behavioral decision theory
Gad Saad
Theories of decision making are either normative, prescriptive, or descriptive (Bell, Raiffa,
and Tversky, 1988; Fischhoff, 2010). Axioms
of rational choice as espoused by many classical
economists constitute the standard normative
model of human decision making. Transitivity
of preferences is an example of such an axiom
of rationality. If a consumer prefers product
A to product B, and product B to product C,
then from a rational/normative perspective it
must be the case that product A is preferred
to product C. Prescriptive theories of decision
making offer frameworks for reaching optimal
decisions wherein optimality is defined using
clearly specified mathematical metrics. For
example, the traveling salesman problem in
operations research has been studied extensively
for several decades. The objective is to identify
the shortest route to take while ensuring to visit
each of n number of cities only once each. In
this sense, proposed solutions are prescriptive
in that they offer a tangible means by which to
solve this optimization problem. The third class
of theories of decision making are descriptive
in that they offer accounts of how individuals
actually make decisions rather than how they
ought to, which is the case with prescriptive
and normative models. Much of the work in
behavioral decision theory (BDT) falls within
this third category (see Kahneman, 2011 for
a broad overview of this research stream).
More specifically, the great majority of works
within BDT has sought to identify ways by
which humans violate axioms of rational choice
(e.g., transitivity, independence of irrelevant
alternatives, procedural invariance).
Another research stream within BDT has
sought to uncover a wide range of decision rules
(or heuristics) utilized by decision makers when
making multiattribute choices. This paradigm
typically uses informational display boards
(IDB) as its methodological tool of choice. An
IDB is an n×m matrix where the n rows correspond to the n alternatives to choose from, and
the m columns are the attributes on which the
alternatives are rated. The weighted additive
rule (WADD) is the normative rule in such
an instance∑where the WADD score of alternative A =
(Wi × Ai ), for i = 1 to m. This
process is repeated for all n alternatives and
the one with the highest total WADD score is
chosen. The reality though is that individuals
seldom utilize all of the available information
(as implied by WADD) prior to choosing an
alternative. Examples of heuristics that have
been uncovered include the Lexicographic rule
(choose the alternative that scores the best on
the most important attribute), the Disjunctive
rule (choose the first alternative that passes any
of the attribute cutoffs as set by the decision
maker), the Equal Weights rule (same as WADD
but ignore the attribute weights), the Majority of
Confirming Dimensions rule (choose the alternative that is superior on the most number of
attributes), and the Satisficing rule (choose the
first alternative that meets or exceeds all of the
attribute cutoffs as set by the decision maker).
Other heuristics include the Conjunctive, Lexicographic Semiorder, Elimination-by-Aspects,
Attribute Difference, and the Frequency of
Good and Bad Features heuristics (see Payne,
Bettman, and Johnson, 1993 for an exhaustive
discussion of all of these heuristics along with
the relevant references for each of the rules).
Given the plethora of possible decision rules
that could be applied for any given decision, how
do people choose among these (meta-decision)?
In other words, when do decision makers use
the Lexicographic rule versus say the WADD
rule? The accuracy-effort framework has been
the operative framework in tackling this issue.
Each decision rule carries a differential level of
cognitive effort along with its unique relative
accuracy (e.g., WADD is more effortful than
Disjunctive, but it is also more accurate as it
processes more of the available information).
In their seminal book The Adaptive Decision
Maker, Payne, Bettman, and Johnson (1993)
provided an extensive treatise of how individuals
navigate through the effort-accuracy trade-off
as a function of a wide range of factors including
task effects (e.g., the size of the IDB) and context
effects (e.g., how correlated the attribute values
are for a given decision). For example, when
facing time pressure, it is best from an accuracy
perspective to sample a little bit of attribute
information on all alternatives than to acquire
Wiley Encyclopedia of Management, edited by Professor Sir Cary L Cooper.
Copyright © 2014 John Wiley & Sons, Ltd.
2 behavioral decision theory
all of the attribute information for only one
alternative.
That individuals do not utilize a rigid and
invariant decision process in making choices (as
assumed by classical economists) and instead
adapt their behaviors to the tasks and contexts at
hand is a central tenet of bounded rationality as
originally espoused by the late Nobel Laureate
Herbert A. Simon. Notwithstanding this important conceptual advance, BDT researchers have
spent too much of their intellectual efforts
demonstrating the ways in which our bounded
minds violate normative rationality. It is now
abundantly clear that Homo economicus is not
an accurate representation of Homo sapiens.
To avoid becoming a stagnant discipline, BDT
must move beyond an obsessive cataloging of
violations of rational choice and instead explore
the evolutionary roots of our decision making
proclivities (see Wilke and Todd, 2012 for an
exhaustive review of the nexus between evolution and decision making). Congruent with
this objective, several scholars have offered
new definitions of rationality rooted in the
recognition of the evolutionary forces that have
shaped human minds, including ecological
rationality (Gigerenzer, Todd, and the ABC
Research Group, 1999; Todd, Gigerenzer,
and the ABC Research Group, 2012), deep
rationality (Kenrick et al., 2009), and adaptive
rationality (Haselton and Nettle, 2006; Haselton
et al., 2009).
Stopping strategies constitute another fertile
area of research within the BDT literature.
A central element of any decision whether
choosing a mate, accepting a job offer, hiring
an employee, or purchasing a car, is determining when to terminate the information
search process. Behavioral decision theorists
have uncovered several such stopping strategies
using the sequential choice paradigm. Sequential choice refers to the fact that at each step of a
decision, individuals can either stop and choose
one of the alternatives, or continue to acquire
additional information. In some instances,
sequential choice is attribute-based, namely the
number of alternatives is fixed (typically at two)
but the amount of attribute information that
can be acquired on the competing alternatives
can vary (Saad and Russo, 1996). For example,
within the American political landscape, the
last step in a presidential election is deciding
between the two final remaining candidates.
In other cases, the sequential choice process is
alternative-based, as per the classic secretary
problem (see Ferguson, 1989 for a review).
Suppose that one has to hire a new secretary
out of a sample of n possible candidates, and
that it is crucially important that the one that
is hired is the best of the n candidates. If n
is large, it is likely implausible to interview
all of the available candidates. Rather, some
optimal stopping strategy must be used that
maximizes the likelihood that the best candidate
is chosen without having to sample all n options.
Candidates are interviewed one at a time, and at
each step one must decide whether to stop and
choose the current interviewee or proceed by
interviewing another candidate, without having
the ability to go back and choose a previously
rejected candidate. In this class of sequential
choices, the iterative counter is over the number
of alternatives to evaluate prior to making a final
choice. Irrespective of whether the sequential
choice is attribute-based, alternative-based, or
a combination of both (Saad, Eba, and Sejean,
2009, study 2), all such models recognize that
contrary to normative rationality, individuals
do not acquire all of the obtainable and pertinent information prior to selecting the winning
alternative.
Whether identifying violations of rational
choice, or documenting the wide array of decision rules and stopping strategies used when
individuals are facing multiattribute choices, the
explanatory powers of BDT have been substantial. It is not surprising then that innumerable
fields have incorporated BDT principles within
their theoretical purviews including marketing,
accounting, finance, risk analysis, medicine,
psychology, law, negotiations, and political
science.
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behavioral decision theory
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