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Personality and Social Psychology Review2002, Vol. 6, No. 4,
264-273
Copyright © 2002 byLawrence Erlbaum Associates, Inc.
The Dynamical Perspective in Personality and Social
Psychology
Robin R. VallacherDepartment ofPsychologyFlorida Atlantic
University
Stephen J. ReadDepartment ofPsychology
University ofSouthern California
Andrzej NowakDepartment ofPsychology
University of Warsaw
Human experience reflects the interplay ofmultiple forces
operating on various timescales to promote constantly evolving
patterns of thought, emotion, and action. Thecomplexity and
dynamism ofpersonal and social phenomena have long been
recog-nized, but capturing thesefeatures ofpsychologicalprocess
represents a serious chal-lengefor traditional research methods. In
this article, we introduce basic concepts andmethodsfrom the study
ofnonlinear dynamical systems, andwe outline the relevance ofthese
ideas and approachesfor investigating phenomena at different levels
ofpsycho-logical reality. We suggest that the dynamicalperspective
is ideally suited to capture theemergence and maintenance
ofglobalproperties in apsychological system, andfor in-vestigating
the time-dependent relation between external influences and a
system's in-ternally generatedforces. Althoughfairly new to
personality and socialpsychology, the
dynamicalperspective has been implemented with respect to a wide
variety ofphenom-ena, utilizing both empirical methods and computer
simulations. This diversity oftop-ics and methods is reflected in
the articles comprising the special issue.
The subject matter of personality and social psy-chology is
inherently dynamic. Actions are com-prised of movement, judgments
are grounded in theflow of thought, emotions rise and fall in
intensityover time, social interactions unfold with a
particularrhythm of words and gestures, and relationships
aredefined in terms of the evolution of roles and mutualsentiment.
The dynamism inherent in personal andinterpersonal experience has
not been lost on ourfield. Indeed, the nature of human dynamism
pro-vided a focal point in the earliest attempts to charac-terize
intrapersonal and interpersonal processes, asreflected in the
seminal work of such pioneers asJames (1890), Mead (1934), Cooley
(1902), Lewin(1936), and Asch (1946). The focus on dynamics
isapparent today in the coupling of the word dynamicwith the
various literatures that define the field. Thus,we speak of
personality dynamics, dynamics of atti-tude change, interpersonal
dynamics, and group dy-
We thank Eliot Smith for his constructive comments on an
earlierversion of this article.
Requests for reprints should be sent to Robin R. Vallacher,
De-partment of Psychology, Florida Atlantic University, Boca
Raton,FL 33431. E-mail: [email protected]
namics, as if these topics each represented a particu-lar
manifestation of an underlying proclivity for evo-lution and change
on the part of people.
In this basic sense, the theme of the special issue-the
dynamical perspective in personality and social psy-chology-is
hardly controversial. Recent years, how-ever, have witnessed the
ascendance of a new way toconceptualize and investigate the nature
ofdynamism atdifferent levels of psychological reality. Areas of
in-quiry as diverse as cognitive neuroscience (cf. Port &van
Gelder, 1995), developmental psychology (e.g.,Fischer & Bidell,
1997; Levine & Fitzgerald, 1992;Thelen& Smith, 1994; van
Geert, 1991), organizationalbehavior (Axelrod & Cohen, 2000;
Guastello, 1995),and political sociology (e.g., Axelrod, 1984;
Nowak &Vallacher, 2001; Weidlich, 1991) are being reframed
interms that allow rigorous and precise insight into basicdynamic
processes that heretofore could only be in-ferred, and were often
overlooked for want of appropri-ate tools. There are signs that
this new approach to dy-namics is emerging as apotentially
integrative paradigmfor personality and socialpsychology as well
(cf. Carver& Scheier, 1999; Cervone & Mischel, 2002;
Lewis,1997; Nowak& Vallacher, 1998a; Read& Miller,
1998;Smith, 1996; Vallacher & Nowak, 1994a, 1997). The
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THE DYNAMICAL PERSPECTIVE
aim ofthe special issue is to highlight this new paradigmand
illustrate its relevance to a broad spectrum of topicsin
personality and social psychology. Accordingly, wehave assembled a
group of researchers, each of whomhas charted new directions for
theory and researchwithin the dynamical perspective.
