Behavioural Assumptions Overlooked in Travel-Choice Modelling

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In J. Orthúzar, S. Jara-Diaz & D. Hensher (Eds.) (1998), Transport modeling (pp. 3-18). Oxford: Pergamon.
BEHAVIORAL ASSUMPTIONS OVERLOOKED IN TRAVEL-CHOICE
MODELING
Tommy Gärling
Department of Psychology
Göteborg University
Haraldsgatan 1, S-41314 Göteborg, Sweden
Phone (voice) +46 317731881, (fax) +46 317734628
Email: Tommy.Garling@psy.gu.se
ABSTRACT
The substantial theoretical basis of travel-choice modeling is criticized for being an inaccurate
description of how people make choices. Drawing on research in the behavioral sciences in
general and psychology in particular, several alternative behavioral assumptions are proposed.
These include problem solving in connection with interdependent choices; information
acquisition, representation, and use preceding choices; accuracy-effort tradeoffs in the
application of decision rules for making isolated choices; the constraining influence of social
factors on selfish motives; and planning and automatization in the implementation of choices.
Although less simple and elegant than the current theory, such a set of more valid behavioral
assumptions is in particular needed in a field concerned with applications.
INTRODUCTION
In travel-choice modeling a clear distinction is not always made between the statistical theory,
on the basis of which techniques for estimating model parameters are derived, and the
substantial theory which must guide any modeling of a real-world process (such as making
choices). It is unfortunate for the field that the former seems to have received much more
attention than the latter. In this paper I will focus exclusively on the substantial theory. The
point I am trying to make is that this theory overlooks several important behavioral
assumptions.
In reviewing the proceedings of two previous travel behavior conferences (Gärling, 1993), I
made the observation that behavioral assumptions are almost always made without reference to
existing theories in the behavioral sciences. To some extent I feel this is so because of
ignorance. Another important reason is that behavioral-science theories are seldom
quantitative.
The first reason should be relatively easy to do something about. However, with regard to the
second reason, travel-choice modellers may need to realize that quantitative behavioral theories

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may not be attainable. At least this is the point Simon (1990) makes in a major paper. He
argues that we may be unjustified in believing that it will ever be possible to discover
quantitative laws that apply to human behavior. If taking this statement seriously, quantitative
forecasting of, for instance, travel demand may never be feasible. New ways of approaching
forecasting problems are therefore called for.
Following my criticism in the next section, I will attempt to provide alternative behavioral
assumptions based on current research in the behavioral sciences (in particular psychology).
These assumptions have guided my own work in the field (e.g., Gärling et al, 1989, 1997a,
1997b; Golledge et al, 1994). However, a full-fledged alternative theory does not exist. My
suggestions are meant to indicate directions I believe future developments need to take.
CRITICISM OF THE THEORETICAL BASIS OF TRAVEL-CHOICE MODELING
Current travel-choice modelling is based on microeconomic theory (Ben-Akiva and Lerman,
1985). However, for more than 30 years behavioral scientists (e.g., Camerer, 1989;
Edwards, 1954; Simon, 1982; Kahneman and Tversky, 1979; see Abelson and Levy,
1985, for review) have been arguing, and occasionally economists themselves have
been arguing (March, 1978; Thaler 1992), that this theory is not an accurate
description of how people make decisions1. Contrary to a basic assumption of the
theory, people's preferences have been shown to be inconsistent. For instance, there is
evidence that preferences are intransitive (e.g., Tversky, 1969), that they change over
time (e.g., Loewenstein and Prelec, 1993; Stevenson, 1986), and that they are
influenced by the elicitation procedure (e.g., Fischhoff 1991).
I am aware of two counterarguments. One is that people's preferences are only inconsistent if
they are stated, not if they are revealed in "real behavior." It is true that personal involvement,
requirement to justify a choice to others, and real consequences of decisions occasionally have
been shown empirically to affect choices (Payne et al, 1992). However, it is not always the
case that such effects can be interpreted as support for microeconomic theory (Grether and
Plott, 1979; Slovic and Lichtenstein, 1983). In fact, inconsistencies may sometimes increase
rather than decrease.
The other counterargument, formalized in random utilility theory (Ben-Akiva and Lerman,
1985), is that inconsistencies ("taste variation") cancel at an aggregate level. Unfortunately,
techniques for estimating parameters in models (such as the logit) do not seem to provide
sensitive tests of systematic deviations. In fact, as shown by Camerer (1987, 1992), in markets
many known systematic biases people are susceptible to, demonstrated for years in
psychological research (e.g., Kahneman et al, 1982), do not cancel. On the contrary, they may
contribute significantly to market dysfunction.
Microeconomic theory is also incomplete. Neither does it specify what utility is (apart from a
hypothetical variable lacking any other theoretical meaning), nor how it is maximized by a
1Implied by this sentence is that one or more deliberate decisions are first made to choose something, then the
choice is (observed to be) executed. Subsequently the term decision is used interchangeably with choice. It
should be clear from the context when it is assumed that choice (or behavior) is not preceded by decisions.

