What makes organizations so similar? We contend that the engine of rationalization and bureaucratization has moved from the competitive marketplace to the state and the professions. Once a set of organizations emerges as a field, a paradox arises: rational actors make their organizations increasingly similar as they try to change them. We describe three isomorphic processes-coercive, mimetic, and normative—leading to this outcome. We then specify hypotheses about the impact of resource centralization and dependency, goal ambiguity and technical uncertainty, and professionalization and structuration on isomorphic change. Finally, we suggest implications for theories of organizations and social change.

The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields (Chinese Translation)

The computational brain: Patricia S. Churchland and Terrence J. Sejnowski (MIT Press, Cambridge, MA, 1992); xi, 544 pages, $39.95

Social psychological research has taught us a lot about why people protest. This article provides a theoretical and empirical overview. Discussed are grievances, efficacy, identification, emotions ...

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The social psychology of protest

■ Abstract Sociologists often model social processes as interactions among variables. We review an alternative approach that models social life as interactions among adaptive agents who influence one another in response to the influence they receive. These agent-based models (ABMs) show how simple and predictable local interactions can generate familiar but enigmatic global patterns, such as the diffusion of information, emergence of norms, coordination of conventions, or participation in collective action. Emergent social patterns can also appear unexpectedly and then just as dramatically transform or disappear, as happens in revolutions, market crashes, fads, and feeding frenzies. ABMs provide theoretical leverage where the global patterns of interest are more than the aggregation of individual attributes, but at the same time, the emergent pattern cannot be understood without a bottom up dynamical model of the microfoundations at the relational level. We begin with a brief historical sketch of the shift from “factors” to “actors” in computational sociology that shows how agent-based modeling differs fundamentally from earlier sociological uses of computer simulation. We then review recent contributions focused on the emergence of social structure and social order out of local interaction. Although sociology has lagged behind other social sciences in appreciating this new methodology, a distinctive sociological contribution is evident in the papers we review. First, theoretical interest focuses on dynamic social networks that shape and are shaped by agent interaction. Second, ABMs are used to perform virtual experiments that test macrosociological theories by manipulating structural factors like network topology, social stratification, or spatial mobility. We conclude our review with a series of recommendations for realizing the rich sociological potential of this approach.

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FROM FACTORS TO ACTORS: Computational Sociology and Agent-Based Modeling

We describe some of the capabilities of the ergm package and the statistical theory underlying it. This package contains tools for accomplishing three important, and interrelated, tasks involving exponential-family random graph models (ERGMs): estimation, simulation, and goodness of t. More precisely, ergm has the capability of approximating a maximum likelihood estimator for an ERGM given a network data set; simulating new network data sets from a tted ERGM using Markov chain Monte Carlo; and assessing how well a tted ERGM does at capturing characteristics of a particular network data set.

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ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks.

In this article, we use newly developed statistical methods to examine the generative processes that give rise to widespread patterns in friendship networks. The methods incorporate both traditional demographic measures on individuals (age, sex, and race) and network measures for structural processes operating on individual, dyadic, and triadic levels. We apply the methods to adolescent friendship networks in 59 U.S. schools from the National Longitudinal Survey of Adolescent Health (Add Health). We model friendship formation as a selection process constrained by individuals’ sociality (propensity to make friends), selective mixing in dyads (friendships within race, grade, or sex categories are differentially likely relative to cross-category friendships), and closure in triads (a friend’s friends are more likely to become friends), given local population composition. Blacks are generally the most cohesive racial category, although when whites are in the minority, they display stronger selective mixing than do blacks when blacks are in the minority. Hispanics exhibit disassortative selective mixing under certain circumstances; in other cases, they exhibit assortative mixing but lack the higher-order cohesion common in other groups. Grade levels are always highly cohesive, while females form triangles more than males. We conclude with a discussion of how network analysis may contribute to our understanding of sociodemographic structure and the processes that create it.

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Birds of a Feather, or Friend of a Friend? Using Exponential Random Graph Models to Investigate Adolescent Social Networks

Recent research has focused on the role of social networks in facilitating participation in protest and social movement organizations. This paper elaborates three currents of microstructural explanation, based on information, identity, and exchange. In assessing these perspectives, it compares their treatment of multivalence, the tendency for social ties to inhibit as well as promote participation. Considering two dimensions of multivalence—the value of the social tie and the direction of social pressure—this paper discusses problems of measurement and interpretation in network analysis of movement participation. A critical review suggests some directions for future research.

Mobilizing in Black Boxes: Social Networks and Participation in Social Movement Organizations

This paper reports on biases in group members' inferences about collective support for group norms. Whereas theories of looking glass perception suggest a tendency to project our own preferences onto others, this paper shows that observed biases simply may reflect flows of information through social networks. Members conceal counternormative behavior and disclose it selectively within confidence relations. This process yields structured inference, in which members'inferences depend on their social ties, and also pluralistic ignorance, in which members generally overestimate collective support for existing norms. These predictions are evaluated in a field study of perceived normative consensus in five vegetarian housing cooperatives. Results fail to support the intrinsic bias argument, but demonstrate these forms of in formation bias.

Egocentric bias or information management? Selective disclosure and the social roots of norm misperception

New technologies have made it possible to collect information about social networks as they are acted and observed in the wild, instead of as they are reported in retrospective surveys. These technologies offer opportunities to address many new research questions: How can meaningful information about social interaction be extracted from automatically recorded raw data on human behaviorq What can we learn about social networks from such fine-grained behavioral dataq And how can all of this be done while protecting privacyq With the goal of addressing these questions, this article presents new methods for inferring colocation and conversation networks from privacy-sensitive audio. These methods are applied in a study of face-to-face interactions among 24 students in a graduate school cohort during an academic year. The resulting analysis shows that networks derived from colocation and conversation inferences are quite different. This distinction can inform future research in computational social science, especially work that only measures colocation or employs colocation data as a proxy for conversation networks.

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Inferring colocation and conversation networks from privacy-sensitive audio with implications for computational social science

Centralized sanctions (selective incentives) and informal norms have been advanced as distinct solutions to collective action problems. This article investigates their interaction, modeling the eme...

James A. Kitts

Papers

Birds of a Feather, or Friend of a Friend? Using Exponential Random Graph Models to Investigate Adolescent Social Networks

Mobilizing in Black Boxes: Social Networks and Participation in Social Movement Organizations

Egocentric bias or information management? Selective disclosure and the social roots of norm misperception

Inferring colocation and conversation networks from privacy-sensitive audio with implications for computational social science

Collective Action, Rival Incentives, and the Emergence of Antisocial Norms: