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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]

Recommendation systems are an increasingly prominent part of the web, accounting for up to a third of all traffic on several of the world's most popular sites. Nevertheless, little is known about how much activity such systems actually cause over and above activity that would have occurred via other means (e.g., search) if recommendations were absent. Although the ideal way to estimate the causal impact of recommendations is via randomized experiments, such experiments are costly and may inconvenience users. In this paper, therefore, we present a method for estimating causal effects from purely observational data. Specifically, we show that causal identification through an instrumental variable is possible when a product experiences an instantaneous shock in direct traffic and the products recommended next to it do not. We then apply our method to browsing logs containing anonymized activity for 2.1 million users on Amazon.com over a 9 month period and analyze over 4,000 unique products that experience such shocks. We find that although recommendation click-throughs do account for a large fraction of traffic among these products, at least 75% of this activity would likely occur in the absence of recommendations. We conclude with a discussion about the assumptions under which the method is appropriate and caveats around extrapolating results to other products, sites, or settings. [ABSTRACT FROM AUTHOR]

Cooperation in repeated games has been widely studied in experimental settings; however, the duration over which players participate in such experiments is typically confined to at most hours, and often to a single game. Given that in real world settings people may have years of experience, it is natural to ask how behavior in cooperative games evolves over the long run. Here we analyze behavioral data from three distinct games involving 571 individual experiments conducted over a two-year interval. First, in the case of a standard linear public goods game we show that as players gain experience, they become less generous both on average and in particular towards the end of each game. Second, we analyze a multiplayer prisoner's dilemma where players are also allowed to make and break ties with their neighbors, finding that experienced players show an increase in cooperativeness early on in the game, but exhibit sharper "endgame" effects. Third, and finally, we analyze a collaborative search game in which players can choose to act selfishly or cooperatively, finding again that experienced players exhibit more cooperative behavior as well as sharper endgame effects. Together these results show consistent evidence of long-run learning, but also highlight directions for future theoretical work that may account for the observed direction and magnitude of the effects. [ABSTRACT FROM AUTHOR]

The article discusses a study of empirical agent based modeling (EABM) of cooperation in public goods games. It mentions that agent-based models (ABMs), also called individual-based models or artificial adaptive agents, are popular method to investigate the dynamics of human interactions but rarely for observations of actual human behavior. In addition, the study examined data from an experimental setting where more than 150 human players played in a series of public goods games.

Models of networked diffusion that are motivated by analogy with the spread of infectious disease have been applied to a wide range of social and economic adoption processes, including those related to new products, ideas, norms and behaviors. However, it is unknown how accurately these models account for the empirical structure of diffusion over networks. Here we describe the diffusion patterns arising from seven online domains, ranging from communications platforms to networked games to microblogging services, each involving distinct types of content and modes of sharing. We find strikingly similar patterns across all domains. In particular, the vast majority of cascades are small, and are described by a handful of simple tree structures that terminate within one degree of an initial adopting "seed." In addition we find that structures other than these account for only a tiny fraction of total adoptions; that is, adoptions resulting from chains of referrals are extremely rare. Finally, even for the largest cascades that we observe, we find that the bulk of adoptions often takes place within one degree of a few dominant individuals. Together, these observations suggest new directions for modeling of online adoption processes. [ABSTRACT FROM AUTHOR]

Citing recent successes in forecasting elections, movies, products, and other outcomes, prediction market advocates call for widespread use of market-based methods for government and corporate decision making. Though theoretical and empirical evidence suggests that markets do often outperform alternative mechanisms, less attention has been paid to the magnitude of improvement. Here we compare the performance of prediction markets to conventional methods of prediction, namely polls and statistical models. Examining thousands of sporting and movie events, we find that the relative advantage of prediction markets is surprisingly small, as measured by squared error, calibration, and discrimination. Moreover, these domains also exhibit remarkably steep diminishing returns to information, with nearly all the predictive power captured by only two or three parameters. As policy makers consider adoption of prediction markets, costs should be weighed against potentially modest benefits. [ABSTRACT FROM AUTHOR]

Cultural objects, like movies, books, and music, vary greatly in their success suggesting large inherent differences between objects. Yet, paradoxically, success in cultural markets remains unpredictable. In this paper we experimentally explore the possibility of self-fulfilling prophecies in cultural markets. We constructed a website where subjects (n = 9,996) could listen to, and if they chose, download new pop songs. By controlling the information that subjects received about the behavior of others, we can directly measure the effect of false beliefs about the popularity of the songs on subsequent success. We find that the perception of success does lead to future success and that, in some cases, the process can become self-sustaining. That is, distortions in perceived popularity, whether random or intentional, can lock-in and become stable. In other cases, these distortions fade away and do not affect long-term system behavior. ..PAT.-Unpublished Manuscript [ABSTRACT FROM AUTHOR]

Many cultural markets are characterized by both extreme variation in success and extreme unpredictability. These two apparently contradictory macro-level characteristics can be shown to derive from a common micro-level mechanism. A simple model of these markets is developed and then tested with a novel web-based experiment. The unique experimental design allows us to estimate how much the success of an object is related to its inherent appeal and how much is related to chance and cumulative advantage. Data generated by 7,209 participants supports the theories presented which argue that the success of objects is largely determined by things other than the appeal of the object. Implications for the sociology of culture as well as social science in general are discussed. [ABSTRACT FROM AUTHOR]

The natural tendency for humans to choose with whom to form new relationships and with whom to end established relationships is thought to facilitate the emergence of cooperation. Helping cooperators to mix assortatively is believed to reinforce the rewards accruing to mutual cooperation while simultaneously excluding defectors. However, the relationship between endogenous partner selection, assortativity, and cooperation has been largely unexplored experimentally. Here we report on a series of human subjects experiments in which groups of 24 participants played a multi-player prisoner's dilemma game where, critically, they were also allowed to propose and delete links to players of their own choosing at some variable rate. Over a wide variety of parameter settings and initial conditions, we found that endogenous partner selection significantly increased the level of cooperation, the average payoffs to players, and the assortativity between cooperators. Even relatively slow update rates were sufficient to produce large effects resulting in cooperation levels over 80%. Subsequent increases to the update rate still had a positive, although smaller, effect. For standard prisoner's dilemma payoffs, we also found that assortativity resulted predominantly from cooperators avoiding defectors, not by severing ties with defecting partners, and that cooperation correspondingly suffered. Finally, by modifying the payoffs to satisfy two novel conditions, we found that cooperators did punish defectors by severing ties, leading to levels of cooperation approaching 100% which persisted for longer. [ABSTRACT FROM AUTHOR]