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Control Theoriesin Sociology

Dawn T. Robinson

Department of Sociology, University of Georgia, Athens, Georgia 30602-1611;email: [email protected]

Annu. Rev. Sociol. 2007. 33:15774

First published online as a Review in Advance onApril 17, 2007

The Annual Review of Sociology is online athttp://soc.annualreviews.org

This articles doi:10.1146/annurev.soc.32.061604.123110

Copyright c 2007 by Annual Reviews.All rights reserved

0360-0572/07/0811-0157$20.00

Key Words

mathematical sociology, cybernetics, identity, affect, production

systems

Abstract

Sociologists use negative feedback loop systems to explain ident

processes, interpersonal behavior, crowd behavior, organizatiobehavior, social relationships, and the behavior of political systemControl system models help us to understand how actors enact so

roles with enough stability to preserve institutional arrangemenwhile still demonstrating remarkable creativity in unusual circu

stances. These theories take us away from an oversocialized viewthe actor, without relegating us to exclusive reliance on ground

theory. They provide a foundation for several generative theorof adaptive, goal-seeking behavior on the part of social actors ainstitutions. This chapter begins by tracing the history of cont

theorizing in sociology, then describes several contemporary theries that rely on control imagery, reviews the empirical support these theories, describes some of their significant points of over

and departure, and examines some of the key tested and untesimplications of a control system approach in sociology.

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CONTROL SYSTEM THEORIESIN SOCIOLOGY

A staggering array of theories in sociology re-lies loosely or explicitly on some version of

the concept of control. In fact, Gibbs (1989)referred to the concept as sociologys cen-

tral notion. Most uses of control in sociolog-ical theory focus on its meanings related toinfluence, dominance, and/or restraint. This

review focuses on theories that invoke a spe-cific usage of controlthat of a negative feed-

back loop system as developed in engineering.In engineering, control systems are used asthe bases of processes like climate control. In

a standard climate control system, tempera-ture is controlled at some desired reference

state (the thermostat setting). When a room

gets too hot or too cold, the system notes thediscrepancy between the thermostat setting

and the actual temperature (the thermometerreading) and generates a response (heating or

cooling) to reduce that discrepancy.Control system ideas form the basis for

an increasing amount of theoretical devel-

opment in sociology. These system formula-tions eschew oversimplified cause-and-effect

thinking, while maintaining scientific rigor.Sociologists use control systems, or negative

feedback loop systems, to explain identityprocesses, interpersonal behavior, collectivebehavior, organizational behavior, social

relationships, and the behavior of politicaland economic systems. These theories differin key ways from other approaches in the

discipline and offer solutions for some classicsociological problems. This review character-

izes the main threads of sociological researchrelying on control systembased theories anddescribes some of the primary insights drawn

from contemporary control systembasedresearch.

HISTORY OF CYBERNETICSYSTEM THINKING INSOCIOLOGY

The history of contemporary control systemtheorizing in sociology can be traced back

to the cybernetics and general social syste

theory movements in the 1940s (Rosenblueet al. 1943, Wiener 1948). In the mid-194a core group of scholars in engineering, bio

ogy, medicine, physics, psychology, and athropology began to hold biannual conf

ences where they discussed issues of contrregulation, and feedback in both machinand living systems. The only two social s

ence participantsin this early group, MargaMead and Gregory Bateson, began to arg

for the application of these ideas not justliving organisms, but to entire social syste(Heims 1991). Much of this early schola

ship focused on the utility of feedback loofor helping to further our understandingself-regulating systems. This new intellectu

movement brought together ideas from a vriety of sciences and began to develop mod

of purposive systems using concepts of ccularity, reciprocal causation, recursiveneand dynamic process. In particular, this ea

work highlighted the use of feedback loofor modeling goal-oriented behavior in m

chines, people, and other systems.Feedback loops characterize adaptive sy

tems by incorporating the notions of goa

observations, matching procedures, and r

sponse functions. Figure 1 depicts the dnamic outcomes of negative and positive feeback over time. An adaptive system begwith a goal (or reference state), has som

way of assessing the actual or observed stahas some way of assessing deviations betwe

the reference state and the observed staand responds to those deviations in an tempt either to reduce (in the case of ne

ative feedback) or to increase (in the caof positive feedback) those deviations. Neg

tivefeedback loops generate compensatory,error-reducing, system behaviors and are thtypically used to model self-regulating sy

tems and equilibrium-seeking behavior. Poitive feedback loops generate either sig

amplification or dampening. Without an tervening process, positive feedback lootend to result in an explosion or collapse

the system, depending on the nature of t

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a

Referencepoint

Observedstate

Amplification

Blocking

Startingpoint

b

Disturbance, deflection

Figure 1

(a) Negative feedback. (b) Positive feedback.

initial feedback (see Figure 1b). These mod-

els can be used to model contagion, diffusion,snowball effects, etc.

At the time of this early cybernetics move-

ment, negative feedback loopbased systems(also called control systems) were proving

highly useful in the development of weaponstechnology during World War II (Heims1991). Members of thecybernetics movement

sought to generalize these models to a vari-ety of domains. A classic illustration of a con-

trol system in engineering is that of a climatecontrol system mentioned above. In any con-trol system, input signals (e.g., thermometer

readings) reveal information about the cur-

rent state of a system (e.g., the air temperature

in a room), which is then compared with a ref-erence level (e.g., a thermostat setting). Thedirection and size of the difference between

the current state and the reference level guidethe output behavior of the system (e.g., activ-

ity of furnace or air conditioner). This typeof system is also called a negative feedbackloop because the system responds by trying

to reduce the discrepancy generated by thefeedback. In the case of a climate control sys-

tem, discrepancies between the input (tem-perature) and the reference level (thermostatsetting) produce output in the form of activat-

inga furnace or airconditioner. An illustration

www.annualreviews.org Control Theories 159

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Outputfunction

Inputfunction

Social andphysical

environmentComparator

Referencesignal

Errorsignal

Perceptualsignal

Figure 2

Basic control system.

of a basic feedback control system is depictedin Figure 2. Control systems generally con-

tain three primary functions (input, compara-tor, output) and three primary signals (input,reference, output).

