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Academic
From Research to Market: What the EU can learn from the USA
The research project “From Research to Market: What the EU can learn from the USA” addresses the gap between the laboratory research and market. I examine how government, universities and private companies facilitate the transition of research results to market in the USA. In the report I present various programs that are available to the researchers and entrepreneurs in the US and invite to consider them for implementation in Europe. I argue that different stages of lab-to-market transfer require different mechanisms that should not be limited to funding but include technology transfer assistance and advice on intellectual property, mentoring by peers and industry mentors and access to the laboratory space and incubators. I conclude that the US answer to closing the lab-to-market gap is by a combination of support mechanisms that reinforce and complement each other, when implemented simultaneously. I invite to discuss which of the US initiatives and programs described in this report shall be promoted in Europe and at which level. Read More
Academic
Swarm Intelligence used to Amplify the IQ of Collaborating Teams
Abstract— In the natural world, Swarm Intelligence (SI) is a well-known phenomenon that enables groups of organisms to make collective decisions with significantly greater accuracy than the individuals could do on their own. In recent years, a new technology called Artificial Swarm Intelligence (ASI) has been developed that enables similar benefits for human teams. It works by connecting networked teams into real-time systems modeled on natural swarms. Referred to commonly as “human swarms” or “hive minds,” these closed-loop systems have been shown to amplify group performance across a wide range of tasks, from financial forecasting to strategic decision-making. The current study explores the ability of ASI technology to amplify the IQ of small teams. Five small teams answered a series of questions from a commonly used intelligence test known as the Raven’s Standard Progressive Matrices (RSPM) test. Participants took the test first as individuals, and then as groups moderated by swarming algorithms (i.e. “swarms”). The average individual achieved 53.7% correct, while the average swarm achieved 76.7% correct, corresponding to an estimated IQ increase of 14 points. When the individual responses were aggregated by majority vote, the groups scored 56.7% correct, still 12 IQ points less than the real-time swarming method. Read More
Academic
Beyond the Pyramid: Alternative Formal Hierarchical Structures and Team Performance
Formal hierarchical differentiation is a cornerstone of the organizing process. Prior research has focused primarily on pyramid-shaped formal hierarchies, despite documented limitations of the pyramid structure. We adopt a multi-method approach to consider the utility of alternative hierarchical shapes. First, we identify six “pure type” formal hierarchies that teams might employ. Next, we develop three propositions explaining the effects of hierarchy on team members’ cognition and behavior. We use the propositions to parameterize an agent-based computational model in which formal hierarchical differentiation influences team performance by creating power imbalances that affect team members’ perspective taking motivation, and by influencing members’ social identification with the team. The modeling results reveal how the effects of the six hierarchies are contingent upon task characteristics that influence team members’ perspective taking accuracy (e.g., task variety), and enable us to craft an expanded, team-level theory of the association between formal hierarchical differentiation and team performance. A field study of 68 clinical nursing shifts in 5 mid-sized hospitals supports a key theoretical prediction. Specifically, we find that a negatively skewed (inverse pyramid-shaped) formal hierarchy enhances team performance relatively to a positively skewed (pyramid-shaped) hierarchy when task variety is high, but not when task variety is low. Read More
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