No abstract

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.

Population characteristics and selective migration explain around half of the previously reported superlinear urban scaling.

Adding to the debate about the "broken windows" thesis we discuss an explanation of minor norm violation based on the assumption that individuals infer expected sanctioning probabilities from contextual cues. We modify the classical framework of rational crime by signals of disorder, local social control, and their interaction. Testing our implications we present results from three field experiments showing that violations of norms, which prevent physical as well as social disorder, foster further violations of the same and of different norms. Varying the net gains from deviance it shows that disorder effects are limited to low cost situations. Moreover, we provide suggestive evidence that disorder effects are significantly stronger in neighborhoods with high social capital.

In the last decade interest in the "wisdom of crowds" effect has gained momentum in both organizational research and corporate practice. Crowd wisdom relies on the aggregation of independent judgments. The accuracy of a group's aggregate prediction rises with the number, ability, and diversity of its members. We investigate these variables' relative importance for collective prediction using agent-based simulation. We replicate the "diversity trumps ability" proposition for large groups, showing that samples of heterogeneous agents outperform same-sized homogeneous teams of high ability. In groups smaller than about 16 members, however, the effects of group composition depend on the social decision function employed: Diversity is key only in continuous estimation tasks (averaging) and much less important in discrete choice tasks (voting), in which agents' individual abilities remain crucial. Thus, strategies to improve collective decision-making must adapt to the predictive situation at hand.