Ideas about recursive processes, adap-tive systems and equilibria were part of the

Zeitgeist of the period. Even as the cybernet-ics group was busy thinking about feedbackloops and self-regulating systems, Vilfredo

Pareto, a social scientist with an engineeringbackground, was arguing for modeling soci-

ety as a dynamic equilibrium best understoodas a system with a set of mutual reciprocal in-fluences (Pareto 1935). According to Pareto,

the key insight of system thinking was that theintroduction of change in one part of a system

will necessarily result in change elsewhere inthe system. Much of this change, Pareto ar-gued, is in the form of compensation, or the

system maintaining itself. Pareto argued for

a type of functionalism, a mathematical one,in which the state of each system variable is afunction of the others. In other words, there isno directed causality. Others noted that feed-

back systems transcended notions of equilib-rium processes and provided the foundation

for modeling purposive behavior in a way thattakes into account adaptation to changes inthe environment and even the goals them-

selves (Deutsch 1951, p. 198; Buckley 196p. 58).

In the 1950s and 1960s, sociologists liHomans, Parsons, and Buckley picked up

the general excitement of the ideas beigenerated by these early cyberneticians a

systems theorists. Parsons (1961, ParsonsShils 1951) was intrigued by system theoarguments and developed a general theory

social structures as equilibrium-maintainisystems. Parsons argued that systems seequilibrium (or pattern maintenance) by e

gaging in mechanisms of control like belenforcement and sanctions for deviance. T

notion of control (as exerted by the systeitself ) differs somewhat from the traditioncybernetic usage, but the notions of purp

sive systems are very much in line with thearlier work. Homans (1950) picked up on t

equilibrium ideas of Pareto but, like Deuts(1951), argued against presuming that scial phenomena could best be understood

homeostatic processes. He argued instead thgroups and societies reach practical equil

ria as a result of social behavior, but ththese equilibria are not the goals of social bhavior (Homans 1950, pp. 113114). Buck

(1967) offered a critique of the vaguely dev

oped system arguments posed by Parsons aHomans and argued for a rigorous, compmodel of societies as adaptive systems wicontinuous exposureto new information, w

bothtension-reducing and tension-increasiimpulses, and systems for both learning a

preserving. In other words, Buckley called fa well-specified theoryof society as an interlated system of positive and negative feedba

loops. Rather than developing such a theoBuckley instead offered (as a first step) a

ries of examples of control systems (negatfeedback loop) applications to various groprocesses.

For the most part, these early uses of sytem theory ideas in sociology were false star

not leading in a cumulative fashion to elabration and refinement. Partial exceptionsthis claim include Luhmanns (1983) co

ception of social systems as self-reproduci

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(autopoietic) agents who maintain their

identities using communication systems.Luhmann, a student of Parsons, argued thatsystem identity maintenance is a process of

complexity reduction, whereby social sys-tems create and maintain boundaries separat-

ing themselves from the complexity of theirenvironments (Luhmann 1995). Luhmannswork has only recently begun to be influ-

ential in American sociology and has notyet generated the kind of theoretical devel-

opment promised by the early enthusiasts.One example is a recent historical analysis byPadgett & MacLean (2006) of the emergence

of business systems in Renaissance Florence.These authors analyze the interaction be-

tween interrelated systems (economic net-

works and political networks) as the site ofthe emergence of a new social form (business

systems).Other recent appeals to system theory

ideas include Kuhnes (2001) use of cyber-netic ideas to describe the transformation ofpolitical and economic systems in Eastern

Europe as a negative feedback loop systemand Jeppersons (1991) use of control theoret-ical ideas to explicate neoinstitutional theory

as it applies to organizational analysis. With-

out making explicit connection to these ear-lier ideas, White (1992) uses a similar notionof control when he describes social organi-zation and social structure as emerging from

identities at various levels (e.g., persons, orga-nizations, disciplines) seeking control by an-

ticipating and responding to environmentaldisruptions.

In summary, sociology has had a long his-

tory of flirting with systems theoretical ideas,but not until recently have these ideas be-

come incorporated into what Merton (1957)referred to as our theories of the middlerange. The recent surge in theoretical de-

velopment that makes explicit use of systemtheory ideascontrol systems in particular

can be traced to the work of William T.Powers (1973), who introduced engineering-based control system concepts and methods

to social scientists in the form of a new theory

of perception. I now turn to an overview ofthis tradition.

REVIVING CYBERNETICS INTHE SOCIAL SCIENCES:

WILLIAM T. POWERSSPERCEPTION CONTROLTHEORY

In the 1970s, a landmark work provided themodern inspiration for a new generation of

sociological control theories. Powerss (1973)book, Behavior: The Control of Perception, of-fered a comprehensive application of control

system logic and mathematics to behavioralpsychology. In it, he imported engineering

models of control systems into a theory of hu-

man behavior.Powerss work was set against the intellec-

tual backdrop of the behaviorist-dominatedmodels in the research psychology of the

1940s, 1950s, and 1960s. A core premise inbehaviorist theories is that there is a fairlystraightforward relationship between envi-

ronmental inputs and behavioraloutputs (e.g.,Skinner 1969): Environment controls be-

havior. Meanwhile, humanist psychologistsoffered more appreciation for the ways in

which individuals interpret and organize in-formation from the environment, and thesescholars focused on individual agency in the

self-actualization process. The behavioristsseemedtohavescientificmethodontheirside,concentrating on observable features of the

environment and manifest behavior. Powersturned the logic of behaviorism on its head,

while offering an approach to modeling cre-ative human behavior that did notsacrificesci-entific rigor or testability. His core premise:

Behavior is the visible process by which wecontrol perceptions.

