PDF Handbook of Historical Sociology

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New Password. Password Changed Successfully Your password has been changed. Powerful computers and simulation software have changed the picture - so much so that it is possible to have some confidence that agent-based computer simulations will transform important parts of sociological theory because they allow for rigorous theoretical analyses of large and complex social systems.

All the concepts they use are the same as those used in social simulation. The second part consists of eight chapters which are the social cogs and wheels of analytical sociology. Some of them are focused on individual aspects, such as emotions, beliefs, preferences, opportunities, and heuristics.

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Others are concerned with relational aspects, such as signalling, norms and trust. Taken together, they are a comprehensive analysis of the micro-foundations of sociological models. I particularly enjoyed the first three chapters, by Jon Elster on emotions, Jens Rydgren on beliefs and Jeremy Freese on preferences. Although written by different authors, with the associated danger of incoherence and redundancy, they complement each other and share basically the same theoretical framework.

The chapter on heuristics by Daniel G. Goldstein is of paramount importance for social simulation, since it shows the potential of combining lab experiments and social simulation to investigate sociological aspects of individual action at their best. The chapter on signalling by Gambetta is a brilliant example of sociological analysis with empirical contents. Elster provides a subtle framework to distinguish between social and moral norms, suggesting a non-instrumental view on social norms with a lot of followers also in social simulation. Finally, Karen S.

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Cook and Alexandra Gerbasi develop an excursus on different social mechanisms than can produce cooperation and social order other than trust. The third part deals with social dynamics that are the bread and butter of social simulation. Some chapters emphasize agent-based modelling and social networks.

Michael Macy and Andreas Flache, two notable social simulation exponents, provide a complete chapter on agent-based models of social interaction. They emphasize the peculiarity of agent-based modelling in respect to game theory, equation-based models and statistical linear models. In doing so, they also illustrate some of the limitations of agent-based modelling, i. This recalls Coleman's position that a sociologist's mission is to tackle complex social patterns not complex cognitive aspects. Pragmatic reasoning leads sociologists to think that it is reasonable not to be too complex in behavioural assumptions of their models, so as to concentrate on concrete sociological aspects.

This sounds absolutely reasonable if one keeps the range of sociological interest within a 'pure' analytical level, as in this book and as many sociologists do. However, if one thinks that social sciences should also deal with policy issues so as to help policy makers understand and solve contingent empirical problems, as this policy mission is important for the social prestige of the discipline, this position must be questioned.

In most cases, scientists making policy models or applied science are requested to get their hands dirty with details, empirical richness, and micro aspects. The consequence is that models become significantly more complex than analytical abstractions. Of course, this does not mean that we should throw in the kitchen sink, but that a more vivid representation of empirical reality at an agent or social structure level is needed for policy models.


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The other chapters of this third part are full of examples of social mechanisms. Brent Lindquist on dominance hierarchies and Stathis Kalyvas on conflict. I particularly enjoyed the chapters by Matthew J. Salganik and Duncan J.

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Watts on social influence in cultural markets, by Delia Baldassarri on collective action, and by Katherine Stovel and Christine Fountain on matching. Salganik and Watts provide a brilliant example of how to use the web to conduct mixed experiments with real agents in virtual social settings. This sounds to me as an example of a creative non-standard lab experiment which sociologists will have more to do with in the future. The authors provide a simple and effective explanation of the emergence of a broad range of successful cultural products, such as bestselling books, hit songs, blockbuster movies and all the next 'big thing' that are impossible to predict, by experimentally studying social influence mechanisms and self-fulfilling prophecies.

Katherine Stovel and Christine Fountain focus on matching in competitive conditions, by introducing an agent-based model that illustrates the relevance of social networks for the emergence of labour-market segregation.


Delia Baldassarri points out certain limitations of standard models of collective action that are also interesting for social simulation, such as the underestimation of the relevance of organized groups, of the heterogeneity of their size and composition and of the agent consciousness about the jointed collective nature of public goods. In particular, the discussion on this last aspect will positively inspire socio-cognitive scientists doing social simulation.