The study of how cultural items diffuse in a population, how cultural meanings and (mis)information spread in social networks, and how shared interpretations of issues and events evolve and change over time greatly benefits from a “computational turn” in sociology. Newly available population-wide networked digital trace data, archives of digitized text, and the computational tools for their analysis help to address some of our discipline’s core questions in novel and rigorous ways.
My work on cultural dynamics builds on the premise that whenever people inform their choices by observing others they react to an environment consisting of others who are prone to reacting likewise. This social interdependency can give rise to hard-to-predict outcomes on the aggregate level that defy a 1:1 mapping to the individual level, and thus cannot be explained by reference to the characteristics and intentions of the individuals involved.
Further, social learning can improve decision-making on the individual level, but it bears the risk of herd behavior undermining the wisdom of crowds.
“Network Segregation and the Propagation of Misinformation.” Scientific Reports 13(1):917.
How do ideologically aligned networks impact the spread of misinformation? We created 16 independent online ecosystems in which participants could share true and false messages about society, science, and politics. The experiment creates “multiple worlds,” in which messages can be shared among individuals that are connected through network ties. The setup resembles many online platforms, and it allows us to vary network architecture. We recruited US participants. Half of the experimental networks were integrated such that contacts were 50:50 liberals and conservatives. In the other half, networks were segregated, such that contacts shared similar political views. This macrosociological experiment reveals that partisan sorting systematically undermines the veracity of information circulating. Agent-based simulations models show robustness of this finding across different network topologies and sizes.
“Success in Cultural Markets: The Mediating Role of Familiarity, Peers, and Experts.” Poetics 51:17-36.
This article emphasizes the uncertainty of cultural markets and thus the relevance of social valuation in disseminating new releases. I hypothesize that recipients of culture simplify cultural choice by reacting to easily attainable signals of product value. Sales data from the German book industry (2001–2006) show that, in the highly ambiguous newcomer segment, imitation and negative media steer audience attention, at times leading to unsatisfactory aggregate outcomes, i.e. ‘bad’ bestsellers. This paper received the Karl-Polanyi-Award in 2016.
“Is Category Spanning Truly Disadvantageous? New Evidence from Primary and Secondary Movie Markets.” Social Forces 96(1):449–479.
Genre assignments help audiences make sense of new releases. We empirically disentangle two mechanisms—reduced niche fitness and audience confusion—causing devaluation of category-spanning offers. Our data (2,971 feature films released to US theaters and, subsequently, on DVD) reveal strong moderating influences on categorization effects. Negative effects are far from universal, manifesting only if (a) combined genres are culturally distant, (b) products are released to a stable and highly institutionalized market context, and (c) offers lack familiarity as an alternative source of market recognition. Our study provides ramifications as to the scope conditions of categorization effects and modifies some widely acknowledged truisms regarding boundary crossing in cultural markets.
“Analytical Sociology and Computational Social Science.” Journal of Computational Social Science 1(1):3-14.
Analytical sociology (AS) 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 (e.g. agent-based simulations, machine learning, large-scale web experiments), and has considerable overlap with the nascent field of computational social science (CSS). By providing relevant analytical tools to rigorously address sociology’s core questions, CSS has the potential to advance sociology in a similar way that the introduction of econometrics advanced economics during the last half century. We outline how the theory-grounded approach of AS can help to move the field forward from mere descriptions and predictions to the explanation of social phenomena.
Here, you find a collection of our chapters and papers on the intersection of AS and CSS and the computational turn in sociology.
“Crowd Wisdom Relies on Agents’ Ability in Small Groups with a Voting Aggregation Rule.” Management Science 63(3):818–828.
“Crowd wisdom” relies on the aggregation of independent judgments, and group accuracy 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. In groups smaller than 16 members, however, diversity is key only in continuous estimation tasks (averaging), and much less so in discrete choice tasks (voting). For ubiquitous voting, agents’ abilities remain crucial. The effects of group composition depend on the social decision function, and strategies to improve collective decision making must adapt to the predictive situation at hand.
“Social Influence Strengthens Crowd Wisdom Under Voting.” Advances in Complex Systems 21(6):1850013.
Our follow-up on crowd wisdom demonstrates that, under voting, social influence contributes to information aggregation and thus strengthens collective judgment. This result stands in stark contrast to the reported effects of social influence on averaging—the highly researched yet infrequently used social decision rule—for which many have said that social influence narrows the variation in individual opinions and thus undermines crowd-wisdom effects. These kinds of agent-based simulation studies can hold important ramifications for the design of collective decision-making in both public administration and private firms.