To set the stage for their contributions to the spe-cial issue,
we outline in broad form the approach todynamics and complexity
that has transformed thenatural sciences in recent years. We
suggest that thisperspective resonates especially well with
enduringissues in personality and social psychology, and thusserves
as a valuable heuristic for research and a po-tential integrative
vehicle for theory construction. Thedynamical perspective should
not be looked upon assimply a metaphor, however, but rather as a
set ofprinciples, methods, and tools that impose rigor andprecision
on topics that are often opaque whenviewed through traditional
lenses. The set of contri-butors we have assembled has been
entrusted with thechallenge to make this case.
Dynamism and Complexity in Personaland Interpersonal
Experience
As noted earlier, the centrality of dynamics to hu-man
experience was recognized in early treatments ofpersonal and
interpersonal processes. James (1890)theorized about the dynamic
nature of human thoughtand action, with special emphasis on the
continuousand ever-changing stream of thought. Cooley
(1902)emphasized people's constant press for action, evenin the
absence of incentives and other external forces.Mead (1934)
discussed people's capacity for sym-bolic representation and the
enormous range of inter-pretation to which this capacity gives
rise. Lewin(1936) suggested that stability and variability in
overtbehavior reflect a persistent struggle to resolve con-flicting
motivational forces, including those withinthe person as well as
those arising from outside influ-ences. Psychodynamic theories
(e.g., Freud, 1937), ofcourse, shared this emphasis on
conflict-induced dy-namism, with particular importance assigned to
mo-tives and fears that are opaque to consciousness. Asch(1946)
suggested that social judgment reflects the in-terplay of thoughts
and feelings, with this interplaypromoting the emergence of a
unique Gestalt that isnot reducible to the additive components of
the indi-vidual cognitive elements themselves. Krech andCrutchfield
(1948), in one of the earliest attempts tosystematize social
psychology in a textbook, framedinterpersonal thought and behavior
in Gestalt terms,with an explicit emphasis on people's constant
recon-figuration of their experience in response to conflict-ing
fields of psychological forces.
These classic statements have found support inempirical research
conducted in the interveningyears, and they resonate well with lay
intuitions re-garding mental and behavioral processes. The
sheernumber and variety of factors identified as relevant tohuman
experience guarantee that everything peoplethink and do is
constantly subject to change.Thoughts, feelings, and actions are
influenced by amyriad of social stimuli that run the gamut
fromthose that are momentary and trivial (e.g., a stranger'sglance)
to those that are persistent and significant(e.g., criticism from a
loved one). This influence iscentral to everyday social
interaction, with each per-son responding to the real or imagined
thoughts, feel-ings, and actions of the other person. Even in the
ab-sence of interpersonal contact, an individual's mentalstate and
predisposition for action can take on a vari-ety of different forms
as he or she reflects on past ex-periences or imagines those yet to
take place. Patternsof thought, feeling, and action are generated
as wellby features of the larger social context, including
theperson's relationship with various groups, his or herposition in
society as a whole, the nature of varioussocial institutions, and
the assortment of beliefs, val-ues, and expectations that
collectively define culture.
The potential for complexity and constant change isenhanced by
several orders of magnitude when oneconsiders the possible ways in
which these factors caninteract to influence an individual. The
norms and be-liefs in a particular social context, for instance,
mayrun contrary to personal beliefs, societal norms, orstandards of
achievement. Which factor or blend offactors predominates, in turn,
may depend upon yetother social influences and their interaction
with priorexperiences reaching back to childhood. Complex
in-teractions of this type hold potential for generating di-verse
patterns of thought and behavior across individu-als and for
establishing different patterns within agiven individual over
time.
Even if we somehow managed to identify all rele-vant factors and
specified how they interact to influ-ence thought and behavior, we
may still be at a loss toexplain or predict a person's beliefs,
decisions, desires,or courses of action. Indeed, often the only
explanationavailable for someone's action centers on the
person'sinternal state-his or her goals, feelings,
personalitytraits, motives, self-defined principles and values,
sud-den impulses, and so on. The human potential for inter-nal
causation not only confers upon people the capac-ity to resist
external influences, but also an inclinationto act in opposition to
them. Unlike lower organisms,humans can disregard promises of
reward, threats ofpunishment, social pressure from peers and
authorityfigures, and other external inducements to action. In
ef-fect, then, the complex edifice of interacting causalforces
permeating social life can collapse in the face ofpersonal desires,
values, and momentary whims.
