Instant Download Ebook PDF Bounded Rational Choice Behaviour Applications in Transport PDF Scribd
Instant Download Ebook PDF Bounded Rational Choice Behaviour Applications in Transport PDF Scribd
Instant Download Ebook PDF Bounded Rational Choice Behaviour Applications in Transport PDF Scribd
The development of scientific disciplines has all the properties of man-made artifi-
cial systems. Although one would expect that scientific evidence is the main driver
of the survival and perseverance of theories and models, academic networks are
institutionalised in terms of journals, conferences and other means of dissemination.
Quality tends to be peer-reviewed, but the process is subjective or inter-subjective at
best. Like in any social system, highly respected scholars serve as sources of inspira-
tion, but at the same time tend to be the gatekeepers of the historical development
of the discipline and acceptance standards. For very good reasons, new approaches
are typically critically assessed under much or too much scrutiny, implying they
may not receive the attention they deserve. There are signs of self-selection as
chances of acceptance may decrease if one deviates too much from the state of the
art. Incremental contributions tend to be applauded; divergent views need more
convincing.
Although the transport community is known for its balance between accumula-
tive research within long-standing modelling approaches, supported and sustained
by continuous training and dissemination practices, and constructive openness to
new ideas, some fundamental foundations of transport research were largely left
unchallenged or were never put on the agenda for decades. The notion of equili-
brium and the principle of rational choice behaviour have been the cornerstones of
the disciplines for the last 40 years. Without any doubt, these concepts have played
a pivotal role in the development of the models that have become commonly used
in transportation planning practice. In turn, accepted practice cannot be disen-
tangled from these basic principles.
At the same time, however, the principle of fully rational behaviour lacks beha-
vioural realism. Nevertheless, compared to other disciplines, attempts to explore the
possibilities of formulating alternative models of activity-travel behaviour, derived
from principles of bounded rationality, have been limited in number in the travel
behaviour community. In part, this may be because transportation is primarily an
applied engineering science, and as such less concerned with more fundamental
explanations of observed behaviour. However, the very nature of the decision-
making processes underlying activity-travel behaviour, characterised by a relatively
stable of antecedent conditions and instrumental in kind, may not need a more
subtle and varied set of behavioural principles and mechanisms.
In any case, although models of bounded rationality have been around in travel
behaviour research since its inception, they never have played a central role in this
x Preface
destinations are equal to the observed total number of trips departing from the ori-
gins and arriving at the destinations. Many different specifications of the attractive-
ness and deterrence function exist, but a detailed discussion of the development of
spatial interaction models is beyond the scope. Useful introductions and reviews of
spatial interaction models can be found in Hayes and Fortheringham (1984) and
O’Kelly (2009).
One of the criticisms of four-step models, and consequently against spatial inter-
action models, concerned their lack of behavioural foundations. The models were
copied from physics and represent in statistical terms macroscopic aggregate rela-
tionships between spatial units (zones, districts). Although the mathematical expres-
sions have been given various economic interpretations (e.g. Anderson, 2011), zones
do not make any decisions, and the total number of trips is not the outcome of an
individual travel decision. Thus, spatial interaction models describe regularities in
aggregated decision outcomes of individuals, not the decisions of individuals
themselves.
Based on the argument that models capturing individual and household deci-
sions processes and choice behaviour are superior forecasting tools compared to
models that describe statistical regularities in aggregate distributions, develop-
ments in categorical data analysis led to the formulation of models of individual
choice behaviour. The multinomial logit model soon became the benchmark in
modelling transport mode, destination and route choice decisions. Many more
advanced discrete choice models followed to relax the limiting assumptions under-
lying the MNL model, allowing for substitution effects. Although it should be
noted that the mathematical expression of the MNL can logically be derived from
several different, even fundamentally conflicting, theoretical constructs, the MNL
model and many of its variants have been predominantly linked to random utility
theory.
Random utility theory assumes that individuals derive a utility from the chosen
alternative. This utility consists of a deterministic part and an error term.
Consequently, individuals are assumed to have stochastic preferences. In addition,
they are assumed to maximise their utility when choosing a single alternative from
the available options. Assumptions about the error terms of the utility functions
then, ceteris paribus, dictate the probability of choosing a particular alternative.
Random utility theory can be viewed as an example of rational decision-making.
