The dynamics of team cognition: A process-oriented theory of knowledge emergence in teams.

Team cognition has been identified as a critical component of team performance and decision-making. However, theory and research in this domain continues to remain largely static; articulation and examination of the dynamic processes through which collectively held knowledge emerges from the individual- to the team-level is lacking. To address this gap, we advance and systematically evaluate a process-oriented theory of team knowledge emergence. First, we summarize the core concepts and dynamic mechanisms that underlie team knowledge-building and represent our theory of team knowledge emergence (Step 1). We then translate this narrative theory into a formal computational model that provides an explicit specification of how these core concepts and mechanisms interact to produce emergent team knowledge (Step 2). The computational model is next instantiated into an agent-based simulation to explore how the key generative process mechanisms described in our theory contribute to improved knowledge emergence in teams (Step 3). Results from the simulations demonstrate that agent teams generate collectively shared knowledge more effectively when members are capable of processing information more efficiently and when teams follow communication strategies that promote equal rates of information sharing across members. Lastly, we conduct an empirical experiment with real teams participating in a collective knowledge-building task to verify that promoting these processes in human teams also leads to improved team knowledge emergence (Step 4). Discussion focuses on implications of the theory for examining team cognition processes and dynamics as well as directions for future research. (PsycINFO Database Record

A geographic analysis of population density thresholds in the influenza pandemic of 1918-19.

BACKGROUND: Geographic variables play an important role in the study of epidemics. The role of one such variable, population density, in the spread of influenza is controversial. Prior studies have tested for such a role using arbitrary thresholds for population density above or below which places are hypothesized to have higher or lower mortality. The results of such studies are mixed. The objective of this study is to estimate, rather than assume, a threshold level of population density that separates low-density regions from high-density regions on the basis of population loss during an influenza pandemic. We study the case of the influenza pandemic of 1918-19 in India, where over 15 million people died in the short span of less than one year. METHODS: Using data from six censuses for 199 districts of India (n=1194), the country with the largest number of deaths from the influenza of 1918-19, we use a sample-splitting method embedded within a population growth model that explicitly quantifies population loss from the pandemic to estimate a threshold level of population density that separates low-density districts from high-density districts. RESULTS: The results demonstrate a threshold level of population density of 175 people per square mile. A concurrent finding is that districts on the low side of the threshold experienced rates of population loss (3.72%) that were lower than districts on the high side of the threshold (4.69%). CONCLUSIONS: This paper introduces a useful analytic tool to the health geographic literature. It illustrates an application of the tool to demonstrate that it can be useful for pandemic awareness and preparedness efforts. Specifically, it estimates a level of population density above which policies to socially distance, redistribute or quarantine populations are likely to be more effective than they are for areas with population densities that lie below the threshold.

Mortality from the influenza pandemic of 1918-1919: the case of India.

Estimates of worldwide mortality from the influenza pandemic of 1918-1919 vary widely, from 15 million to 100 million. In terms of loss of life, India was the focal point of this profound demographic event. In this article, we calculate mortality from the influenza pandemic in India using panel data models and data from the Census of India. The new estimates suggest that for the districts included in the sample, mortality was at most 13.88 million, compared with 17.21 million when calculated using the assumptions of Davis (1951). We conclude that Davis' influential estimate of mortality from influenza in British India is overstated by at least 24%. Future analyses of the effects of the pandemic on demographic change in India and worldwide will need to account for this significant downward revision.

A cautionary note on modeling growth trends in longitudinal data.

Random coefficient and latent growth curve modeling are currently the dominant approaches to the analysis of longitudinal data in psychology. The application of these models to longitudinal data assumes that the data-generating mechanism behind the psychological process under investigation contains only a deterministic trend. However, if a process, at least partially, contains a stochastic trend, then random coefficient regression results are likely to be spurious. This problem is demonstrated via a data example, previous research on simple regression models, and Monte Carlo simulations. A data analytic strategy is proposed to help researchers avoid making inaccurate inferences when observed trends may be due to stochastic processes.