In perception control theory, the inputfunction translates information from the en-vironment into perceptual signals; the com-

parator assesses the difference between theperceptual signal and the reference signal,outputting that difference as an error signal;

and the output function translates the error


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signal into specific action. The reference sig-

nal itself is exogenous to the system.Imagine holding one end of a long rub-

ber band. The rubber band has a knot in the

center, and another individual holds the otherend of the band while facing you. The knot

is hovering over a red spot on the floor be-tween you. Your goal is to keep the knot overthe red spot. Now imagine the person across

from you moves her end of the band first toyour right and then to your left. In your at-

tempt to keep the knot situated exactly overthe red spot, you move it to your left and thento your right. Now, the other person takes a

step backward. In your effort to keep the knotover the spot you, too, step back. Now, imag-

ine instead that you are an observing scientist

attempting to develop a predictive model ofyour behavior without reference to the band,

the knot, or the spot. Looking only at the en-vironmental input (your partners behavior)

and your own behavioral response would giveone a misleading picture of the true underly-ing model. Upon observing your responses to

your partners initial moves to left and thenright, one might develop the hypothesis thatyour behaviors were in direct opposition to

your partners. That hypothesis would be dis-

proved when your partner stepped back andyou did the same. Now, imagine that yourpartner raises her end of the rubber band.What will you do? In your effort to keep

the knot squarely over the spot you mightalso raise your end. Or, you might lower your

end and step back. The goal remains simpleand constant: keep the knot over the spot.As the complexity of the environmental in-

put increases, however, it becomes impossi-ble to model the observable actions as a di-

rect functionof the environmental input. Yourpartners behavior may be the only observablemovement in the environment. Your partners

behavior, however, is not controlling yours.Your behavior is controlling the relationship

between the knot and the spot. Without ex-plicit recognition of the knot and the spot,any attempt to model your behavior will fall

short.

In Powerss model, the relationship btween spot and knot forms your referen

level. Any departure from the knot over tmark creates an error signal. This disturban

provokes a specific outputa behavioral tempt to restore the knot to its place over t

spot. By taking into account these propertof the control system (i.e., reference level aerror signal), it becomes possible to expla

more precisely the relationship between tenvironment and your behavior.

Powers argued that human behavior c

best be understood as a hierarchically nestset of control systems designed to cont

perception. Thousands of control systemay simultaneously organize behavior at agiven moment. Powerss (1973) original hi

archy of control systems contained nine leels, which later expanded to eleven (Pow

1989, p. 80). Thefirst-order through thefiftorder control systems concern fairly low-leperceptions (intensity, sensations, configu

tions, transitions, and events). Sixth-ordcontrol systems concern relationships (esp

cially temporal and spatial) between evensensations, configurations, forces, etc. Forample, I might have a reference level for pro

erly situating my reading glasses on my no

or for keeping a comfortable physical dtance between myself and student with whoI am talking. My efforts to maintain thoreference levels may lead me to respond

more proximate sensationssuch as the feing of my glasses at the tip of my no

Seventh-order control systems are categorof lower-level signals distinguished by sybols. Symbols are verbal and nonverbal g

tures that refer to or represent a set of lowelevel perceptions (for example a beckoni

wave, or the concept of doctor). Powers dscribes ninth-order control systems as prgrams or plans. These entail more compl

activities involving decision trees (for exaple, getting a degree, selecting a restaura

for dinner, planting a garden). Tenth-ordcontrol systems involve more abstract rerence levels such as principles and belie

Finally, eleventh-order control systems re

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to self-systemsusing system-level concepts

as reference levels. These entail entire sets ofprinciples or beliefs. When multiple controlsystems are in play, the higher-level control

systemsreceiveinputfrom,andsenderrorsig-nals to, lower-level control systems. Only the

lowest-level control system sends its outputto the environment. Only the highest-levelcontrol system receives a steady reference

signal.Higher-level control systems determine

the ultimate goal, with lower-level systemsoperating on more proximate goals deter-mined by both the higher-level systems and

the environmental input. Each control sys-tem is continuously processing environmental

input, comparing its current state to a refer-

ence level, assessing errors, and attemptingto resolve them by generating new behav-

iors. The amount of amplification as a sig-nal travels around a loop is called loop gain.

The output of any given control loop is itserror signal multiplied by the output gain.This tells us about the system sensitivity, or

how effective any disturbance will be in caus-ing a change of the controlled quantity. Sys-tems with high output gain respond aggres-

sively to even small errors and thus control

reference levels much more tightly. Controlsystems with low gain tolerate larger errors.Higher gain means more precise control (notnecessarily bigger responses). For a given er-

ror, the response would be amplified underhigh gain, but because the errors will be re-

duced by the increased response, that servesto decrease the output over time as the signaltravels around the control loop.

Those are the basic ideas contained inPowerss perception control theory. In addi-

tion to these ideas, Powers offered a modelspecification that allows for concrete predic-tions from theory. This specification enables

Powerss theory to remain generative, whileposing a clear contrasting model to the tra-

ditional stimulus-response causal model thatis the dominant form of argument in psycho-logical theory. This specification is also what

caught the attention of David R. Heise and

inspired him to develop affect control theory(Heise 1979, 2007), which became the first in

a series of contemporary sociological theoriesto make explicit use of control systems.

AFFECT CONTROL THEORYAs noted by Fararo & McClelland (2006,p. 17), one of the most developed sociolog-

ical traditions to make use of Powerss workon perception control theory is affect control

theory. The extensiveness of this literaturemakes a thorough treatment of that researchtraditionincluding more than 100 publica-

tions to datebeyond the scope of this briefreview. Consequently, I devote some time be-

low describing the basics of theory and its use

ofacontrolmodel,andIprovideabriefreviewof the relevant empirical literature.

Heise developed affect control theory asan empirically grounded, mathematical the-

ory of social interaction. Beginning with sym-bolic interactionist insights about the primaryimportance of language and of the symbolic

labeling of situations, the theory presumesthat people reduce existential uncertainty by

developing working understandings of theirsocial world using cultural symbols. After

creating this working definition, the theoryfurther argues that actors are motivated tomaintain it.

Affect control theory assumes that ourlabeling of social situations evokes affectivemeanings. These are the meanings that we try

to maintainduring interaction. Behavior is thevisible process by which we control affective

meanings. The theory uses three dimensionsof meaning to measure the affective mean-ings associated with specific labels, equations

to describe how events change those mean-ings, and a mathematical function to showwhat actions will best maintain or restore orig-

inal meanings. The theory is contained inthis three-part formalizationthe measure-

ment structure, the event reaction equations,and the mathematical statement of the con-trol process. The substance of the theory

is embodied in its mathematical expressions,


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i.e., the mathematical model predicts patterns

of social interaction that can then be testedempirically.