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decision maker. The first incompleteness leads to the following circularity (McNulty 1990): A
person choses an alternative A over B because he or she prefers it; A is preferred over B
because the person choses it. The vagueness of the concept of utility was recently noted by
Kahneman and Snell (1990) who argue that a distinction should be made between experienced
utility (satisfaction from consuming a good), predicted utility (anticipating satisfaction from
consuming a good), and decision utility (the weight asssigned to the outcome consuming the
good when making a decision). In a similar vein, Gärling et al (1996) suggested a classification
of utility with reference to a temporal and an experience dimension. In other research I have
carried our in collaboration with different colleagues (Lindberg et al, 1989, 1992), we have, in
some cases successfully, attempted to find a relation between utility maximized in choices (of,
e.g., residential location) and psychologically meaningful motivational concepts such as, for
instance, "happiness," "an interesting life," "inner harmony," and "moral obligation." Another
related target of criticism is the underlying assumption that utility refers to the selfishness
motive. As discussed in Biel and Gärling (1995) and, related to travel behavior, in Gärling and
Sandberg (1997), research in social psychology has documented that social motives may
sometimes be equally important as selfishness for people's choices.
There is also a second form of incompleteness. At best microeconomic theory specifies the
variables affecting choices, tacitly assuming that information about these variables are available
to the decision maker. Such assumptions are however frequently not justified. They may be
relaxed if it instead can be assumed that people are simply not completely informed, not that
they are systematically misinformed. However, underlying the notion of bounded rationality
(Simon, 1982), errors people make in acquiring and processing information before making
choices are often systematic. Such findings suggest that alternative theories are both needed
and feasible.
Some years ago my colleagues and I published a conceptual paper (Gärling et al, 1984) where
we argued that travel is guided by plans. As noted by Walmsley (1988), this is neither a new
insight nor is it hardly more than a trivial statement. However, our main point was not so much
that travel is guided by plans but that information acquisition is. Our goal was to understand
why people acquire cognitive maps of their environments with the properties they appear to
have. Somewhat naively, we thought at that time that travel behavior researchers would be
interested in our ideas. However, with few exceptions (see Axhausen and Gärling, 1992)
conceptualizations of travel choices as plans do not exist. There are also few, if any, accurate
conceptualizations of information acquisition, representation, and use. Once I even heard the
argument that cognitively represented information plays no role because "objective"
information about destinations is a better predictor of choices of destinations than people´s
responses to questions aimed at measuring knowledge of destinations. This finding is of course
not surprising, if reliable measures are lacking of the cognitive information.
On what other information than a cognitive map could a choice (of, for instance, a destination)
be based? As we discussed (Gärling et al, 1984) there are external sources of information (e.g.,
actual maps) which people may access. Choices are sometimes also made when people are in
the place, in which case they can rely on perceptual information. A third possibility is that
people are able to make educated guesses on the basis of other knowledge which they have.
Yet, it cannot be denied that people acquire information which they later retrieve and use.