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VALLACHER, READ, & NOWAK
The Relevance of NonlinearDynamical Systems
Because of its inherent dynamism and complexity,the subject
matter of personality and social psychologyrepresents a serious
challenge for the methods andtools developed within the traditional
natural scienceparadigm. Indeed, one could argue that the structure
ofhuman social experience is simply too intricate andmultifaceted
to admit to complete description, let aloneprecise prediction.
Although this assessment has ledsome to question the goal of
framing interpersonalphenomena in scientific terms (e.g., Gergen,
1994;Harre, 1987; Parker & Shotter, 1990), the nature of
thefield's subject matter actually puts social psychologyin a
strong position to lead developments in science aswe enter the 21
st century. This is because the physicalsciences have undergone a
profound transformationsince the 1980s, a transformation that makes
these ar-eas of inquiry more in tune with what personality
andsocial psychologists have been talking about all along.The basis
for this transformation was the realizationthat many phenomena in
nature do not conform to cer-tain long-standing assumptions
regarding causalityand reduction, but rather are more appropriately
con-ceptualized as nonlinear dynamical systems (cf.Davies, 1988;
Eckmann & Ruelle, 1985; Glass &Mackey, 1988; Gleick, 1987;
Haken, 1978; Schuster,1984; Thompson & Stewart, 1986; Weisbuch,
1992).
Broadly defined, a dynamical system is simply a setof elements
that undergoes change over time by virtueof interactions among the
elements. The primary taskof dynamical systems theory is to
describe the connec-tions among a system's elements and the changes
in thesystem's behavior that these connections promote.Prior to the
advent of the mathematical theory of non-linear dynamical systems,
the physical sciences as-sumed that the relations among elements
could be ap-proximated as linear. A linear relation simply
meansthat a change in one element (represented as a variable)is
directly proportional to changes in another element(variable) the
greater the change in magnitude of onevariable, the greater the
resulting change in magnitudeof the other variable. Expressed in
causal terms, linear-ity means that the magnitude of the effect is
propor-tional to the magnitude of the cause. In a linear
system,moreover, the relations among variables are additive,so that
a description of the system can be decomposedinto separate
influences, each of which can be analyzedindependently. From this
perspective, the complexityof a system's behavior is a direct
reflection of the num-ber of interacting elements and the
complexity of theirmutual influences.
In a nonlinear system, the effects of changes inone variable are
not reflected in a proportional man-ner in other variables. A
variable may increase dra-matically in magnitude, for example, with
no corre-
sponding change in magnitude of another variableuntil a
threshold is reached, beyond which evenminiscule changes in the
first variable can promotevery large changes in the second
variable. The behav-ior of a nonlinear system, moreover, often
cannot bedecomposed into separate additive influences. In-stead,
the relations among variables typically dependon the values of
other variables in the system andthus are interactive in nature.
This means that onecannot ignore the effects of other variables
when de-scribing the relation between one's variables of inter-est.
These features of nonlinear systems provide a dif-ferent
perspective on the source of complexity in asystem's behavior. Even
a system consisting of a fewelements can exhibit behavior of
enormous complex-ity when the interactions among the elements
arenonlinear rather than linear (cf. Schuster, 1984).
Dynamical systems are characterized by global sys-tem-level
properties. When the relations among systemelements are nonlinear
in nature, neither the system'smacrolevel properties nor the
patterns of change inthese properties are inherent in the system.
Rather,these properties and their patterns of change emergefrom
rules specifying how the system's elements inter-act. Emergence is
reminiscent of pattern formation inGestalt psychology (cf. K6hler,
1947) and is capturedby the well-known phrase, "the whole is more
than thesum of its parts." In less evocative terms, the
propertiesand patterns of behavior characterizing a system mayarise
in a fashion that cannot be predicted solely fromknowledge of the
individual elements in isolation. De-spite the holistic (i.e.,
non-reductionistic) connotationof this feature of nonlinear
systems, emergence is actu-ally a well-specified process and can be
understood interms of a tendency toward self-organization among
asystem's elements. The basic idea is that the interactionamong
system elements, where each element adjusts toother elements, can
promote the emergence of highlycoherent structures that provide
coordination for thesystem elements (cf. Haken, 1978; Kelso, 1995).
Asystem's macrolevel properties derive from the internalworkings of
the system, in other words, without theneed for a higher order
control mechanism. This capac-ity for emergence is a defining
feature of nonlinear sys-tems in many areas of science, having been
demon-strated in fields as diverse as hydrodynamics (Ruelle
&Takens, 1971), meteorology (Lorenz, 1963), laserphysics
(Haken, 1982), and biology (e.g., Amit, 1989;Glass & Mackey,
1988).
The emergence of system-level properties by meansof
self-organization is apparent in a variety of other-wise distinct
personal and interpersonal phenomena.Classic accounts of group and
societal dynamics, forexample, noted how group norms often
developthrough the spontaneous coordination of members' im-pulses
and actions, without the need for a higher-levelauthority to impose
rules and standards (cf. Durkheim,
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THE DYNAMICAL PERSPECTIVE
1938; Turner & Killian, 1957). In recent years, this
ob-servation has been verified in computer simulationsand empirical
research on social influence and interde-pendence (e.g., Axelrod,
1984; Messick & Liebrand,1995; Nowak, Szamrej, & Latane,
1990; Nowak &Vallacher, 2001). Thus, simple social interactions
overtime tend to promote the emergence of public opinion,altruistic
values, and other group level properties. Atan intrapersonal level,
meanwhile, the spontaneousself-organization of cognitive and
affective elementsinto higher order structures has been revealed in
exper-imental work on social judgment (e.g., Vallacher,Nowak, &
Kaufman, 1994) and action identification(Vallacher, Nowak, Markus,
& Strauss, 1998), and incomputer simulations of self-reflection
processes(Nowak, Vallacher, Tesser, & Borkowski, 2000).
Thiswork suggests that organized patterns of social think-ing can
emerge without the need for a higher-level cog-nitive mechanism or
homunculus.
The specific trajectory of self-organization in a sys-tem
reflects the system's attempt to satisfy multipleconstraints
embedded in the initial state of the system.These constraints
include the initial states of the ele-ments, the nature of the
interactions among elements,and the external influences on the
system. The sys-tem's evolution represents its attempt to reach a
statethat does the best possible job of satisfying such
con-straints. The evolution of a group norm, for example,depends on
the initial dispositions and attitudes of eachgroup member, the
nature of the relationships amonggroup members, and the exposure of
group members toideas and information from sources outside of
thegroup. The norm that ultimately emerges representsthe group's
attempt to find a balance among these po-tentially conflicting
constraints. Constraint satisfac-tion also underlies the
self-organization of specificthoughts and feelings into higher
order cognitive struc-tures within the individual. Thus, an
individual's atti-tudes and values presumably arise from the
attempt toreconcile his or her preexisting judgments, diversepieces
of old and new information, and conflicting so-cial pressures and
expectations.
Dynamical systems are rarely self-contained, butrather are open
to influence from external factors byvirtue of being embedded in a
larger context. Thus, aperson's attitude may change in response to
persuasivecommunication, a group may reverse a decision basedon new
information, and a society can undergo trans-formations due to
changing international conditions.The result of such influences,
however, is dependent onthe internal state of the system in
question. This is be-cause external factors do not cause changes
directly inan otherwise passive system, but rather exert their
in-fluence by modifying the course of whatever internallygenerated
dynamics are operative for the person,group, or society. Lacking
insight into the ongoing pro-cesses within a person or social
group, it is difficult to
know what effect a given external influence is likely tohave.
When external influences are present, the sys-tem's macrolevel
properties may change in a mannerthat is non-proportional to the
magnitude of the influ-ences. Sometimes an external factor produces
only re-sistance, with little or no change in the ongoing
pro-cesses of the person or group. At other times, theperson or
group may show an exaggerated response toa lesser value of the same
external factor. At yet othertimes, an external influence may
initiate a process thatunfolds according to its own pattern of
changes, the ef-fects of which may not be apparent for days,
minutes,or years, depending on the phenomenon in question.
Research strategies that reduce dynamics to a singlepass and
focus on a stable outcome are clearly inade-quate to capture these
propensities. Rather than focus-ing on a snapshot of a specific
phenomenon, insight isoften better served by exploring how the
system inquestion evolves in time. A system may eventually
sta-bilize at a given value, but knowing this value may beless
informative than knowing the sequence of statesthrough which the
system evolved in route to this state.Two people may ultimately be
swayed by a persuasivemessage, for example, but they may have
experiencedqualitatively different routes to their shared
stance,with one person incrementally adjusting his or her ini-tial
position and the other person demonstrating siz-able swings in
opinion before reaching a new attitudeequilibrium. The evolution of
each person's attitudemay provide insight into the nature of his or
her under-lying cognitive structure and personality traits
thatwould not be forthcoming from knowing only the ulti-mate effect
of the persuasive appeal.