Using these three components, Heise de-

veloped a computer simulation to predictwhat people would do, think, and feel as

a result of social events (Heise & Lewis1988, Heise 2001). This programa com-bination of theoretical principles expressed

through mathematical operations and ofempirical estimates of parametersis often

used to generate theoretical predictions inthe affect control research program (e.g.,Robinson & Smith-Lovin 1992, Tsoudis

& Smith-Lovin 1998). A regularly updatedwebsite (http://www.indiana.edu/socpsy/ACT/index.htm) hosts the newest version of

the simulation program, along with currentdata sets, an affect control theory bibliogra-

phy, and a host of theory-based exhibits. Af-fect control theorists have also translated the

theory into verbal propositions (MacKinnon& Heise 1993, MacKinnon 1994) and refinedits scope (Robinson 2006, Robinson & Smith-

Lovin 2006).Affect control theory assumes that people

respond affectively to every social event (the

affective reaction principle). The theory

describes affective responses along threedimensions of meaningevaluation (good-bad), potency (powerful-weak), and activity(lively-quiet). These are universal dimensions

identified by Osgood and colleagues (1957,1975) that describe substantial variation in

the affective meaning of lexicons in morethan 20 national cultures.

The goodness, powerfulness, and liveli-

ness evoked by social concepts are referredto as sentiments in the theory. Sentiments

are trans-situational, generalized affective re-sponses to symbols that are widely sharedin a culture (or subculture). Affect control

theory researchers have used evaluation, po-tency, and activity ratings to index meanings

in a variety of different cultures, includingthe United States, Canada, Japan, Germany,China, and Northern Ireland. Within each

culture, average evaluation, potency, and ativity ratings are compiled into cultural d

tionaries that contain generalized meaninThere are three-number evaluation-poten

activity profiles for hundreds of identities, bhaviors, traits, emotions, and settings in ea

culture. These sentiment profiles locate thecultural symbolspotential elements of scial eventsin the same three-dimension

affective space.After we define a social situation using c

turally meaningful labels, the affective mea

ings change as social interaction unfolThese situated meanings are called transie

impressions: contextualized affective meaings that arise from labeling of specific socinteractions. Affect control theory uses a

of impression-formation equations to predchanges in the impressions of the identit

and behaviors as a result of social interactioThese impression-change equations are epirical summaries of basic social and cultu

processes. Along with the sentiment dictinaries, these equations provide the empi

cal basis for affect control theorys theoretipredictions.

Sentiments are the culturally shared, fu

damental meanings that we associate with s

cial labels. Impressions are the more transiemeanings that arise as social interactions ufold. Discrepancies between sentiments aimpressions tell us something about h

well interactions that we experience aconfirming cultural prescriptions. The co

trol system part of the theory models tsymbolic interactionist assumption that scial actors try to maintain their worki

definitions of social situations (the affect cotrol principle). Sentiments act like a th

mostat settinga reference level for intpreting what happens in a situation. Whimpressions vary from that reference le

(as the temperature might vary in a roompeople do things to bring the impressio

back in line with cultural sentiments. Afect control theory defines deflection as tdiscrepancy between fundamental cultu

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sentiments and transient situated impres-

sions (in the three-dimensional evaluation-potency-activity space). After an event thathasdisturbed meanings, solving for the behavior

profile produces the creative response that anactor is expected to generate to repair the sit-

uation. These behavioral predictions are theprimary output of the control system.

The Social Nature of Affect Control

In affect control theory, meanings of situa-tions are controlled. The fundamental senti-ments attached to all elements of a situation

(actor, object, traits, behaviors, emotions, set-ting, etc.) serve as the reference signal. The

transient impressions of all of these elements

combine to serve as the input signal. Deflec-tion (the error signal) is operationalized as the

sum of the squared differences between all ofthe reference signal and perceptual signal el-

ements on all three dimensions of meaning(evaluation, potency, and activity). When thiserror signal is minimized, the affect associated

with the entire definition of the situation is si-multaneously controlled. The output of this

control system is a behavior that would op-timally confirm the original definition of the

situation.The idea that actors strive for consistency

in meanings of entire situations illustrates the

more social focus of affect control theory rela-tive to its predecessor, perception control the-ory. Moreover, the fact that all actors in a

setting are simultaneously working to main-tain reference levels for relevant role iden-

tities, actions, and settings leads the controlprocess to be a truly social enterprise in af-fect control theory. The actions of others

are modeled explicitly as possible remediesto a disturbed identity. Affect control theory

specifies in detail the way that the actionsand identities of others contribute to onesown identity meanings. One consequence of

this is that one can solve for a variety of newactions that are substitutable in order to re-

solve a disturbance to any part of the situ-

ational definition. Some implications of thissubstitutability are discussed at the end of this

review.

Empirical Support

Empirical tests and illustrations of affect con-trol theory span a remarkable variety ofmethodologies and topics. A large number of

studies support the basic control-based pre-dictions of affect control theory. The studies

find that people act to restore their identi-ties when those identities are disturbed (e.g.,Wiggins & Heise 1987), choose to interact

with those who support their identity mean-ings (e.g., Robinson & Smith-Lovin 1992),

prefer occupations whose meanings are con-

sistent with other identity meanings (Moore& Robinson 2006), use emotions as cues to

label people whose identities are ambiguous(e.g., Tsoudis & Smith-Lovin 1998), and cre-

ate new role identities that reinforce personalidentity meanings (Smith-Lovin & Douglass1992).

