Peter Hedström

Social mechanisms : an analytical approach to social theory

Acknowledgements List of contributors 1. Social mechanisms: an introductory essay Peter Hedstrom and Richard Swedberg 2. Social mechanisms and social dynamics Thomas C. Schelling 3. A plea for mechanisms Jon Elster 4. Real virtuality Gudmund Hernes 5. Concatenations of mechanisms Diego Gambetta 6. Do economists use social mechanisms to explain? Tyler Cowen 7. Social mechanisms of dissonance reduction Timur Kuran 8. Social mechanisms without black boxes Raymond Boudon 9. Is sociological theory too grand for social mechanisms? Axel van den Berg 10. Theoretical mechanisms and the empirical study of social processes Aage B. Sorensen 11. Monopolistic competition as a mechanism: corporations, universities, and nation-states in competitive fields Arthur L. Stinchcombe 12. Rational imitation Peter Hedstrom Indexes.

Contagious Collectivities: On the Spatial Diffusion of Swedish Trade Unions, 1890-1940

This article how distances and relations between actors are likely to influence the growth and spread of social movements. A formal theoretical model is developed that extends previous work on threshold models of collective behavior. Spatial distribution of a population influences the network that are likely to emerge within the population; these network, in turn, will influence the likely outcome of a mobilization effort. Key theoretical predictions are tested using data on the founding of local union organizations in Sweden, 1890-1940. The empirical analyses show that contagious spatial processes were of considerable importance for the growth of the Swedish union movement, thus supporting the theoretical argument. The analyses presented in the article provide an alternative interpretation of density-dependent founding rates to the one offered by organizational ecologists.

Mesolevel Networks and the Diffusion of Social Movements: The Case of the Swedish Social Democratic Party1

In analyzing the spatial diffusion of the Swedish Social Democratic Party, this article introduces the notion of a mesolevel network. A mesolevel network is a social network that differs in three important respects from interpersonal microlevel networks directly linking prior and potential adopters of a practice to one another: (1) it is generated by a different causal process than the microlevel network; (2) it tends to be much sparser than the microlevel network; and (3) the typical edge of a mesolevel network bridges much longer sociometric and geographic distances than the typical edge of a microlevel network. These types of mesolevel networks are important because they can dramatically influence the speed at which a contagious practice will diffuse. The mesolevel network focused upon in this article is the network that emerged out of the travel routes of political agitators affiliated with the Social Democratic Party. Computational modeling shows that the diffusion of the Social Democratic Party is likely to have been considerably influenced by the structure of this network. Empirical analyses of the founding of party organizations during the period 1894–1911 support these theoretical predictions and suggest that this mesolevel network was of considerable importance for the diffusion of the Swedish Social Democratic Party.

Organizational Differentiation and Earnings Dispersion

This article analyzes earnings distribution within organizations. A formal model specifies mechanisms through which the hierarchical structure of organizations is likely to influence the extent of earnings dispersion. The predictions derived from the model are confronted with data on the pay structure and formal organizational structure of 5,600 Swedish manufacturing establishments. The empirical analyses largely confirm the predictions derived from the model: the extent of earnings dispersion within an organization is closely related to the vertical and horizontal differentiation of its formal structure. This conclusion holds true even when the sex, education, and work experiences of those populating the organizational structures are controlled for.

Recent Trends in Agent-based Computational Research A Brief Introduction

Agent-based modeling is becoming increasingly important in most of the behavioral sciences. It is used productively for purely theoretical purposes as well as for empirically oriented analyses in such varied fields as economics (Farmer and Foley 2009), finance (Mathieu, Beaufils, and Brandouy 2005), political science (De Marchi and Page 2014), geography (O’Sullivan 2008), criminology (Birks, Townsley, and Stewart 2012), epidemiology (Auchincloss and Roux 2008), social psychology (Smith and Conrey 2007), demography (Billari and Prskawetz 2003), sociology (Macy and Flache 2009), organizational research (Fioretti 2013), archaeology (Wurzer, Kowarik, and Reschreiter 2015), ecology (Grimm et al. 2005), and biology (Thorne, Bailey, and Peirce 2007). But what is agent-based modeling about and why is it an important tool for social scientists? In order to isolate the defining characteristics of an agent-based model, it is useful to make a preliminary distinction between ‘‘statistical’’ and ‘‘theoretical’’ models (Skvoretz 1991), the former referring to models that are used for estimation purposes while the latter type of models seek to formally represent a process believed to have generated a specific type of outcome (see Fararo 1969; Boudon 1979; Sørensen 1998 for related distinctions). Agentbased models first and foremost are theoretical models. They simulate the

Causal mechanisms in organization and innovation studies

We outline the guiding ideas behind mechanisms-based theorizing in analytical sociology as a fruitful alternative to economics-inspired research on identification of causal effects, and discuss the potential of mechanisms-based theorizing for further development in organization and innovation studies. We discuss the realist stance on providing broader explanations as an identifying characteristic of the mechanism approach, its focus on the dynamic processes through which outcomes to be explained are brought about, and outline theoretical and methodological implications for organization and innovation studies.

Identifying Complex Dynamics in Social Systems A New Methodological Approach Applied to Study School Segregation

It is widely recognized that segregation processes are often the result of complex nonlinear dynamics. Empirical analyses of complex dynamics are however rare, because there is a lack of appropriate empirical modeling techniques that are capable of capturing complex patterns and nonlinearities. At the same time, we know that many social phenomena display nonlinearities. In this article, we introduce a new modeling tool in order to partly fill this void in the literature. Using data of all secondary schools in Stockholm county during the years 1990 to 2002, we demonstrate how the methodology can be applied to identify complex dynamic patterns like tipping points and multiple phase transitions with respect to segregation. We establish critical thresholds in schools’ ethnic compositions, in general, and in relation to various factors such as school quality and parents’ income, at which the schools are likely to tip and become increasingly segregated.

Analytical sociology and computational social science

Analytical sociology focuses on social interactions among individuals and the hard-to-predict aggregate outcomes they bring about. It seeks to identify generalizable mechanisms giving rise to emergent properties of social systems which, in turn, feed back on individual decision-making. This research program benefits from computational tools such as agent-based simulations, machine learning, and large-scale web experiments, and has considerable overlap with the nascent field of computational social science. By providing relevant analytical tools to rigorously address sociology’s core questions, computational social science has the potential to advance sociology in a similar way that the introduction of econometrics advanced economics during the last half century. Computational social scientists from computer science and physics often see as their main task to establish empirical regularities which they view as “social laws.” From the perspective of the social sciences, references to social laws appear unfounded and misplaced, however, and in this article we outline how analytical sociology, with its theory-grounded approach to computational social science, can help to move the field forward from mere descriptions and predictions to the explanation of social phenomena.