Beyond that, some systems may fail to reach a sin-gle stable
state that satisfies all constraints, displayinginstead a sustained
pattern of changes among differ-ent states. For phenomena
characterized by such dy-namic as opposed to static equilibrium
tendencies,the attempt to identify a single state as most
represen-tative may provide an impoverished and a
potentiallymisleading depiction. There may be greater informa-tion
value, for example, in knowing the temporal pat-tern of someone's
mood variability than in knowingthe central tendency of his or her
mood state a statehe or she might never experience. At an
interpersonallevel, identifying the temporal pattern of a
couple'smutual affect may provide greater insight into the na-ture
of the relationship than simply collapsing overtime to compute the
mean level of affect in the rela-tionship. A husband and wife may
oscillate betweenperiods of deep passion and bitter resentment, for
ex-ample, and never experience the central tendency ofthese
opposing sentiments.
The approach of nonlinear dynamical systems isideally suited to
investigate internally generated dy-namics, self-organization, the
emergence of globalproperties from the interaction of basic
elements, and
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VALLACHER, READ, & NOWAK
the time-dependent relation between external influ-ences and a
system's intrinsic dynamics. And becausethis approach is defined in
formal terms, it holds poten-tial for identifying invariant
properties of dynamicsthat transcend topical boundaries and levels
of analy-sis. Research on nonlinear dynamical systems has
es-tablished, in fact, that the dynamics of highly diversesystems
in areas as distinct as physics, chemistry, biol-ogy, and economics
conform to a handful of basic pat-terns or attractors. Rather than
simply evolving towarda stable equilibrium (i.e., fixed-point
attractor), sys-tems can display self-sustaining temporal
patterns(e.g., periodic, quasi-periodic, or chaotic attractors) asa
result of repeated iterations of the mutual influencesamong
variables internal to the system.
To identify and investigate patterns of intrinsic dy-namics,
mathematicians and scientists in various fieldshave developed a
variety of methods and tools, many ofwhich are readily adaptable to
basic concerns in per-sonality and social psychology. This suggests
the po-tential for developing general laws of psychologicaldynamics
that apply to all levels of social reality, fromthe flow of
individual thoughts to societal transforma-tions. Beyond providing
coherence to an admittedlyfragmented discipline (cf. Kenrick, 2001;
Vallacher &Nowak, 1994b), the discovery of such laws in
socialpsychology may foster new levels of integration withother
areas of psychology that have already embracedthe dynamical
perspective (e.g., developmental andcognitive psychology) and with
other areas of scienceas well.
Dynamical Research: FromMeta-Theory to Implementation
No one would argue with the suggestion that humansocial
experience is complex and dynamic, nor wouldmost observers deny the
potential relevance and utilityof nonlinear dynamical systems to
personal and inter-personal phenomena. Indeed, the past decade has
wit-nessed the emergence of considerable interest and curi-osity
regarding the promise of the dynamical approach(cf. Barton, 1994;
Carver & Scheier, 1999; Eiser, 1994;Goldstein, 1996; Guastello,
1995; Holland, 1995;Kenrick, 2001; Nowak & Vallacher, 1998a;
Vallacher& Nowak, 1994a, 1997). What is less clear to many
re-searchers, though, is whether it is necessary-or possi-ble, for
that matter-to implement this approach intheir own research
agendas. Many of the methods andtools developed to investigate the
dynamic propertiesof complex systems are foreign to the experience
ofpersonality and social psychologists, and it isn'tself-evident
that going to the trouble to adapt suchtools will have a ready
pay-off in advancing theoreticalunderstanding or real-world
application.