The term ‘rational’ has received multiple definitions and interpretations, but in the
context of travel demand forecasting, it is commonly been used to indicate that the
concept of utility maximisation refers to the best or optimal choice. Rational means
that the decision-maker will systematically evaluate all available choice alternatives
and select the best, based on reason (i.e. a cognitive process), from the possible
choices. Models based on the principle of rationality assume that an individual will
define the set of attributes that is important to the decision-making problem. Next,
an individual will cognitively assess the outcomes of his possible decision for each
alternative in the choice set and choose the best option. The cognitive decision-
making process involves processing the various attributes and arrive at some overall
judgement by integrating the evaluation of the various attributes according to some
Introduction xiii
have been developed. While much of this work has been focused on information
processing in stated preference and choice experiments, there is no reason to assume
that similar reduction of task complexity will not be operant in real-world choice
and decision-making. Collins and Hensher provide a detailed review of the historical
evolution of various attribute non-attendance models that have been suggested, pri-
marily in the transportation and in the environmental economics literature. They
present and illustrate a random parameters attribute non-attendance model to
simultaneously infer attribute non-attendance and handle preference heterogeneity.
Using stated choice data on route choice of commuters under travel time uncer-
tainty and one or more time and cost attributes, their results indicate that attribute
non-attendance becomes more prevalent with an increasing number of attribute
levels, a decreasing number of choice alternatives and an increasing number of
attributes.
Zhu and Timmermans also address the problem that individuals may not consider
all potentially relevant attributes when making a decision. Rather than assuming a
single threshold, they define a series of successive activation levels. In addition to the
use of activation thresholds, defined at the attribute levels, an overall threshold is
estimated, which differentiates the choice alternatives into accepted and rejected
alternatives. Different overall thresholds then represent different non-compensatory
decision rules, such as disjunctive, conjunctive and lexicographic rules. For this rea-
son, they call their model a heterogeneous decision rule framework. The probability
that a particular rule will be used is a probabilistic function of mental effort, risk per-
ception and expected outcome. This approach is unique for travel behaviour research
where choices are usually modelled in terms of some performance measure of deci-
sion outcomes and not in terms of cognitive processes. Differences in mental effort
occur because the different non-compensatory decision rules involve a different
degree of processing the attributes. Risk perception depends on the setting of the
threshold. Little mental effort may imply some opportunity costs related to the
expected regret that results from making an inferior decision. Shannon’s entropy
measure is used to represent risk perception. Finally, expected outcome measures the
extent the use of a decision rule leads to preferred outcomes. Results of applications
of the model to aspect of pedestrian movement show that it represents observed data
slightly better than utility-maximising multinomial logit models.
As indicated, attribute non-attendance models have been predominantly devel-
oped in the context of stated choice experiments. Although it is likely that indivi-
duals also apply simplifying decision heuristics in real-world settings, some
differences between real-world decision-making and decisions in quasi-laboratory
settings prevail. In stated choice experiments, subjects have to understand the
experimental task, relate it to their personal decision context, process the attributes
and their levels, and the choice alternatives and choice sets, and then try to apply
their internalised preference structures to the reconstructed experimental task.
Selectivity and representation bias may occur in this process. By contrast, when
faced with a decision to be made, in real-world settings individuals need to apply
their preference functions to attribute levels of the choice alternatives that are
retrieved from their memory, which holds a cognitive representation of the
xvi Introduction
more scholars have examined the problem of dynamic route assignment from the
perspective of bounded rationality. The notion of bounded rationality in this
domain of study has also been subject of varying and often too vague definitions,
missing mathematical rigour. Szeto, Wang and Han deliver a good introduction to
the dynamic traffic assignment, the alternative meanings of the notion of bounded
rationality in this field of study and the latest developments. Bounded rationality in
route choice implies that the travel times of all selected routes between the same
origin-destination are all the same within some defined acceptable tolerance thresh-
old from the minimum travel time. They present (heuristic) solution methods for
this objective and discuss existence and uniqueness of solutions. Finally, an exten-
sion to the joint departure time route choice problem is discussed.
The challenge of these approaches is the find a close form mathematical specifica-
tion that is consistent with the attempted behavioural principles and which at the
same time can be estimated. Consequently, there are limits to these kinds of model
in general, and particularly in modelling complex dynamic processes and systems.