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Research showing that people acquire cognitive maps witnesses to this (Gärling and Golledge,
1989). Why should people then not use the information they acquire? If it is used, inaccuracies
in this information will however affect choices. Such systematic inaccuracies are likely to apply
to most people. An example is the systematic errors people seem to make in judging spatial
relations (e.g., B. Tversky, 1981). Another example is the many systematic errors people make
in forecasting events (e.g., Kahneman et al, 1982).
Still another issue concerns the implementation of a choice. Since microeconomic theory does
not specify the process preceding an observed choice (behavior), this important problem has
been overlooked. A few economists (Hoch and Loewenstein, 1991; Thaler and Shefrin, 1981)
have been more insightful. They have discussed the use of self-control techniques in
implementation of choices. Repeated successful implementation often entails developing habits.
Theorizing about how habits are acquired has a long tradition in behavioral research (e.g.,
Gärling and Garvill, 1993; Ronis et al, 1989).
In summary, microeconomic theory is both an invalid and incomplete description of how
people make choices. Therefore, it is not an appropriate theoretical basis of travel-choice
modeling. In particular the theory fails to account for (1) that chocies are often part of plans;
(2) that people show systematic biases in acquiring, representing, and using information on
which choices are based; (3) that choices or preferences are inconsistent; (4) that the concept
of utility refers to many different entities, not all of which are related to a selfish motive; and
(5) that choices are implemented through a process which sometimes entails developing habits.
SOME SUGGESTIONS OF ALTERNATIVE BEHAVIORAL ASSUMPTIONS
Interdependent Decisions
It has been recognized that choices are frequently interdependent. In such cases the
interdependent choices are simply modeled by expanding the choice set so as to include all
possible combinations of options. However, interdependencies go further than this. Like many
other activities, making a trip requires the formation of a plan (Gärling et al, 1984; Miller et al,
1960). Formation of a plan entails problem solving as it has been studied in psychological
laboratories: Alternatives are generated after heuristic search in a solution space, evaluated
according to designated criteria, selected, and implemented. Starting with the work of Newell
and Simon (1972), production systems have proved to be a useful means of modeling problem
solving. A production system is a set of instructions specifying the conditions under which
actions should be undertaken. A review of production-system models relevant to travel-choice
modelling is found in Gärling et al (1994). Current research on human problem solving is
reviewed in many sources (e.g., Lesgold, 1988).
An example of what may be called a problem-solving theory of decision making is given in
Hayes-Roth and Hayes-Roth (1979). In their production-system model of how people "plan a
day's errands" implemented in a computer program, planning is not a linear sequence of
decisions. Rather than proceeding hierarchically from a global, schematic plan to a more
refined plan, people are modeled as opportunistic in their planning. For instance, tentative
decisions to perform some initial activities may highlight constraints on the planning of later

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activities and cause a refocussing on planning of them. Furthermore, a person may decide that
there is not time to plan ahead, to only do some rudimentary planning, or to plan meticulously.
Such metadecisions of how much to plan are also integral parts of the planning process. Other
metadecisions concern by what criteria to evaluate the plan, what types of decisions to make,
and by what heuristics to make the decisions. In planning people change forth and back
between the different levels of abstraction, rather than always proceeding orderly from the
more to the less abstract.
In addition to outlining the properties of planning, Hayes-Roth and Hayes-Roth (1979)
attempted to specify underlying mechanisms of problem solving. This was done on the basis of
data collected from subjects by means of think-aloud protocols (Ericson and Simon, 1984) as
well as other conventional techniques in psychological experimentation such as chronometry
and error analyses (Ericson and Oliver, 1988). The assumption is made that planning comprises
the independent actions of many "cognitive specialists" who record their decisions in a
common data structure. On the basis of this available information, each specialist makes
tentative decisions to be incorporated into the plan. These decisions concern the plan itself,
what data are useful in developing the plan, desirable attributes of plan decisions, and how to
approach the planning problem. Some of the specialists suggest high-level, abstract additions
to the plan, others suggest detailed sequences of specific operations. An executive makes
decisions about how to allocate cognitive resources, what types of decisions to make at certain
points in time, and how to resolve conflicts when there are competing decisions.
My example has two implications for the current practice of travel-choice modeling: (1) The
interdependence of travel choices is more conditional on the external circumstances than
usually believed; (2) How interdependent travel decisions are made depends to a larger extent
on metadecisions than usually believed.
Information Acquisition, Representation, and Use
It is apparent that acquisition, representation, and use of information play an important role for
travel choices. Unknown alternatives are not chosen. If the consequences of choosing an
alternative are unkown or misrepresented, it will also affect the choice. Thus, it is obvious that
risk and uncertainty are invariably associated with consequences, although almost never
considered in travel-choice modeling.
A first step is to specify what information is relevant. Such a specification must take as its
starting point an analysis of the information possessed by the decision maker rather than what
information ought to be relevant. In particular environments, people know how far away
destinations are, how they can get there, how places look so that they can be recognized from
different angles, and how useful different places are in relation to the current purpose (Gärling
and Golledge, 1989). However, such information is never complete. Usually it is also in some
ways systematically distorted (e.g., B. Tversky, 1981). There are several attempts at
production-system modeling of the acquisition, representation, and use of information about
the environment (e.g., Gopal et al, 1989). The availability of geographical information systems
may make further such efforts feasible (Golledge et al, 1994). Several questions remain to be
answered, such as how distortions are produced, the role of affective components in the