This concern has diminished somewhat in recentyears with the
advent of several research programs thathave established a track
record in implementing dy-namical concepts and methods. Some of
these pro-grams have used experimental methods to track thetemporal
trajectories of diverse processes, includingsocial interaction
(e.g., Beek & Hopkins, 1992; Buder,1991; Newtson, 1994),
personality expression (e.g.,Brown & Moskowitz, 1998; Mischel
& Shoda, 1995),mood (e.g., Schuldberg & Gottlieb, 2002),
group dy-namics (e.g., Arrow, 1997; Arrow, McGrath, &Berdahl,
2000; Losada & Markovitch, 1990), close re-lationships (e.g.,
Gottman, Murray, Swanson, &Tyson, in press), attitude change
(Kaplowitz & Fink,1992; Latane & Nowak, 1994), conformity
(Tesser &Achee, 1994), social judgment (e.g., Vallacher et
al.,1994), and self-evaluation (e.g., Vallacher & Nowak,2000).
In a few instances, the experimental methodshave been supplemented
by analytical tools designedto identify the formal properties of
the observed dy-namics. The periodic flow of social interaction
hasbeen shown to have a fractal (i.e., self-similar) struc-ture
(Newtson, 1994), for example, and the intrinsicdynamics of social
judgment have been shown to re-flect the operation of a
low-dimensional cognitive-af-fective system (Vallacher et al.,
1994).
For the most part, however, computer simulationsprovide the tool
of choice in investigating personal andinterpersonal dynamics. To
date, the most frequentlyemployed simulation platforms in this work
are cellu-lar automata and neural networks (cf. Liebrand,Nowak,
& Hegselman, 1998; Nowak & Vallacher,1998b; Read &
Miller, 1998). These approaches haveproven especially useful in
modeling the emergence ofglobal properties from the interactions of
individual el-ements. Two levels of social reality are most often
in-vestigated in this manner. At the level of
intrapersonalprocesses, elements typically correspond to
compo-nents of the cognitive system (e.g., specific thoughtsand
pieces of information), and the global level refersto such
macroscopic properties of cognition as deci-sions, judgments, and
self-concepts. Different mani-festations of the tendency toward
coherence in socialjudgment (e.g., dissonance reduction, causal
reason-ing, impression formation, stereotype formation andchange),
for example, have been analyzed as constraintsatisfaction processes
within a connectionist or neuralnetwork framework (e.g., Kunda
& Thagard, 1996;Read & Montoya, 1999; Read, Vanman, &
Miller,1997; Shultz & Lepper, 1996; Smith, 1996). The
emer-gence of global properties of self-concept (e.g.,
self-es-teem, differentiation) from the self-organization
ofspecific elements of self-relevant information, mean-while, has
been modeled within a cellular automataframework (Nowak et al.,
2000). At a higher level ofsocial reality, elements correspond to
individuals andthe system-level properties refer to various
group-level
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phenomena. This line of research has proven success-ful in
modeling the emergence of public opinion(Nowak et al., 1990), the
development of social net-works (Nowak, Vallacher, & Burnstein,
1998), theemergence of cooperation in social dilemma
situations(e.g., Messick & Liebrand, 1995), and the nature
ofeconomic and political transitions in society (Nowak
&Vallacher, 2001).
Computer simulations have two noteworthy advan-tages in
investigating the dynamics of complex personaland interpersonal
processes. First, they allow one to in-vestigate the relationship
between micro- andmacrolevels of social reality. An investigator
can equipindividual elements with established rules of behaviorand
observe how these rules give rise to global proper-ties for the set
of elements as a whole. Thus, for exam-ple, simple rules of social
interaction can promote theevolution of shared norms and attitudes
in a group. In areversal of this procedure, one can start with
knownglobal phenomena and trace backwards to discoverwhat rules on
the level of individual elements are neces-sary to produce the
system-level phenomena. The sec-ond advantage of computer
simulations is their capacityto reveal temporal patterns. For many
phenomena, it isunreasonable to expect the effects of a given cause
to berevealed immediately. An insult may produce hate, forexample,
but the development of such a feeling maytake a relatively long
time to develop. And although loveat first sight is a frequent
subject of novels and movies,in reality many interactions and
prolonged contact maybe necessary for a romantic attraction to
develop. Thevery nature of computer simulations is ideal for
study-ing the effects of multiple iterations of a given
process.Decades of real time, and thousands of real
interactions,may be compressed into seconds of computer time,
re-vealing delayed consequences that simply cannot be ob-served in
real time. It is not surprising, then, that com-puter simulations
have proven to be central to thedevelopment of dynamical models,
both in the naturalsciences and in the recent applications to
personal andsocial psychological phenomena.
