To inform their choices, people often rely on heuristics of social proof. Situations in which people react to an environment consisting of other individuals who are reacting likewise give rise to complex dynamics and hard-to-predict collective outcomes.
I like to study such phenomena in cultural markets because they lack almost entirely any objective standards of valuation. Against the notion of “mad crowds,” I am also interested in “crowd wisdom”—the advantages of groups over individuals in decision-making.
“Product Success in Cultural Markets: The Mediating Role of Familiarity, Peers, and Experts.” Poetics 51:17-36.
Cultural markets provide a valuable testing ground for socially influenced 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. Important 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.
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. This article introduces an empirical strategy to disentangle two mechanisms—reduced niche fitness and audience confusion—causing devaluation 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.
“Analytical Sociology and Computational Social Science.” Journal of Computational Social Science 1(1):3-14.
In this programmatic essay, we 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.
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.
“Social Influence Strengthens Crowd Wisdom Under Voting.” Advances in Complex Systems 21(6):1850013.
In our follow-up on crowd wisdom we demonstrate, again using agent-based simulations, that under voting social influence contributes to information aggregation and thus strengthens collective judgment. This result stands in stark contrast to the 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. Understanding the consequences of social learning through these types of studies holds important ramifications for the design of collective decision-making in both public administration and private firms.