To enrich the models, some agent-based model systems of decision-making pro-
cesses that are based on principles of bounded rationality have been suggested. Two
of these are included in this volume. First, Psarra, Arentze and Timmermans outline
an agent-based model and illustrate its properties using numerical simulations that
simulate dynamic choice behaviour in response to endogenous and exogenous
change. Agents learn about their environment when making choices. Consequently,
agents become aware of the choice alternatives in their environment, develop choice
sets and build up context-dependent cognitive representations about the attributes
of the alternatives in their choice set. It leads to dynamically updated beliefs about
the state of the world. Over time, if a choice alternative has not been visited forget-
ting is also simulated, implying that choice alternatives have different activation
levels. In addition to this cognitive mechanism, agents build up affective beliefs,
which are defined as a function of the discrepancy between expected and experi-
enced utility and act on those. At the same time, agents have context-dependent
aspirations, which may also change over time if after trying different behaviour they
cannot be met. Endogenous change is triggered as a function of stress, which builds
up if experienced utility is lower than the corresponding aspiration level. The agent-
based system thus is capable of simulating very different dynamic behavioural tra-
jectories of activity-travel behaviour, depending on the parameters setting. It will
simulate the emergence of habitual behaviour from a state of complete unawareness
of the environment if the agent’s environment allows a balance between aspiration
levels and the utility that can be derived from the environment. It may also simula-
tion lowering of aspirations levels or a change of residential and/or job locations if
the current long-term decisions do not allow achieving aspirations levels associated
with their activity-travel behaviour. The model system incorporates several mechan-
isms that assume agents do not maximise their utility and have perfect knowledge,
but rather act in a bounded rational way. The numerical simulations reported in
their chapter focus on the impact of memory-activation parameters, habit strength
and the strength of emotional response. Results illustrate the effect of trade-offs
between past and recent emotional experiences, and between cognitive and affective
xviii Introduction
account for the typical violations of expected utility maximisation that have been
documented in the literature. Seminal work on prospect theory in travel behaviour
can be traced back to Avineri and his co-authors in their attempts to operationalise
the key concepts of prospect theory in a travel behaviour context and judge the rele-
vance of prospect theory for route choice departure choice and other choice pro-
blems in travel behaviour research. In this book, Avineri and Ben-Elia provide an
excellent overview of the theoretical foundations of (cumulative) prospect theory,
discuss the model specifications that have been applied and give a detailed account
of the design and results of accumulated research in travel behaviour research that
is based on these theoretical foundations that deviate from rational behaviour under
conditions of risk and uncertainty. The potential of prospect theory for particular
decision-making in travel behaviour research is clearly articulated, but limitations
are also identified, leading to further research needs.
This collection of chapters represents the frontier in travel behaviour research in
endeavours to increase the behavioural realism of our model apparatus that is used
to predict transport demand. The different approaches and models witness, all in
their own right, how principles of bounded rationality can be incorporated into the-
ories and models of choice and decision-making, both under conditions of certainty
and uncertainty, as they are related to the different facets of activity-travel beha-
viour. These contributions, however, also evidence that increased realism tends to
come with increased complexity. The number of parameters tends to increase.
Moreover, while conventional models come with performance indicators such as
willingness to pay and consumer surplus and straightforward equations for calculat-
ing (cross-)elasticities, for some of the models discussed in this volume, equivalent
equations will be difficult or impossible to generate. Moreover, as discussed, some
of these models of bounded rationality violate properties of classic models such as
regularity, which the travel behaviour research community seems to have embraced,
regardless of empirical evidence on the contrary. Furthermore, the estimation of
some models of bounded rationality is far from standard, and may require dedi-
cated software development. The lack of software to estimate a model of course
should never be an excuse for not accepting or further exploring it, but it does indi-
cate that substantial investment in the development, dissemination and discussion of
alternative modelling approaches is needed.
Soora Rasouli
Harry Timmermans
Editors
References
Li, Z., & Hensher, D. (2011). Prospect theoretic contributions in understanding traveller
behaviour: A review and some comments. Transport Reviews, 31, 97115.
O’Kel, M. (2009). Spatial interaction models. International Encyclopedia of Human
Geography, 2009, 365368.
Rasouli, S., & Timmermans, H. J. P. (2014a). Activity-based models of travel demand:
Promises, progress and prospects. International Journal of Urban Sciences, 18, 3160.
Rasouli, S., & Timmermans, H. J. P. (2014b). Applications of theories and models of choice
and decision-making under conditions of uncertainty in travel behavior research. Travel
Behaviour and Society, 1(3), 7990.
Chapter 1
Abstract
Purpose This chapter reviews models of decision-making and choice under
conditions of certainty. It allows readers to position the contribution of the
other chapters in this book in the historical development of the topic area.
Theory Bounded rationality is defined in terms of a strategy to simplify the
decision-making process. Based on this definition, different models are reviewed.