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process, and how decisions depend on how the information is acquired and represented. With
respect to the last question it has, for instance, been shown that a maplike spatial
representation is more easily acquired if people have access to a map (Thorndyke and Hayes-
Roth, 1982). In a series of studies (e.g., Gärling, 1989; Gärling and E. Gärling, 1988) my
collaborators and I have shown that whether a spatial representation is cognitively available
affects choices.
It is never the case that travelers choosing among alternatives are informed about probabilities
of the outcomes. However, this does not mean that they assume that the outcomes are certain.
Instead the travelers are likely to supply their own probabilities in evaluating the different
alternatives. Choices will be affected since the judgments of probability are probably used to
weight the consequences (Tversky and Kahneman, 1992). How judgments of uncertainty are
made have been extensively studied. In general they are based on heuristics which sometimes
are biased relative to base rates (Kahneman et al, 1982). For instance, if a fatal accident
becomes known through the mass media, many people will judge the probability of an accident
to be higher. Another bias is a tendency to be overoptimistic (Zakay, 1983), at least about
one's own future (Sjöberg and Biel, 1983;). In making choices people who do not know the
objective probability of consequences may distort them in a way consistent with such
overoptimism (Hogarth and Einhorn, 1990).
My examples in this subsection suggest (1) that choice sets should be modeled more accurately
with regard to how people actually acquire and represent information about environments, and
(2) that the influence of risk and uncertainty on choices cannot be overlooked.
Decision Rules
In a major behavioral theory of how people make single, isolated choices among multiattribute
alternatives, Payne et al (1993) proposed a constructive view of decision making which
captures much of the accumulated knowledge of contemporary research in the area.
According to this theory, people use different heuristic decision rules in adjusting to
demands such as time pressure, information overload, and accuracy standards. The
theory may be seen as a special case of the model proposed by Hayes-Roth and Hayes-
Roth (1979), although Payne et al argue that "top-down" processes play a more
decisive role than Hayes-Roth and Hayes-Roth conjectured. Yet, instead of using the
expected utility/value, additive utility, or weighted additive utility decision rules, the
cornerstones of utility maximization, people use many other decision rules in response
to task demands. The same single choice may even be preceded by the sequential
application of several decision rules (Tversky, 1972). For instance, the number of
alternatives may first be screened by means of an elimination-by-aspects, disjunctive, or
conjunctive decision rule, then a weighted additive decision rule used to select one of
the remaining alternatives.
On the basis of a computer simulation, Payne et al (1993) were able to define how much effort
each of several decision rules required. For instance, the lexicographic decision rule
(choosing on the basis of the most important attribute differentiating between the
alternatives) was found to require much less effort than the weighted additive decision