The use of computers should not be viewed as an al-ternative to
experimental research. To the contrary,these two approaches are
complementary, providingcross-validation for one another and
working togetherin theory construction and testing. Computers,
first ofall, provide a tool for the visualization of both
experi-mental and simulation data. Through computer visual-ization,
an investigator can discover patterns that arepredicted by theory
or that exist in reality. Thus, onecan literally see the emergence
of temporal and spatialpatterns in a social psychological process,
whether thespread of public opinion through social influence(Nowak
et al., 1990) or the progressive differentiationof self-concept
through socially provided feedback onone's qualities (Nowak et al.,
2000). Beyond that, thecomparison of patterns and outcomes
identified in ex-
perimental data and the patterns and outcomes pro-duced by
computer simulation of a model provides anew means of verifying a
theory. The results of an ex-periment or a set of experiments can
be implemented ina computer program to assess feasibility and
long-termconsequences of the process in question. The results ofa
computer simulation, in turn, may suggest a particu-lar
configuration of influences that can be validated insubsequent
experimental work. Through repeated iter-ations of the reciprocal
feedback between simulationand experiment, one is in a position to
gain greater pre-cision in theoretical understanding.We wish to
emphasize that the dynamical approach
does not represent a challenge to well-established intu-itions
regarding human experience. To the contrary, theapplication of
dynamical concepts and methods islikely to enhance rather than
diminish the uniquenessof personality and social psychology. Unlike
the appli-cation of traditional natural science assumptions,
thedynamical approach provides full expression to thecomplexity and
malleability of human experience, en-abling researchers to be more
explicit than ever aboutthe issues that defined our field in its
infancy. Only re-cently have the physical sciences matured to the
pointthat they can appreciate what personality and
socialpsychologists have known all along. In their attempt
tocapture the complex and dynamic nature of basic phys-ical
processes, scientists in many disciplines have de-veloped a wide
variety of algorithms, formal tools, andnew empirical approaches.
By adapting these methodsto the special nature of human experience,
the field ofpersonality and social psychology is in a position
toimpose precision and rigor on the early insights thatdefined this
area of inquiry.
Overview of Articles in theSpecial Issue
The dynamical perspective is in its infancy and thusstill
largely unfamiliar to the majority of personalityand social
psychologists. Despite the emerging lines ofresearch described
previously, then, it may not be clearhow the various methods and
tools we have describedcan be implemented to shed new light on the
widerange of topics that define the field. By bringing to-gether
prominent researchers who are on the cuttingedge of this approach,
we hope to bridge the gap be-tween metaphor and practice, to
translate promise intoreality. Each of the following articles
outlines a uniqueapproach and does so in the context of an
importantintrapersonal or interpersonal phenomenon. To high-light
the range of phenomena addressed, we havesorted these articles into
a tripartite structure thatshould prove familiar to everyone in our
field: Cogni-tive and Affective Dynamics, Interpersonal and
GroupDynamics, and Personality Dynamics. We hope that
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VALLACHER, READ, & NOWAK
the specific configuration of basic strategies and
topicsrepresented in this set of articles will promote theemergence
of a deeper appreciation of what the dy-namical perspective has to
offer the field.
Cognitive and Affective Dynamics
Thagard and Nerb (this issue) examine how parallelconstraint
satisfaction processes operating in a dynamicneural network can
capture important aspects of emo-tional dynamics. One model, HOTCO,
shows how an in-dividual's emotions may shift as a result of
constraintsatisfaction processes operating on a set of both
cogni-tive and evaluative components. A second model,ITERA,
integrates work from appraisal theories ofemo-tion within a
constraint satisfaction model and demon-strates how appraisals can
be integrated with other typesof information to generate an
emotional response.
Simon and Holyoak (this issue) argue that earlywork on
consistency theories, although quite promis-ing, failed to provide
an account of how attitudes areformed and decisions are made.
However, they suggestthat recent connectionist models of constraint
satisfac-tion provide a general and testable account of the
rolethat consistency principles play in decision making andattitude
formation. They then discuss their recent bodyof work on complex
legal decision making that demon-strates how
constraint-satisfaction mechanisms cantransform initially ambiguous
legal evidence into co-herent decisions.