Much of the work in this highly quan-titative tradition uses surveys, simulations,

vignettes, and/or laboratory experiments.However, there are some notable qualitative

examinations of affect control theory predic-tions in field settings. In an interesting ex-ample, Francis (1997) studied two support

groups: a divorce support group and a be-reavement support group. As one might ex-pect, members entered each of these groups

with negative identities and unpleasant, pow-erless emotions evoked by the identities of

widow/er and divorcee. Francis found that,rather than focusing directly on these nega-tive, weak self-feelings, support group lead-

ers worked on the identities of the groupmembers former partners. In this way, they

helped members create more positive andpowerful new constructions of identities forthemselves that would generate positive feel-

ings from new events. Surprisingly, they pro-moted more negative relabelings of the for-

mer partners, attributing them with more


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responsibility for the divorce or death. The

group leaders provided an alternate means forreducing deflectionby redefining the situ-ation in such a way that the support group

members could comfortably confirm a morepositive identity.

Affect control theorys emotion predic-tions have enjoyed considerable empiricalsupport (Robinson & Smith-Lovin 1992,

1999; Robinson et al. 1994; Tsoudis & Smith-Lovin 1998, 2001; Stets 2003, 2005). These

studies find that negative emotion resultsfrom social situations that fail to supportpositive identities or that confirm negative

identities. Robinson & Smith-Lovin (1992)also showed that positive emotions can re-

sult when those occupying negative identities

are treated too positively by their interactionpartners, supporting affect control theorys

predictions.

IDENTITY CONTROL THEORY

Building on the structural symbolic inter-

action tradition of identity theory (Stryker1980), Burke (1991) imported ideas from

Powers (1973) to develop a theory intendedto describe the internal processes of self-

verification left implicit in previous versionsof identity theory. The identity theory tradi-tion views the self-structure as a collection of

identities that are activated by different sit-uations. Identities are arranged in a hierar-chy of salience that predicts which identity

an actor will choose when there are multi-ple options available in a situation. In iden-

tity theory, social structure determines thenature of this salience hierarchy and, thus,determines predictions about which identi-

ties individuals are likely to activate. Burke(1991) developed identity control theory to

describe the process by which identities, onceactivated, translate into specific behaviors andfeelings. Like affect control theory, this tra-

dition has generated an abundance of cumu-lative published tests and extensions of the

theory. Constrained by space limitations, Iprovide only a brief overview of this work.

Owing to the similarities between the twtheories, this overview focuses on departu

from affect control theory and novel applictions and implications.

Identity as a Control SystemIn identity control theory, reflected apprais(self-relevant social actions from others in

social situation) serve as input signals aare compared with identity standards (cu

turally prescribed meanings that define tidentity), which serve as reference signaWhen reflected appraisals fail to confi

identity standards, this disconfirmation cates a discrepancy, or error signal, that lin

directly to both behavior and emotion (ou

put function) in the theory. Specifically, whreflected appraisals do not support ident

meanings, actors experience negative emtion and generate new actions to resto

meanings (Burke 1991). Behavior is the vible part of the process by which we contidentity.

In identity control theory, the self is a dtinct control system with individuals behavi

to control their own self-meanings. The pceiver is the person who occupies the ident

standard being controlled. Individuals maymultaneously be controlling sets of hierarccally nested identity standards, but the hig

est level in the hierarchy is still in the s(Tsushima & Burke 1999). Nonetheless,with affect control theory, its control proc

is a fundamentally social enterprise. Althouindividuals control their own identity mea

ings, those meanings are often culturally drived and shared. Thus, identity control thory models the maintenance of stable soc

systems as the controlling of common idetity standards by individuals within a sharculture (Burke 2006).Further, following ide

tity theory, research in identity control theofocuses mainly on relationship-based iden

ties and places considerable importance the role of counter-identities for activatiof and feedback about the focal identity. S

an interaction with my stepson activates m

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identity as stepmother and provides relevant

reflected appraisals as feedback for thatidentity.

Like affect control theory, identity con-

trol theory also adopts the general semanticdifferential framework for representing iden-

tity meaning. In contrast with affect con-trol theory, however, work in this traditionuses a more detailed measurement approach

that picks up more local and nuanced aspectsof meaning. Research in the identity control

theory tradition frequently measures identitymeanings in social surveys at the individuallevel (often using evaluative dimensions sim-

ilar to self-concept measures), and then looksto see if those meanings shift over time (of-

ten a period of years) as a result of their

social environment. The social environment,often comprising the views that significant

others have of the focal respondent, is as-sumed to expose the respondent to consis-

tent reflected appraisals that will influencemeanings and emotional responses. Com-pared with affect control theory, the approach

is more relational (in that the identity verifi-cation process is often studied in the contextof ongoing social relationships), more indi-

vidual (in that it is individual meanings are

held for a role identity that constitute thereference level), and less situational (in thatthe interactions that produce reflected ap-praisals are more often inferred than observed

or modeled) (Smith-Lovin & Robinson2006).

Identity control theorys use of the con-trol system is more a metaphor than a cal-culus. Like most sociological theories, iden-

tity control theory is presented in verbalrather than mathematical form. Some for-

mulations have developed propositional lists,and almost all of the empirical papers de-velop hypotheses, but these are derived from

the general verbal statement of the theory.The control system imagery guides the logic

of identity control theory and its predictionsbut does not form a mathematical basis of itspredictions.

Hierarchy and Gain

Beyond its basic control system imagery, iden-tity control theorymakes use of key additionalfeatures of Powerss (1973) perception control

theory model. Most notably, the theory incor-porates the idea of the self as a nested hierar-

chy of control systems (Stets & Carter 2006,Tsushima & Burke 1999). By positing identi-ties as operating at inherently different levels,

this notion opens up a whole host of avenuesfor recastingclassical sociological questions

such as those about role conflict and even rolecomplexity. For example, one might have anidentity standard as an exemplary parent that

comprises several nested identity standards,such as being fun and being responsible. Al-

though these lower-level identity standards

may at times be in direct competition witheach other for behavioral predictions, these

disputes will be solved under a hierarchicalcontrol model in favor of optimally fulfilling

the requirements of the higher-level standard,in this case of exemplary parent.

