lipshitz, R., Klein, G., Orasanu, J., & Salas E. (2001). Taking stock of
naturalistic decision making. Journal of Behavioral Decision Making,
14, 331-352. doi:10.1002/bdm.381
March, J. (1994). A primer decision making. How decision happen.
New York: The Free Press.
Marschak, J. & Radner, R. (1972). Economic theory of teams. New
Haven: Yale University Press.
Mullainathan, S. & Thaler, R. H. (2000). Behavioral economics. Cam-
bridge, MA: National Bureau of Economic Research (NBER). Work-
ing Paper 7948.
Neumann, J. von & Morgenstern, O. (1947). Theory of games and
economic behavior. Princeton: Princeton University Press.
Nisbett, R. E. & Ross, L. (1980). Human inference: Strategies and
shortcomings of soci a l j u d gm e n t . Englewood Cliffs: Prentice Hall.
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive
decision maker. Cambridge: Cambridge University Press.
Richardson, R. C. (1998). Heuristics and satisficing. In W. Bechtel and
G. Graham (Eds.), A companion to cognitive science (pp. 566-575).
Oxford: Blackwell Publishers.
Schwarz, N. & Vaughn, L. A. (2002) The availability heuristic revisited:
Recalled content and ease of recall as information. In T. Gilovich, D.
Griffin, & D. Kahneman (Eds.), The psychology of intuitive judgment:
Heuristics and biases (pp. 103-119). Cambridge: Cambridge Univer-
sity Press.
Scott, S. G. & Bruce, R. A. (1995). Decision making style: The devel-
opment of a new measure. Educational and Psychological Meas-
urement, 55, 818-831. doi:10.1177/0013164495055005017
Selten, R. (1998) Aspiration adaptation theory. Journal of Mathemati-
cal Psychology, 42, 191-214. doi:10.1006/jmps.1997.1205
Simon, H. A. (1983). Reason in human affairs. Stanford: Stanford
University Press.
Simon, H. A. (1997). Models of bounded rationality. Boston: MIT
Simon, H. A. (2000). Scienza economica e comportamento umano.
Torino: Edizioni di Comunità.
Simon, H. A. (1979). Rational decision making in business organization.
Nobel Lecture, Stocholm 1978. American Economic Review, 69,
Simon, H. A. (1978). Information-processing theory of human problem
solving. In W. K. Estes (Ed.), Handbook of learning and cognitive
processes (pp. 271-295). New York, Hillsdale: Erlbaum.
Stanovich, K. E. (1999). Who is rational? Studies of individual differ-
ences in reasoning. Hillsdale: Erlbaum.
Stigler, G. (1961). The economics of information. Journal of Political
Economy, 69, 213-225. doi:10.1086/258464
Tietz, R. (1992). Semi-normative theories based on bounded rationality.
Journal of Economic Psychology, 13, 297-314.
Todd, P. M. & Gigerenzer, G. (2000). Précis of simple heuristics that
make us smart. Behavioral an d Brain Sciences, 23, 727-780.
Zipf, G. K. (1949). Human behavior and the principle of least effort.
Cambridge: Addison Wesley Press.