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.
Pay what you want (PWYW) is an increasingly popular sales strategy in which consumers voluntarily decide how much to pay for a product or service. PWYW has often been described as an exercise in the "empathy economy," where consumers' payment choices might be seen as empowered expressions of their tastes and preferences, and sellers have a stronger incentive for empathizing with them. Beyond their economic interest, PWYW experiences also deserve significant attention in the social sciences given that they challenge several key assumptions of rational choice and neoclassical economic theory, as well as conventional consumer behavior and pricing theories. This paper analyzes three plays performed at the Beckett Theater in Barcelona using PWYW with very profitable outcomes. Our analysis shows that socio-psychological factors, such as payments attributed to others and satisfaction with the play, are the best predictors of customer payments.
Opinion dynamics models usually center on explaining how macro-level regularities in public opinion (uniformity, polarization or clusterization) emerge as the e ect of local interactions of a population with an initial random distribution of opinions. However, with only a few exceptions, the understanding of patterns of public opinion change has generally been dismissed in this literature. To address this theoretical gap in our understanding of opinion dynamics, we built a multi-agent simulation model that could help to identify some mechanisms underlying changes in public opinion. Our goal was to build a model whose behavior could show di erent types of endogenously (not induced by the researcher) triggered transitions (rapid or slow, radical or so). The paper formalizes a situation where agents embedded in di erent types of networks (random, small world and scale free networks) interact with their neighbors and express an opinion that is the result of di erent mechanisms: a coherence mechanism, in which agents try to stick to their previously expressed opinions; an assessment mechanism, in which agents consider available external information on the topic; and a social influence mechanism, in which agents tend to approach their neighbor's opinions. According to our findings, only scale-free networks show fluctuations in public opinion. Public opinion changes in this model appear as a di usion process of individual opinion shi s that is triggered by an opinion change of a highly connected agent. The frequency, rapidity and radicalness of the di usion, and hence of public opinion fluctuations, positively depends on how influential external information is in individual opinions and negatively depends on how homophilic social interactions are.
Analytical sociology is a set of rules for the construction of causal explanations in the social sciences. In this article, I critically assess the value and evolution of this ‘syntax’ for explanation and the concept of social mechanisms on which it relies. I also offer a proposal on how to reform and expand the ideal-typical analytical research path. In short, my proposal is characterized by (a) a generative conception of explanation; (b) a conception of social mechanisms as causal chains of micro-level (that is, individual) events; (c) a denial of downward and upward causation; and (d) a focus on testing the generative sufficiency and describing the generative processes of empirically calibrated agent-based models.
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