Data, generative models, and mechanisms: More on the principles of analytical sociology

Manzo, Gianluca

doi:10.1002/9781118762707.ch01

Cited by 38 publications

(76 citation statements)

References 177 publications

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“…The model is based on theoretical concepts from social norms theory [4], in which the modeler has used the theory abductively as a framework for interpretation of the temporal changes in alcohol use patterns in American society [2]. The conceptual design of the model uses the analytical sociology framework [18], in which the sequence of mechanisms that generate the dynamics of macro-level patterns involve, for every mechanism, the dispositions and behaviors of individuals. Empirical-based falsification is also a key part of the framework -with mechanism-based explanations only remaining as candidates if they are able to sufficiently reproduce observations.…”

Section: An Individual-level Model Of Social Norms 21 Overviewmentioning

confidence: 99%

Toward inverse generative social science using multi-objective genetic programming

Probst

Epstein

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference

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Generative mechanism-based models of social systems, such as those represented by agent-based simulations, require that intraagent equations (or rules) be specified. However there are often many different choices available for specifying these equations, which can still be interpreted as falling within a particular class of mechanisms. Whilst it is important for a generative model to reproduce historically observed dynamics, it is also important for the model to be theoretically enlightening. Genetic programs (our own included) often produce concatenations that are highly predictive but are complex and hard to interpret theoretically. Here, we develop a new method -based on multi-objective genetic programming -for automating the exploration of both objectives simultaneously. We demonstrate the method by evolving the equations for an existing agent-based simulation of alcohol use behaviors based on social norms theory, the initial model structure for which was developed by a team of human modelers. We discover a trade-off between empirical fit and theoretical interpretability that offers insight into the social norms processes that influence the change and stasis in alcohol use behaviors over time.

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Section: An Individual-level Model Of Social Norms 21 Overviewmentioning

confidence: 99%

Toward inverse generative social science using multi-objective genetic programming

Probst

Epstein

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference

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“…The use of a mechanisms-based approach to explanation can be traced back to the seventeenth century, but it was not used in sociology until after World War Two (Hedström and Swedberg 1996). In the past two decades, AS has put this concept in the center of its “syntax” for explanation (Hedström 2005; Hedström and Swedberg 1996, 1998; Hedström and Ylikoski 2010; Manzo 2014b). Scholars have offered a myriad of definitions of mechanisms (for compilations, see e.g., Gross 2009; Hedström and Bearman 2009b), but all point to the epistemological necessity of finding the microfoundations of the sociological explananda.…”

Section: Delimiting the Concept Of Social Mechanismmentioning

confidence: 99%

Analytical Sociology and Agent-Based Modeling: Is Generative Sufficiency Sufficient?

León-Medina

2017

Sociological Theory

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Building mechanisms-based, black box–free explanations is the main goal of analytical sociology. In this article, I offer some reasons to question whether some of the conceptual and methodological developments of the analytical community really serve this goal. Specifically, I argue that grounding our computer modeling practices in the current definition of mechanisms posits a serious risk of defining an ideal-typical research path that neglects the role that the understanding of the generative process must have for a black box–free explanation to be met. I propose some conceptual and methodological alternatives, and I identify some collective challenges that the analytical community should tackle in order not to deviate from its main goal.

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“…Axelrod took the perspective of agent-based computational modeling (e.g., Epstein 1999;Hedström and Manzo 2015;Macy et al 2011;Macy and Flache 2009;Smith and Conrey 2007), an approach that puts emphasis upon unraveling the complex interplay between simple Bfirst principles ( Mark 1998) of interpersonal social interaction and emergent societal outcomes that often can be unexpected and unintended from individuals' point of view. Agentbased computational modeling (ABCM) is closely linked to methodological individualism and the social-mechanism approach to social theory (Coleman 1990;Hedström 2005;Manzo 2014), which views macro-phenomena (like the pattern of local convergence and global diversity observed in many cultural phenomena) as the result of the actions of the individuals that constitute society at the micro-level. ABCM in particular implements the rules of behavior and interaction in computer code, and conducts systematic computational experiments to derive how societal outcomes could result from these micro-level processes.…”

Section: Introductionmentioning

confidence: 99%

Between Monoculture and Cultural Polarization: Agent-based Models of the Interplay of Social Influence and Cultural Diversity

Flache

2018

J Archaeol Method Theory

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Social influence is one of the most important processes in human social interaction. Very often in human social interaction, influence is assimilative in that individuals become more similar to others they interact with. Nevertheless, cultural differences continue to remain in many realms of human life, for example, in the form of technological boundaries. Research on social influence points to a range of possible reasons for persistent cultural diversity, but there is much less clarity about the interplay of various factors and conditions for cultural influence with fundamental processes of social interaction at the micro-level. In this article, I show how agentbased computational modeling can be used as an approach for unraveling the complex interplay between simple first principles of interpersonal social interaction and emergent societal outcomes. I give a brief overview illustrating some of the main approaches agent-based modelers have developed in recent decades to understand conditions and processes of the emergence of cultural diversity. Models will be discussed that generate mainly cultural consensus as long-term behavior, but also models that generate clustering of cultural attitudes in geographical or social space, and models that imply cultural polarization with sharp cultural boundaries between emergent factions. It will be discussed how model dynamics depend on further assumptions, for example about random events, or the scaling of cultural attitudes, and what are further developments in the literature, possible future directions and challenges for the application of computational agent-based modeling in archeological research on cultural boundaries.

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Data, generative models, and mechanisms: More on the principles of analytical sociology

Cited by 38 publications

References 177 publications

Toward inverse generative social science using multi-objective genetic programming

Toward inverse generative social science using multi-objective genetic programming

Analytical Sociology and Agent-Based Modeling: Is Generative Sufficiency Sufficient?

Between Monoculture and Cultural Polarization: Agent-based Models of the Interplay of Social Influence and Cultural Diversity

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