These models have assumed that individuals simplify the decision-making pro-
cess by considering a subset of attributes, and/or a subset of choice alternatives
and/or by disregarding small differences between attribute differences.
Findings A body of empirical evidence has accumulated showing that under
some circumstances the principle of bounded rationality better explains
observed choices than the principle of utility maximization. Differences in
predictive performance with utility-maximizing models are however small.
Originality and value The chapter provides a detailed account of the different
models, based on the principle of bounded rationality, that have been suggested
over the years in travel behaviour analysis. The potential relevance of these mod-
els is articulated, model specifications are discussed and a selection of empirical
evidence is presented. Aspects of an agenda of future research are identified.
The study of travel behaviour concerns the description, analyses and modelling of
decision processes related to multi-faceted travel behaviour. It aims at better under-
standing and predicting travel choices and how these co-vary with the decision-
makers’ personal traits and characteristics, attributes of the choice alternatives and
complex versions of the model may better reproduce the observed choices, but this
approach does not give any guidance whether the assumed utility-maximizing deci-
sion process is the best representation of the decision-making process.
Secondly, one should realize that the same mathematical expression, depicting
the functional relationship between the dependent and the set of independent vari-
ables, can often be derived from different conflicting behavioural theories. For
example, the multinomial logit model can be deducted from Luce’s choice theorem
and random utility theory, which fundamentally differ with respect to the nature
of preferences (deterministic vs. stochastic) and the nature of the decision process
(probabilistic vs. deterministic). Regret-based choice models which define regret as
a linear function of attribute difference between the best non-chosen and the cho-
sen alternative are mathematically equivalent to the multinomial logit model; yet
the principle of regret-minimization is fundamentally different from the principle
of utility-maximizing behaviour. To make matters even more complicated, the
mathematical expression of the multinomial logit model can also be derived from
the quantum response model, which is a theory of decision-making under uncer-
tainty rather than a theory of riskless choice. This equivalence implies that any
satisfactory fit of the model to the data is just a necessary but not a sufficient con-
dition for validating the behavioural principles and mechanisms underlying the
mathematical model.
When developing behavioural models, it is important to critically consider which
theory seems most valid for the decision-making process under investigation.
Unfortunately, the travel behaviour community, unlike for example the marketing
community, does not have a rich tradition of developing, let alone systematically
comparing, alternate theories of choice and decision-making. The vast majority of
studies on various facets of travel behaviour has been based on discrete choice mod-
els, which in turn have been interpreted as representations of random utility theory.
This theory can be seen as an example of a theory of rational decision-making.
Individuals are assumed engaged in a high involvement decision process in which
they have full information about the set of attributes, characterizing the choice
alternatives in their choice set, from which they derive a utility. The behavioural
principle of utility-maximizing behaviour then leads to a set of probabilities of
choosing the alternatives in an individual’s choice set. The approach negates any
emotional considerations.
Although the literature in travel behaviour research on models of bounded
rationality is relatively small, travel behaviour researchers have occasionally
explored the formulation and application of such models. The purpose of this chap-
ter is to provide an overview of these models, allowing readers to better understand
the contribution of the specific papers, included in this book.
This chapter is organized as follows. Firstly, we will present a general framework
for positioning various models and theories of choice and decision-making. Based
on this framework, we will continue the conditions under which we would consider
the decision-making process evidencing bounded rationality. These conditions are
used in the remainder of the chapter to organize existing research in mainly trans-
portation and urban planning research on bounded rationality. More specifically,
4 Soora Rasouli and Harry Timmermans
we will first discuss models that do not necessarily lead to an optimal choice. Next,
we will discuss models that involve simplifications of the decision process by not
considering all relevant attributes. This is followed by a discussion of models, which
assume that individuals ignore small attribute or alternative differences, and there-
fore are indifferent between those choice alternatives that only differ marginally.
Finally, we will discuss modelling attempts aimed at mimicking how individuals
ignore choice options to reduce their consideration set. The chapter is completed
with a conclusion, discussion and agenda of further research.
1.1. Framework
d) Salaperäinen vieras
»Mikä teidän on», sanon, »etteköhän voi pahoin?» Hän olikin juuri
valittanut päänkivistystä.
Sen sanottuaan hän hymyili, mutta oli valkea kuin liitu. Miksi hän
hymyilee? — tämä ajatus tunki läpi sydämeni, ennenkuin vielä olin
mitään tajunnut. Minä itse kalpenin.