Identity control theory, in principle, also

makes use of Powerss concept of system gain.Burke (1991) argues that some identities are

more tightly and some more loosely con-trolled than others. The theory does not, as

of yet, contain a model of which identities aremore likely to be tightly or loosely controlled.It does, however, argue that more tightly con-

trolled identities (corresponding to controlsystems with higher gain) produce larger andmore frequent errors owing to relatively rou-

tine discrepancies. Accordingly, even if twoindividuals sharesimilar identity standards for

what it means to be a good environmentalist,if one of those individuals more tightly con-trols the identity, that individual will experi-

encegreater distress fromevenminor discrep-ancies owing to the increased amplification of

the error signal.

Empirical Support

As with affect control theory, numerous stud-

ies in the identity control theory tradition


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support its use of the basic control imagery

(e.g., Burke 1991, 2006; Burke & Cast 1997;Burke & Stets 1999; Riley& Burke 1995; Stets& Carter 2006; Tsushima & Burke 1999).

These studies examine identity control pro-cesses in parent-child and husband-wife rela-

tionships,aswellasinsmallgroupinteractionsbetween strangers. Empirical research in theidentity control theory tradition also provides

support for the notion of hierarchy in identitycontrol processes. Tsushima & Burke (1999)

found, for example, that mothers who havegeneral principles to guide their child raisingphilosophies are more consistent in their be-

haviors while occupying the mother identity,and better able to maintain the meanings as-

sociated with that identity.

According to identity control theory, whenreflected appraisals do not result in identity

verification, this results in a decrease in self-efficacy, which in turn results in amplified ef-

forts at interpersonal influence and aggres-sion(Stets&Burke2005,p.163).Self-efficacyprovides the mechanism through which iden-

tity disconfirmation generates behavior. Stets& Burke (2005) found support for these pre-dictions in a study of newly married couples.

They used target individuals ratings of how

much they felt they should engage in elevenspousal role activities as a measure of theirspousal role identity standard. They used thespouses ratings of how much they felt the tar-

get individual should engage in those role ac-tivities as a proxy for reflected appraisals. Stets

& Burke acknowledge that this proxy for re-flected appraisals is at least two steps awayfrom the desired measure. Making the as-

sumption, however, that discrepancy betweentwo spouses expectations for the behavior of

a target spouse will be related to the discrep-ancy between theidentity standards of thetar-get spouse and the reflected appraisals of that

target generated by the other spouse, Stets &Burke tested their model. Identity verification

led to increased self-efficacy and decreased at-tempts to dominate ones partner in years twoand three. They also found that for wives,

but not for husbands, decreased self-efficacy

led to increased attempts to dominate thspouses.

SOCIOLOGICAL APPLICATIONOF PERCEPTION CONTROL

THEORYAt about the same time that Burke and cleagues were developing identity control th

ory, a line of research emerged in sociolothat makes direct use of Powerss original pception control theory (e.g., McPhail et

1990). Research in this tradition has tendto make minor extensions and novel applic

tions of the original theory, rather than dveloping a distinct variant. Accordingly, bcause the basics of perception control theo

appear above, this section omits a theoretioverview and goes right to a review of the th

oretical extensions and empirical applicatioof perception control theory in sociology.

One enterprising use of control theory

sociology is McPhails (1991) work on tsociocybernetic theory of collective beha

ior. McPhail and colleagues (McPhail 199McPhail et al. 1990, 2006) make use Powerss perception control theory to addr

explicitly the problem of collective, coor

nated action. In the basic perception conttheory model, we have direct access to nther the perceptual signals nor the referensignals of others. By virtue of operating in t

same social environment, we often have oportunities to impute others perceptual s

nals. However, others reference signals aoften implicit, or even mysterious. In Myth

the Madding Crowd, McPhail (1991) sugge

three ways in which we can come to have acess to one anothers reference signals and

come to behave according to the same comparator function:

1. Independently similar reference sign

2. Interdependently generated referensignals

3. Third-party generated referencesign

Using computer simulations, McPhail acolleagues (McPhail et al. 1990, Tucker et

168 Robinson

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1999) employ perception control theory to

analyze how the movements of collections ofactors, simulated as agents operating with in-dividual control systems, have similar prop-

erties to those observed in nonsimulated so-cial environmentslike parks, fairs, shopping

malls, protests, rallies, and revivals. In thesesimulations, the behaviors of others serve asenvironmental input (as in identity control

theory and affect control theory), contribut-ing to the level of disturbance in an indi-

viduals own control system. In other words,they show how apparently coordinated actionarises without the need for shared reference

signals.In more recent work, McPhail and col-

leagues (2006) condense Powerss eleven lev-

els of control systems into three categoriespresymbolic perceptions (the first six

levels, dealing with pretty low-level percep-tions, mainly concerned with bodily expe-

riences), symbolic perceptions (categories,sequences, programs), and meta-symbolicperceptions (system concepts, principles).

They describe these as the how (presymbolicpurposes), what (symbolic purposes) andwhy (meta-symbolic purposes) of purposive

action. They illustrate the way that categories

can describe complex collective behaviorswith a study of the Promise Keepers 1997rally in Washington, DC (McPhail et al.2006).

Schweingruber (2006) applies these levelsin an ethnographic analysis of door-to-door

booksellers. Schweingruber spent a year re-searching the company and observing salesemployees in the field. He describes how a

company selling educational books (Enter-prise Company) makes use of rules at each of

these levelsin order to optimizetheselling be-havior of itsemployees.First, he notes that thecompany devotes a significant amount of its

sales training to the presymbolic purposesor the how of bookselling. Sales person-

nel learn detailed scripts describing the phys-ical procedures of a sales interaction, includ-ing how to stand, when and how quickly to

move, and what direction to face. Sellers also

learn a set of symbolic purposes (the whatof bookselling). Some of these come in the

form of learning new categories, such as howto classify sales interactions as calls, door de-

mos, sit-down demos, weak sales, or strongsales. Enterprise sellers also learn sequences

and programs, often in the form of interre-lated sets of sales scripts. Finally, the Enter-prise sales force learnsthe why of Enterprise

bookselling, a set of meta-symbolic purposesin the form of what the company calls emo-tional training. These include beliefs that the

bookselling experience builds character, mak-ing one a finisher, and even a better man or a

better woman.On the basis of these observations,

Schweingruber argues that the hierarchical

nature of the nested control loops for thesevarious purposes is useful for the company.