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rule (choosing the alternative with the highest weighted sum of utility across all
attributes). It was also found that the former rule often picked the same alternative as
the latter. When introducing time pressure, the lexicographic rule actually picked this
alternative much more frequently. Empirically it was shown that people tended to
achieve an optimal accuracy-effort tradeoff.
Another reason why people prefer decision rules other than additive utility is that these
alternative rules impose less requirements on tradeoffs which presuppose an interval-
scale representation of utility (Svenson, 1979). If an alternative can be found that
dominates all other alternatives (i.e., an alternative that is better on at least one
attribute and not worse on any), a tradeoff is unnecessary. In this vein it has been
suggested that decision makers attempt to find alternatives which dominates others
(Montgomery, 1989). The lexicographic decision rule is an example of that. Its
application requires that people are willing to place more weight on one attribute.
Another example is the conjunctive decision rule reflecting the satisficing principle
(Simon, 1982): Alternatives are processed sequentially and the first one fulfilling
specified criteria is chosen. As a consequence, tradeoffs representing conflicts are
avoided.
The inconsistencies of preferences which microeconomic theory has difficulty in accomodating
simply reflect the use of heuristic decision rules. Several empirically supported
rationales for using such decision rules have been given. It would certainly be a mistake
to conclude that observations that people use heuristic decision rules are inaccurate. It
is not the observations which are in error but the theory that fails to predict them.
Selfish Versus Social Motives
In economics selfishness is assumed to explain much individual behavior (Samuelson, 1983).
However, the picture is less simple. When there is a conflict between self-interest and what is
good for the society at large, people do not always act in self-interest. In research on social
dilemmas (Caporael et al, 1989; Liebrand et al, 1992) where the outcome of the self-interest
choice becomes the worse alternative if a majority makes this choice, it has been found that
some people restrain themselves and cooperate even though they are anonymous and do not
know about how others choose. Those people are assumed to have a pro-social value
orientation (Liebrand and McClintock, 1988). In contrast, an pro-self value orientation
predisposes people to act in self-interest. Acting in self-interest may however be constrained.
Social factors (communication, personal responsibility, group identification) are known to
constrain selfishness. The dilemma structure (payoff, information feedback) is also known to
do that.
Biel and Gärling (1995) assumed that differences depending on social value orientation remain
even though self-interest is constrained. Social dilemmas have individual and collective
consequences. For instance, positive consequences of choosing the automobile may be travel
time, flexibility, and comfort. These are consequences of a single trip experienced by each
individual. Negative consequences are noise, congestion, air pollution, energy depletion, and
traffic accidents. These collective consequences depend on the number of people who make

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the choice. They also have consequences for the individuals who in varying degrees are
exposed to noise, congestion, etc. When making the choice people may consider the individual
consequences, the collective consequences, or the individual outcomes of the collective
consequences. We assume that the collective consequences are always salient to pro-social
individuals. Those individuals furthermore ignore the uncertainty associated with such
consequences. To pro-self persons the individual consequences are salient. If their self-interst
is constrained, they still focus on the individual outcomes of the collective consequences.
Furthermore, they are more strongly affected by uncertainty.
There are reasons to question that selfish motives always underlie choices people make. Even
if they do, other motives may be prevalent if the social situation constrains self-interest. An
alternative theory must accomodate such observations.
Implementing Choices
A decision is only an intention or commitment to behave. Reflecting that preferences may be
inconsistent over time, the decision maker sometimes change his mind and choose not to carry
out the behavior. Under what circumstances does this occur? In other words, when are
people's behavior possible to predict from their stated choices? The latter issue has been an
important topic in attitude research (Dawes and Smith, 1985). If the behavior is carefully
planned, it is more likely to be carried out due to a higher degree of commitment. Alternative
behaviors may also appear less attractive than they would do otherwise (Svenson 1992). In the
case an individual exerts control, careful planning should furthermore be effective in preventing
obstacles from interfering with a chosen action.
The determinants of initiating a behavior are frequently not the same as those factors which
determine persistence (Ronis et al, 1989). A frequently repeated behavior (such as commuting
by automobile) is not necessarily preceded by deliberate decisions. Such behaviors are
performed automatically. Several theories of automatization have been proposed (e.g.,
Anderson 1982). One important consequence of automatization is that the behavior may be
inconsistent with attitudes (Chaiken and Yates, 1985). Breaking a habit which is not preferred
presupposes that there are alternatives which people become aware of, that the alternatives
look better, that the alternatives are not forgotten, and that the alternatives are eventually
experienced as better.
Implementing a choice is an important neglected phase. Time-inconsistent preferences are one
reason why choices are sometimes not implemented. Automatization explains why in other
cases nonpreferred behaviors are performed.
CONCLUSIONS
Taking my point of departure a critical assessment of microeconomic theory (ben-Akiva and
Lerman, 1985) for being an inappropriate substantial theory for travel-choice modelling, I have
suggested several psychological theories that entail behavioral assumptions which are more
plausible. Unfortunately, far from anything as simple and elegant as microeconomic theory has
been possible to suggest. I think it must be realized that, at least at this time, there is an

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unavoidable tradeoff between simplicity and elegance, on the one hand, and accuracy on the
other. In travel behavior research with its strong emphasis on applications, the latter should
clearly be preferable if the contributions of the research are judged with regard to their
relevance to real-world problems.
ACKNOWLEDGEMENTS
The ideas discussed in this paper were developed in connection with a research project which
was financially supported by grant #91-238-63 from the Swedish Transportation and
Communications Research Board.
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