Queller (this issue) presents a recurrent distributednetwork
model that simulates two seemingly distinctmodels of stereotype
change-book keeping andsubtyping-that have been presented as
reflecting dif-ferent cognitive processes. She shows that the
apparentdifference between book keeping and subtyping is dueto
differences in the degree of covariation among fea-tures induced by
the different stimulus sets used. Hermodel thus explains, and
thereby integrates, these twotypes of stereotype change.
Carver and Scheier (this issue) are well known fortheir control
systems theory of self-regulation, whichportrays an individual as
guiding his or her behavior bymonitoring the discrepancy between a
current state anda goal state. They note that this "top down" view
seemsto conflict with the more "bottom up" view ofdynamicalsystems
models, in which attractors and repellors for asystem develop by
means of self-organization pro-cesses. However, they argue that
these principles some-times reflect different ways oflooking at the
same issue,and sometimes represent complementary principlesthat
address differentlevels ofthemore general process.
Interpersonal and Group Dynamics
Shoda, Tiernan, and Mischel (this issue) extendtheir neural
network model of Cognitive Affective Per-
sonality Systems theory to dyadic interactions. Theyfirst
identify the major attractors of individual net-works operating in
isolation and then they show thatwhen two networks are coupled, the
dyadic networkfrequently develops attractors that are not found in
ei-ther of the individual networks. Their work shows hownew
patterns of thought and behavior can emerge fromthe interaction of
individuals with different personali-ties, and it also has
implications for how we mightthink about influence in
relationships.
Gottman, C. Swanson, and K. Swanson (this issue)provide a step
by step account ofhow marital interactioncan be modeled as a set of
difference equations that cap-ture important aspects of a couple's
dynamics. Theypresent empirical data demonstrating that that
thismodel can effectively predict long-term marital stabilityand
divorce. As they note, this technique is quite generaland can be
used to model any kind of dyadic interactionfor which time series
data are available.
Axelrod, Riolo, and Cohen (this issue) use interact-ing adaptive
agents, playing a Prisoner's Dilemmagame, to examine the impact of
different kinds of com-munication patterns on the development of
coopera-tion among individuals. They find that even when pat-terns
of communication are randomly chosen and theagents are
geographically distant from one another, co-operation will still
develop as long as the interactionsamong the agents persist over
time. They discuss theimplications of this finding for the
development andmaintenance of cooperation in an age in which somuch
interaction is electronically mediated.
Kenrick, Maner, Butner, Li, Becker and Schaller(this issue) note
that evolutionary approaches and dy-namical systems approaches are
becoming increas-ingly important in social psychology and they
discusshow the insights from these two approaches can beintegrated.
They focus on six basic adaptive problemsof life in social groups
and, through the use of bothconceptual analysis and computer
simulations, theyexplore how a dynamical systems approach can
pro-vide insight into the dynamics that evolve in each ofthose
domains.
Personality Dynamics
Read and Miller (this issue), like Shoda et al. (this is-sue),
present a neural network model of personality.Whereas Shoda et al.
focus on the abstract characteris-tics of their model, such as its
dynamics and the numberand types of attractors, Read and Miller
present a modelthat captures important aspects of what is
currentlyknown about personality structure. Relying heavily onwork
in temperament, personality structure, and theneuroscience
ofmotivation and emotion, they constructa neural network model that
attempts to simulate someofthe major distinctions in personality,
such as extrover-sion, neuroticism, and conscientiousness.
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THE DYNAMICAL PERSPECTIVE
Vallacher, Nowak, Froehlich, and Rockloff (this is-sue)
investigate the implications of conceptualizing theself-concept as
a self-organizing dynamical system.They suggest that
self-evaluative thought evolves to-ward regions of maximal
evaluative coherence inself-structure, and that the valence of
these fixed-pointattractors dictates a person's level of
self-esteem. Theypresent preliminary research on the flow
ofself-evaluative thought showing that whereas self-es-teem is
related to overall self-evaluation, self-conceptcoherence
determines the dynamic properties of suchthinking (e.g., movement
between differentself-evaluative states).
Johnson and Nowak (this issue) examine dynamicalpatterns in the
emotions and symptomatology of indi-viduals with bipolar
depression. To do so, they use anewly developed technique that
identifies the numberand nature of attractors in time series data.
In applyingthis method to the monthly self-reports of their
partici-pants, they identify several distinct types of
attractors,each associated with a set of outcome criteria. Theyshow
that the lack of stable attractors is especially dys-functional in
that it predicts frequency of hospitaliza-tion and suicidality.
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