Learning and complying with the long andtedious list of the what purposes that arenecessary to be a successful salesperson is

daunting without the larger organizationof the how purposes. Beyond that, the

long hours, physical discomfort (heat, rain,blisters), unpleasant interactions, and limitedsuccess of door-to-door sales would be

difficult to sustain with only the how

purposes as motivation. Consequently, thesales employees who most intensely adoptthe Enterprise way of thinking (the whypurposes) are more dedicated and more

satisfied than those who remain skeptical.For these party-liners, the lower-level steps

and sequences are merely means of fulfillingtheir higher-level aspirations.

A related sociological application of per-

ception control theory appears in the workby McClelland (1994, 2004, 2006) that

tries to model collective control processes.McClelland focuses primarily on what hap-pens when two or more social actors strive

to control a signal variable in their commonenvironment. McClelland (1994) argued that

sociologists could better understand poweras a structural feature of situations that re-sult from the alignment of multiple refer-

ence signals and force as an interpersonal


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attempt to create a disturbance in someone

elses attempts to control a particular percep-tion. McClelland (2004) later used computersimulations based on the original perception

control model to simulate what happens whenmultiple actors control shared environmen-

tal variablesunder conditions of misalignment(unsharedreferencesignals) and differences ingain (different tolerances for discrepancies).

The results of these simulations prompted areevaluation of one of his earlier arguments

namely, that alignment is crucial for collec-tive action. These results, however, generatedsupport for some of his other arguments

including that individuals have greater socialpower when their reference signals are more

closely aligned.

McClelland (2006) summarized the keyfindings of these simulations: (a) The tight-

ness of a collective control system is propor-tionaltothesumofthegainofallparticipating

control systems. This means that more indi-viduals controlling the same shared referencewill have greater social poweror a more ef-

fective control over that reference value. (b)Misalignment of reference signals does not

necessarily weaken gain. In other words, indi-viduals working with different goals can be

effective nonetheless in their collective ef-forts. (c) Misalignment of reference signalsdoes, however, produce some necessary level

of conflict. Further, McClelland argues thatbecause reference signals can almost never beperfectly aligned, most collective efforts will

be accompanied by some level of interper-sonal conflict.

INSTITUTIONS AS CONTROL

SYSTEMSIn a more novel sociological application of

control system ideas, Fararo & Skvoretz(1984, 1986, 2006; Skvoretz & Fararo 1980,1989, 1996) develop a model of institutions as

cybernetic structures. In this model, behavioris the visible part of the process by which ac-

torsseekto control institutional norms. Theseresearchers model institutions as distributed

production systems, containing grammars

if-then rules that can generate action in rsponse to environmental inputs. These rumake reference to types of institutional a

tors, types of situations (including physiand cultural objects), and types of actio

The theorys most basic principle is that stitutions are stable designs for action that arepresentable by production systems (Fara

& Skvoretz 1986). These researchers view stitutional production systems as social soware, organized systems of statements a

rules to be instantiated in whatever situatioand populations the institution is releva

They use the metaphor of a social menucapture the idea that individuals often havconstrained array of institutionally approp

ate choices that results in a structured fredom. This theory of institutions as cyberne

action systems distinguishes between instrmental and expressive action.

Fararo & Skvoretz model institutionaliz

social action as the control of discrete refence states in a production system. This d

parts in some interesting ways from the cotrol theory approaches reviewed above. Firthecontrolledprocess is modeled as a discre

rather than continuous, process (Fararo

Skvoretz 2006). Observed states are comparwith expectations to determine whether thsatisfy some set of conditions. This matcing of observations to expectations determin

the nature of a behavioral response. Usithe production system notation of New

& Simon (1972), Fararo & Skvoretz (198p. 224) provide some illustrations of somethese if-then rules pulled out of their instit

tional context:Waitress: order unserved CHECK

[kitchen]Student: registration-time

SELECT [courses] (cou

schedule list)Mother: child ego CARE [chi

(need-of-chiMourner: relative-of-deceased prese

new EXRESS [condolen

(relative of deceased

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In these examples, x is a placeholder

for a range of values that could be present.The left-hand side of these if-then rulesrepresents the conditions to be satisfied, and

the right-hand side represents the actions tobe taken when those conditions are met.

A second (and related) departure of thisapproach is that it focuses on instructionalcontrol, rather than negative feedback control

(Fararo & Skvoretz 1986). In a negative feed-back control system, a reference state is com-

pared with an input, and the magnitude andnature of the difference (or error) determinesthe nature of the corrective response (the out-

put). In an instructional control system, a ref-erence state is compared with an input to see

if there is a match. If there is not, then the

program moves on to thenext comparison un-til there is a match. When the conditions are

satisfied, the system generates an output (aninstrumental or expressive behavior).

CONCLUSIONS

The cybernetics and general social systemtheory movements of the 1940s led to a pro-

ductive proliferation of formal sociologicaltheories making use of control system ideas.

Control system theories in sociology are di-verse in form as well as in empirical focus.They share a commonmechanismfor describ-

ing equilibrium processes in social phenom-ena at various levels of analysis. It is impor-tant to note that although negative feedback

loop systems are useful for modeling equilib-rium processes, this does not mean that sys-

tems premised on negative feedback generatestatic outcomes. To the contrary, these typesof control systems are used in engineering and

elsewhereto model adaptive,goal-seeking be-havior. They provide a useful tool for under-

standing systems that are both relatively sta-ble and responsivethe very properties thatcharacterize social institutions.

