Progress in modeling dynamic systems for sustainable development

This Perspective evaluates recent progress in modeling nature-society systems to inform sustainable development. We argue that recent work has begun to address longstanding and often-cited challenges in bringing modeling to bear on problems of sustainable development. For each of four stages of modeling practice-defining purpose, selecting components, analyzing interactions, and assessing interventions-we highlight examples of dynamical modeling methods and advances in their application that have improved understanding and begun to inform action. Because many of these methods and associated advances have focused on particular sectors and places, their potential to inform key open questions in the field of sustainability science is often underappreciated. We discuss how application of such methods helps researchers interested in harnessing insights into specific sectors and locations to address human well-being, focus on sustainability-relevant timescales, and attend to power differentials among actors. In parallel, application of these modeling methods is helping to advance theory of nature-society systems by enhancing the uptake and utility of frameworks, clarifying key concepts through more rigorous definitions, and informing development of archetypes that can assist hypothesis development and testing. We conclude by suggesting ways to further leverage emerging modeling methods in the context of sustainability science.

Group-level physiological synchrony and individual-level anxiety predict positive affective behaviors during a group decision-making task

Joint performance can lead to the synchronization of physiological processes among group members during a shared task. Recently, it has been shown that synchronization is indicative of subjective ratings of group processes and task performance. However, different methods have been used to quantify synchronization, and little is known about the effects of the choice of method and level of analysis (individuals, dyads, or triads) on the results. In this study, participants performed a decision‐making task in groups of three while physiological signals (heart rate and electrodermal activity), positive affective behavior, and personality traits were measured. First, we investigated the effects of different levels of analysis of physiological synchrony on affective behavior. We computed synchrony measures as (a) individual contributions to group synchrony, (b) the average dyadic synchrony within a group, and (c) group‐level synchrony. Second, we assessed the association between physiological synchrony and positive affective behavior. Third, we investigated the moderating effects of trait anxiety and social phobia on behavior. We discovered that the effects of physiological synchrony on positive affective behavior were particularly strong at the group level but nonsignificant at the individual and dyadic levels. Moreover, we found that heart rate and electrodermal synchronization showed opposite effects on group members' display of affective behavior. Finally, trait anxiety moderated the relationship between physiological synchrony and affective behavior, perhaps due to social uncertainty, while social phobia did not have a moderating effect. We discuss these results regarding the role of different physiological signals and task demands during joint action.

Impact Statement

Despite the inherent multilevel structure of groups, little is known regarding how physiological coupling between group members relates to their behaviors during joint group tasks at multiple levels (individual, dyadic, and group). We showed that the relationship between physiological synchrony and smiling/laughing behaviors made by group members were particularly strong at the group level but nonsignificant at the individual and dyadic levels. By using innovative quantification methods—Multidimensional Recurrence Quantification Analysis—we highlight the importance of modeling data in a way that allows for multilevel considerations within groups.

Multilevel Methods and Statistics: The Next Frontier

The purpose of this article is to take stock of extant multilevel methodological and statistical work and highlight needed areas for future research. A basic overview of the history and progression of multilevel methods and statistics in the organizational sciences is provided, as well as a discussion of recent developments to summarize the current state of the science. The eight articles in the current feature topic are also summarized and integrated to depict several themes and directions for the next wave of multilevel methods and statistics. Last, to highlight what still needs to be accomplished in the field, several unresolved issues and future research topics are noted and an agenda related to future multilevel work is discussed.