Search

Hypothetical scenarios provide a popular alternative to field experiments for scholars interested in nudging behavior change, comprising a substantial proportion of such studies in the domains of finance, transportation, and sustainability. Yet their validity as proxies for real-world contexts is unclear. To investigate, we designed four styles of hypothetical scenarios to approximate five recent field studies of nudges in distinct domains, running a total of 20 pre-registered experiments (N=16,071, n>200 per cell). This design allows clear comparison of old field data with new hypothetical data. We find that hypothetical outcomes are consistently biased upwards – participants engage more in target behaviors by a median factor of 3.81 compared to the original field experiment – while their estimations of treatment effects are unpredictable: sometimes bigger, sometimes smaller, sometimes calibrated. Further, none of our four hypothetical designs reliably reduced estimation error. Without a gold standard approach to constructing hypothetical scenarios, behavioral researchers and practitioners should use caution when employing this low-cost but unreliable tool to evaluate nudge interventions. [ABSTRACT FROM AUTHOR]

The goal of this symposium is to connect theorists, methodologists, and technologists at the interdisciplinary frontier of team collaboration and conflict management studies. Specifically, this symposium weaves together technology's dual implications on both teams and the science of teamwork. Across four original research papers, we will demonstrate that studying teams in a digital setting is more than simply a recreation of in-person interactions, but rather a rich setting for methodological innovation. These innovations, in turn, push the boundaries of our knowledge about teamwork, particularly in a world in which collaboration increasingly occurs via technology. We call this bidirectional interplay between methods and theory a "computational science of collaboration." Paper 1 introduces a model designed to capture the dynamics of naturalistic turn-taking, showing how fine-grained data can help to bridge the gap between micro-level turn dynamics and broader macro-level outcomes. Paper 2 further expands on micro-level dynamics to explore how conflict dynamics differ across communication media, with implications for facilitating constructive disagreements in a digitized yet polarized world. Paper 3 presents the results of an online experiment that systematically varies five facets of teamwork (Team Composition, Team Size, Task Attributes, Task Complexity, and Communication Process). Its findings show that, all else equal, team outcomes are highly dependent on the task at hand. Lastly, Paper 4 demonstrates how researchers can take advantage of the latest advances in Large Language Models to build interactive agents for behavioral experiments. Following the four presentations, discussants Laurie Weingart and Randall Peterson will lead a conversation integrating the papers' theoretical and methodological contributions. In what ways do these novel tools extend prior theories of teamwork, perhaps with greater precision or resolution, and in what ways do they highlight novel forms of collaboration — whether across different modalities, different tasks, or different types of "teammates" (AI-powered versus human)? How might this "computational science" influence our field's research agenda in the years to come? Interloper: A Model to Segment Transcripts into Naturalistic Conversational Turns Author: Gus Cooney; The Wharton School, U. of Pennsylvania Speaking of Receptiveness: How Spoken Conversations Facilitate Constructive Disagreement Author: Juliana Schroeder; U. of California, Berkeley Author: Burint Bevis; Imperial College Business School Recipe for Success: Quantifying the Relative Influence of Teamwork Facets on Performance Author: Xinlan Emily Hu; Wharton Author: Abdullah Almaatouq; Massachusetts Institute of Technology Author: Mark Whiting; U. of Pennsylvania Author: Duncan J. Watts; U. of Pennsylvania Generative Agents and Confederates: Promise, Peril, and Application to Online Experiments Author: Mohammed Alsobay; MIT Sloan School of Management [ABSTRACT FROM AUTHOR]