The use of control systems to study soci-ological questions offers a number of key ad-

vantages over conventional alternatives. First,these theories take us away from the overso-

cialized view of social actors, without leavingus to rely exclusively on grounded theory ap-

proaches. Quite the contrary, these modelsallow for spontaneity and creativityin the

context of a generative theory of social be-havior. The structure of these theories is dif-

ferent in crucial ways from our more conven-tional, linear ways of formal theorizing aboutsocial behavior. Not all social actors respond

in thesame way to thesame environmental in-put. Moreover, the same social actors do notconsistently respond in the same way to the

same environmental input. Models of socialbehavior that use only environmental condi-

tions as input will never account for the levelsof innovation and spontaneity we observe inour social surroundings. Without taking into

account the other source of input (the refer-ence signal), any attempt to understand goal-

seeking behavior will fall short. Further, with-out taking into account the reference signalsin social actors control systems, we ignore

culture.Smith-Lovin & Robinson (2006, pp. 233

35) offer some implications from affect con-trol theory that highlight the importance ofthese insights.Actors are substitutable in their

role as meaning managers. Recall that in this

theory, social meaning for all elements of asituation (actors, objects, actions, emotions,settings) are controlled simultaneously. Thecombined error (deflection) for all these social

elements is reduced to generate predictions.As a consequence, for any given disruption

in situational meanings there are multipleroutes to repair. For example, social actorsmay be entirely substitutable in their ability

to engage in reparative action after a deflec-tion. When one friend insults another in an

interaction between four pals at the schoolyard, a possible restorative act is for one ofthe observers to console the hurt friend. The

theory does not predict which observer ismost likely to do sothe two observers are

interchangeable in this capacity. Affectivelysimilar actions are substitutable in the abil-ity to restore meaning. Because the theory

considers only the relationship between the


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reference level affective meanings of a sit-

uation and the transient affective meaningsof that situation, behaviors whose affectivemeanings are identical are also identical un-

der the theory. Affectively different actionscan be substitutable when performed by ac-

tors whose identities are affectively different.Affect control theory contains a precise spec-ification of the relationship between various

actors identities and behaviors. The actionsnecessary for an actor with a soiled identity to

help repair anothers identity are predicted tobe different from those of an actor operatingin a positive identity. This interchangeabil-

ity of acts, persons, and person-acts helps usto see how a control systembased theory (in

thiscase,affectcontroltheory)canaccountfor

the complexity of individual behavioral possi-

bilities in social interactions, while focusion the larger, more stable aspects of meani

maintenance at the situation level.Finally, the true measure of how impo

tant control system ideas are becoming in sciology lies in their theoretical and practi

utility. Sociological control system theorhave amassed an impressive list of empiritests and applications. The research review

here uses control system ideas to examine tcrowds at protests and rallies, mock jury dliberations, task group interactions, ident

disruption and repair, routine and nonroutiinstitutional behavior, and the process of b

reavement, to name a sampling. The most sential advantage offered by these approachis their ability to successfully model fund

mental social processes.

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Annual Review

of Sociology

Volume 33, 2007Contents

Frontispiece

Leo A. Goodman p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p x

Prefatory Chapter

Statistical Magic and/or Statistical Serendipity: An Age of Progress in

the Analysis of Categorical DataLeo A. Goodman p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 1

Theory and Methods

Bourdieu in American Sociology, 19802004

Jeffrey J. Sallaz and Jane Zavisca p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 21

Human Motivation and Social Cooperation: Experimental and

Analytical Foundations

Ernst Fehr and Herbert Gintis p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 43

The Niche as a Theoretical Tool

Pamela A. Popielarz and Zachary P. Neal p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 65

Social Processes

Production Regimes and the Quality of Employment in Europe

Duncan Gallie p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 85

The Sociology of Markets

Neil Fligstein and Luke Dauter p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 105

Transnational Migration Studies: Past Developments and Future TrendsPeggy Levitt and B. Nadya Jaworsky p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 129

Control Theories in Sociology

Dawn T. Robinson p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 157

Institutions and Culture

Military Service in the Life Course

Alair MacLean and Glen H. Elder, Jr. p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 175

v

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School Reform 2007: Transforming Education into a Scientific

Enterprise

Barbara L. Schneider and Venessa A. Keesler p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p

Embeddedness and the Intellectual Projects of Economic Sociology

Greta R. Krippner and Anthony S. Alvarez p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p

Political and Economic Sociology

The Sociology of the Radical Right

Jens Rydgren p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p

Gender in Politics

Pamela Paxton, Sheri Kunovich, and Melanie M. Hughes p p p p p p p p p p p p p p p p p p p p p p p p p p p p

Moral Views of Market Society

Marion Fourcade and Kieran Healy p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p

The Consequences of Economic Globalization for AffluentDemocracies

David Brady, Jason Beckfield, and Wei Zhao p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p

Differentiation and Stratification

Inequality: Causes and Consequences

Kathryn M. Neckerman and Florencia Torche p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p

Demography

Immigration and Religion

Wendy Cadge and Elaine Howard Ecklundp p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p

Hispanic Families: Stability and Change

Nancy S. Landale and R.S. Oropesa p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p

Lost and Found: The Sociological Ambivalence Toward Childhood

Suzanne Shanahan p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p

Urban and Rural Community Sociology

The Making of the Black Family: Race and Class in Qualitative Studies

in the Twentieth CenturyFrank F. Furstenberg p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p

Policy

The Global Diffusion of Public Policies: Social Construction,

Coercion, Competition, or Learning?

Frank Dobbin, Beth Simmons, and Geoffrey Garrett p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p

v i C on te nt s

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Workforce Diversity and Inequality: Power, Status, and Numbers

Nancy DiTomaso, Corinne Post, and Rochelle Parks-Yancy p p p p p p p p p p p p p p p p p p p p p p p p p p p p 473

From the Margins to the Mainstream? Disaster Research

at the Crossroads

Kathleen J. Tierney p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 503

Historical Sociology

Toward a Historicized Sociology: Theorizing Events, Processes, and

Emergence

Elisabeth S. Clemens p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 527

Sociology and World Regions

Old Inequalities, New Disease: HIV/AIDS in Sub-Saharan Africa

Carol A. Heimer p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 551

Indexes

Cumulative Index of Contributing Authors, Volumes 2433 p p p p p p p p p p p p p p p p p p p p p p p p 579

Cumulative Index of Chapter Titles, Volumes 2433 p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 583

Errata

An online log of corrections to Annual Review of Sociology chapters (if any, 1997 to

the present) may be found at http://soc.annualreviews.org/errata.shtml

annurev.soc.32.06160477777

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