A dynamic systems theory of intrateam conflict contagion.

Recognizing the challenges that conflict poses, organizational researchers have invested considerable energy toward investigating the processes by which conflict occurs and spreads within a team. However, current theoretical frameworks of conflict contagion posit a static growth trajectory in which members become engaged in conflict and stay in conflict. While this trajectory is certainly possible, the broader conflict literature outside of the organizational sciences has shown evidence for a more varied set of potential trajectories of conflict contagion. To advance theory on team conflict, we integrate conflict research from micro-level (interpersonal) to macro-level (interstate) perspectives into a formal theory of intrateam conflict contagion. Drawing from conflict stage and social contagion theory, we theorize that team members move through three stages of conflict (disengaged, at-risk, engaged) at rates determined by four process mechanisms (faultlines, forgiveness, frustration, integration) such that disengaged individuals become at-risk of engaging in conflict, engage in conflict, then disengage, only to potentially become at risk of reengaging at a later point in time. Using computational modeling, we demonstrate the generative sufficiency of our theory to account for conflict trajectories observed in the broader conflict literature. To facilitate the interpretation of such trajectories, we present a typology of contagion trajectories, discuss the dynamic properties of these trajectories (e.g., stability, bifurcations), and provide implications for future theory building and practice. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The dynamic nature of interpersonal conflict and psychological strain in extreme work settings.

Humanity will mount interplanetary exploration missions within the next two decades, supported by a growing workforce operating in isolated, confined, and extreme (ICE) conditions of space. How will future space workers fare in a closed social world while subjected to persistent stressors? Using a sample of 32 participants operating in ICE conditions over the course of 30-45 days, we developed and tested a dynamic model of conflict and strain. Drawing on conservation of resources (COR) theory, we investigated reciprocal relationships between different forms (i.e., task and relationship) of conflict, and between conflict and strain. Results demonstrated evidence for a resource threat feedback loop as current-day task conflict predicted next-day relationship conflict and current-day relationship conflict predicted next-day task conflict. Additionally, results indicated support for a resource loss feedback loop as current-day relationship conflict predicted next-day strain, and current-day strain predicted next-day relationship conflict. Moreover, we found that job conditions affected these associations as current-day relationship conflict was more associated with next-day task conflict when next-day workload was high, but not when next-day workload was low. Similarly, current-day relationship conflict was more associated with next-day strain when next-day workload was high; however, this association decreased when next-day workload was low. Therefore, the results suggest that workload plays a critical role in weakening the effect of these spirals over time, and suggests that targeted interventions (e.g., recovery days) can help buffer against the negative impact of relationship conflict on strain and decrease the extent that relationship conflict spills over into task disputes. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis.

Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing in silico synthetic planning into their overall approach to accessing target molecules. A data-driven synthesis planning program is one component being developed and evaluated by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) consortium, comprising MIT and 13 chemical and pharmaceutical company members. Together, we wrote this perspective to share how we think predictive models can be integrated into medicinal chemistry synthesis workflows, how they are currently used within MLPDS member companies, and the outlook for this field.