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1. Transforming Teams of Experts into Expert Teams: Eight Principles of Expert Team Performance

   https://doi.org/ISSN 2573-2773  

   Bisbey, Tiffany; Traylor, Allison; Salas, Eduardo (January 2021, Journal of expertise)

   Anders Ericsson’s seminal research on expert performance spurred a number of streams of research across psychological disciplines. Though his work was primarily focused on expert individual performance, there has been increasing interest over the past several decades on the factors underlying expert teamwork. This paper advances eight principles of expert team performance based on decades of team science research: shared mental models, learning and adaptation, role clarity, shared vision, dynamic leadership, psychological safety, cooperation and coordination, and resilience. In addition, we review a number of team development interventions aimed at building team expertise including team training, simulation, coaching, and debriefing. Accordingly, this paper is divided into three sections addressing (1) how expert teams perform, (2) interventions to develop expert team performance, and (3) a reflection on the role Anders Ericsson’s work has played in team science, including a personal reflection from Eduardo Salas on deliberate and guided practice. 

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2. Prediction with expert advice applied to the problem of prediction with expert advice

   https://doi.org/10.1007/s11229-022-03809-5  

   Herrmann, Daniel A. (July 2022, Synthese)

   Abstract We often need to have beliefs about things on which we are not experts. Luckily, we often have access to expert judgements on such topics. But how should we form our beliefs on the basis of expert opinion when experts conflict in their judgments? This is the core of the novice/2-expert problem in social epistemology. A closely related question is important in the context of policy making: how should a policy maker use expert judgments when making policy in domains in which she is not herself an expert? This question is more complex, given the messy and strategic nature of politics. In this paper we argue that the prediction with expert advice (PWEA) framework from machine learning provides helpful tools for addressing these problems. We outline conditions under which we should expert PWEA to be helpful and those under which we should not expect these methods to perform well. 

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3. Large teams develop and small teams disrupt science and technology

   https://doi.org/10.1038/s41586-019-0941-9  

   Wu, Lingfei; Wang, Dashun; Evans, James A. (February 2019, Nature)

   One of the most universal trends in science and technology today is the growth of large teams in all areas, as solitary researchers and small teams diminish in prevalence1,2,3. Increases in team size have been attributed to the specialization of scientific activities3, improvements in communication technology4,5, or the complexity of modern problems that require interdisciplinary solutions6,7,8. This shift in team size raises the question of whether and how the character of the science and technology produced by large teams differs from that of small teams. Here we analyse more than 65 million papers, patents and software products that span the period 1954–2014, and demonstrate that across this period smaller teams have tended to disrupt science and technology with new ideas and opportunities, whereas larger teams have tended to develop existing ones. Work from larger teams builds on more-recent and popular developments, and attention to their work comes immediately. By contrast, contributions by smaller teams search more deeply into the past, are viewed as disruptive to science and technology and succeed further into the future—if at all. Observed differences between small and large teams are magnified for higher-impact work, with small teams known for disruptive work and large teams for developing work. Differences in topic and research design account for a small part of the relationship between team size and disruption; most of the effect occurs at the level of the individual, as people move between smaller and larger teams. These results demonstrate that both small and large teams are essential to a flourishing ecology of science and technology, and suggest that, to achieve this, science policies should aim to support a diversity of team sizes. 

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4. A Phenomenological Study of Expert Problem Solving

   https://doi.org/10.18260/1-2--45703  

   De_Rosa, Alexander; Reed, Teri (April 2024, ASEE Conferences)

   I initially became interested in knowledge transfer after observing my students’ general inability to use mathematical knowledge and skills in an applied (engineering) context. My personal belief was that the students should have an understanding of basic basic mathematical concepts, like integration, and be able to use them correctly to solve problems. Clearly, something was missing in my students’ understanding or perhaps memory that was causing them problems in this regard. In my initial work on knowledge transfer, I found that many students did not even recognize the need to transfer knowledge and for example, to integrate to solve a problem framed in an engineering context unless they were prompted to do so. Concerned by this troubling observation, coupled with my belief that engineers should be able to both understand and apply mathematical concepts in their coursework and careers, I determined to investigate the cause of the problem and, if possible, evidence a potential solution to help students transfer mathematical knowledge into an applied (engineering) context. In this study, I examine an expert (faculty) approach to problem solving using a semi-structured, think-aloud interview protocol coupled with a thorough thematic analysis for phenomenological themes. This analysis, grounded in an existing framework of knowledge transfer, provides a rich, thick description of the knowledge transfer, and problem solving process employed by the faculty expert and serves as a useful comparative case against which student approaches to problem solving and knowledge transfer can be judged. Important findings of this study relate to the extensive use of reflective and evaluative practices employed by the faculty member at all stages of the problem solving process. These internal checks and balances are rarely observed among novice problem solvers and perhaps represent behaviors that we, as educators, should seek to impart in our students if they are to become more adaptable engineers who are better equipped to transfer their knowledge and skills across a range of contexts. 

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5. Finding Teams of Maximum Mutual Respect

   https://doi.org/10.1109/ICDM50108.2020.00149  

   Nikolakaki, Sofia Maria; Pitoura, Evaggelia; Terzi, Evimaria; Tsaparas, Panayiotis (November 2020, 2020 International Conference on Data Mining (ICDM))

   null (Ed.)