The emergence of group constructs is an unfolding process, whereby actions and interactions coalesce into collective psychological states. Implicitly, there is a connection between these states and the underlying procession of events. The manner in which interactions follow one another over time describe a group's behavior, with different temporal patterns being indicative of different team characteristics. In this study, we explicitly connect event sequences to the process of emergence. We argue that the temporal relationship between events in a sequence will vary depending on the team's psychological outcome. Further, certain patterns of behavior will be repeated at different rates in teams with varying emergent states. To support this approach, we apply a statistical methodology-relational event modeling-for analyzing sequences of interactions that builds on the foundation of social network analysis. Using a dataset comprised of 55 work teams of military personnel engaged in a tactical scenario, we found that individuals who perceived team process (regarding coordination and information sharing) as having different qualities engaged in significantly different patterns of behavior. Our findings indicate that individuals who had a positive perception of process quality were more likely to initiate communication events in a reciprocal, transitive, and decentralized fashion.

A fundamental assumption in the study of groups is that they are constituted by various interaction processes that are critical to survival, success, and failure. However, there are few methods available sophisticated enough to empirically analyze group interaction. To address this issue, we present an illustration of relational event modeling (REM). A relational event is a "discrete event generated by a social actor and directed toward 1 or more targets" (Butts, 2008, p. 159). Because REM provides a procedure to model relational event histories, it has the ability to figure out which patterns of group interaction are more or less common than others. For instance, do past patterns of interaction influence future interactions, (e.g., reciprocity), do individual attributes make it more likely that individuals will create interactions (e.g., homophily), and do specific contextual factors influence interaction patterns (e.g., complexity of a task)?The current paper provides an REM tutorial from a multiteam system experiment in which 2 teams navigated a terrain to coordinate their movement to arrive at a common destination point. We use REM to model the dominant patterns of interactions, which included the principle of inertia (i.e., past contacts tended to be future contacts) and trust (i.e., group members interacted with members they trusted more) in the current example. An online appendix that includes the example data set and source code is available as supplemental material in order to demonstrate the utility REM, which mainly lies in its ability to model rich, time-stamped trace data without severely simplifying it (e.g., aggregating interactions into a panel).

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Algorithms have begun to encroach on tasks traditionally reserved for human judgment and are increasingly capable of performing well in novel, difficult tasks. At the same time, social influence, through social media, online reviews, or personal networks, is one of the most potent forces affecting individual decision-making. In three preregistered online experiments, we found that people rely more on algorithmic advice relative to social influence as tasks become more difficult. All three experiments focused on an intellective task with a correct answer and found that subjects relied more on algorithmic advice as difficulty increased. This effect persisted even after controlling for the quality of the advice, the numeracy and accuracy of the subjects, and whether subjects were exposed to only one source of advice, or both sources. Subjects also tended to more strongly disregard inaccurate advice labeled as algorithmic compared to equally inaccurate advice labeled as coming from a crowd of peers.

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