Social Dynamics

To inform their choices, people often rely on heuristics of social proof. This line of my research highlights the importance of social influence in the formation of individual preferences and the propagation of aggregate trends. Situations in which people react to an environment consisting of other individuals who are reacting likewise are prone to give rise to complex dynamics and hard-to-predict collective outcomes.

To study such phenomena, I focus on cultural markets because they lack almost entirely any objective standards of valuation. Consequently, market success often depends on social interactions signaling availability, quality, and the potential for shared experiences. Such environments provide valuable testing grounds for theories of socially-influenced behavior, diffusion, and cumulative advantage.

More recently, I have been increasingly engaged in computational social science. Sociologists can gain a lot from computational tools, particularly when it comes to the study of social dynamics. Computational social science might even have the potential to advance sociology in a way that the introduction of econometrics advanced economics during the last half century.

Against the notion of “mad crowds,” I am also interested in the advantages of groups over individuals in decision-making. Averaging over a collection of individual judgments, for example, proves a reliable strategy to aggregate information. Adding to the literature on the “wisdom of crowds,” I investigate the effects of specific aggregation rules and the consequences of social influence for collective judgment.

Product Success in Cultural Markets: The Mediating Role of Familiarity, Peers, and Experts.” Poetics 51:17-36.


The mediation of ambiguous markets has been essential to recent developments in economic sociology. Cultural industries provide a valuable testing ground for its perspective of socially influenced market behavior. 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. Mechanisms of valuation include product familiarity, peer influence, and expert critique. Focusing on an exemplary cultural market, I confront theoretical implications with data from the German book industry (2001–2006). Panel and cross-section regressions show that, alongside well-defined market segments, separate mechanisms guide consumer behavior. For incumbents’ offerings, prior recognition stabilizes cultural choice and reinforces differences in market success. 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.

logo_socfor fig1_sffig2_sffig3_sf

Genre assignments help audiences make sense of new releases. Studies from a wide range of market contexts have shown that generalists defying clear mapping to established categories suffer penalties in market legitimacy, perceived quality, or audience attention. Together with Thomas Wimmer I introduce an empirical strategy to disentangle two mechanisms, reduced niche fitness and audience confusion, causing devaluation or ignorance of boundary-crossing offers. Our data on 2,971 feature films released to US theaters and subsequently made available on DVD further reveal that consequences of category spanning are subject to strong moderating influences. 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.

Imitation und Konformität [Imitation and Conformity].” Pp. 903-934 in: Braun, N. and N.J. Saam (eds.) Handbuch Modellbildung und Simulation in den Sozialwissenschaften. Wiesbaden: Springer VS.

logo_hdbim1im2Ausgehend von einer Klassifikation unterschiedlicher Imitationsmotive werden in Abgrenzung zum diffusionstheoretischen Ansatz zwei Erklärungsmodelle von Konformität besprochen. Das Schwellenwertmodell führt die Übernahme von Verhaltensweisen Anderer auf die Existenz positiver Netzwerkeffekte zurück, wobei vollständig informierte Akteure die eigene Aktivierung von der Zahl bereits handelnder Populationsmitglieder abhängig machen. Demgegenüber begründet das Herdenmodell Imitation mit dem Wunsch nach Entscheidungssicherheit. Die Anpassung des eigenen Verhaltens an eine Mehrheitsentscheidung wird hier als Orientierungshilfe in Situationen mit unbekannten Handlungserträgen interpretiert. Die zugrundegelegten Interaktionsstrukturen implizieren soziale Dynamiken, deren Ergebnisse ohne Kenntnis einzelner Parameterwerte – wie der Verteilung individueller Schwellen oder der Reihenfolge empfangener Signale – nicht vorherzusagen sind. Dabei ermöglichen beide Prozesse die Verbreitung sozial wünschenswerter Neuerungen, sie können allerdings auch fehlgeleitete Konformität oder soziale Stagnation auslösen. Die theoretische Darstellung wird jeweils beispielhaften Ergebnissen aus Simulationsstudien gegenübergestellt. Abschließend wird die Brauchbarkeit des Herdenmodells zur Erklärung von Konformität in Finanzmärkten untersucht.

“Konformität durch Herdenverhalten: Theorie und Empirie zur Entstehung von Bestsellern [Conformity Through Herd Behavior: On the Emergence of Bestsellers].” Kölner Zeitschrift für Soziologie und Sozialpsychologie 64(1):1-36.

logo_kölner The adjustment of one’s own actions to the behavior of others offers an inexpensive alternative to self-reliant reasoning. Thus, from a sociological perspective conformity mirrors informative social influences. First, I present a basic model of social herding, which describes initial conditions and consequences of informational imitation. I then test central model implications against process-produced data from the German book market (2001–2006). While the release of public information and announcements by opinion leaders trigger conformity in a highly opaque market setting, these stimuli show no effects when the general availability of information is improved. Moreover, the availability of information among readers determines the quality of emerging bestsellers. Altogether, the model of herd behavior proves useful in understanding demand processes in book markets.

Das Bestseller-Phänomen: Die Entstehung von Nachfragekonzentration im Buchmarkt [The Bestseller Phenomenon: Demand Concentration in the Book Market]. Wiesbaden: VS Springer.

logo_diss diss1diss3diss2

Konzentration der Nachfrage erzeugt in einigen Märkten extreme Erfolgsungleichheiten, dazu zählen insbesondere Kulturmärkte. In meiner Dissertation untersuche ich Konsumentenverhalten im Buchmarkt und führe extreme Marktergebnisse mit vielen Misserfolgen und einigen Bestsellern auf Konformität unter Käufern zurück. Die präsentierten Erklärungsmodelle und deren empirische Prüfung erlauben Rückschlüsse auf die Entstehungsweise von Bestsellern und geben Hinweise auf Entscheidungshilfen, die von Konsumenten in einem Umfeld geringer Markttransparenz genutzt werden. Diese Forschungsarbeit trägt zum Verständnis der Konsequenzen von Informationsasymmetrien für Marktergebnisse bei und bezieht sich dabei nicht allein auf Nachfrageprozesse im Buchmarkt. Book Review

Analytical Sociology and Computational Social Science.” Journal of Computational Social Science, Online First.

logo_jcss In this rather programmatic essay, me, Niclas Lovsjö, and Peter Hedström carve out the similarities and differences between analytical sociology (AS) and computational social science (CSS). 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 such as agent-based simulations, machine learning, and large-scale web experiments, and has considerable overlap with the nascent field of 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. Practicioners of CSS 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 AS, with its theory-grounded approach to CSS, can help to move the field forward from mere descriptions and predictions to the explanation of social phenomena.

Crowd Wisdom Relies on Agents’ Ability in Small Groups with a Voting Aggregation Rule.” Management Science 63(3):818–828.

 logo_ms   ms1  ms2  ms3

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. Together with Christian Ganser I 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 approximately 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.