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      Creativity in temporal social networks: how divergent thinking is impacted by one’s choice of peers

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          Abstract

          Creativity is viewed as one of the most important skills in the context of future-of-work. In this paper, we explore how the dynamic (self-organizing) nature of social networks impacts the fostering of creative ideas. We run six trials ( N = 288) of a web-based experiment involving divergent ideation tasks. We find that network connections gradually adapt to individual creative performances, as the participants predominantly seek to follow high-performing peers for creative inspirations. We unearth both opportunities and bottlenecks afforded by such self-organization. While exposure to high-performing peers is associated with better creative performances of the followers, we see a counter-effect that choosing to follow the same peers introduces semantic similarities in the followers’ ideas. We formulate an agent-based simulation model to capture these intuitions in a tractable manner, and experiment with corner cases of various simulation parameters to assess the generality of the findings. Our findings may help design large-scale interventions to improve the creative aptitude of people interacting in a social network.

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          WordNet: a lexical database for English

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            Structural Holes and Good Ideas

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              Coevolutionary games--a mini review.

              Prevalence of cooperation within groups of selfish individuals is puzzling in that it contradicts with the basic premise of natural selection. Favoring players with higher fitness, the latter is key for understanding the challenges faced by cooperators when competing with defectors. Evolutionary game theory provides a competent theoretical framework for addressing the subtleties of cooperation in such situations, which are known as social dilemmas. Recent advances point towards the fact that the evolution of strategies alone may be insufficient to fully exploit the benefits offered by cooperative behavior. Indeed, while spatial structure and heterogeneity, for example, have been recognized as potent promoters of cooperation, coevolutionary rules can extend the potentials of such entities further, and even more importantly, lead to the understanding of their emergence. The introduction of coevolutionary rules to evolutionary games implies, that besides the evolution of strategies, another property may simultaneously be subject to evolution as well. Coevolutionary rules may affect the interaction network, the reproduction capability of players, their reputation, mobility or age. Here we review recent works on evolutionary games incorporating coevolutionary rules, as well as give a didactic description of potential pitfalls and misconceptions associated with the subject. In addition, we briefly outline directions for future research that we feel are promising, thereby particularly focusing on dynamical effects of coevolutionary rules on the evolution of cooperation, which are still widely open to research and thus hold promise of exciting new discoveries. Copyright (c) 2009 Elsevier Ireland Ltd. All rights reserved.
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                Author and article information

                Contributors
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                Journal
                Journal of The Royal Society Interface
                J. R. Soc. Interface.
                The Royal Society
                1742-5689
                1742-5662
                October 2020
                October 14 2020
                October 2020
                : 17
                : 171
                : 20200667
                Affiliations
                [1 ]Department of Electrical and Computer Engineering, University of Vermont, VT, USA
                [2 ]Department of Computer Science, University of Vermont, VT, USA
                [3 ]Department of Mathematics & Statistics, University of Vermont, VT, USA
                [4 ]Department of Physics and Astronomy, University of Rochester, Rochester, NY, USA
                Article
                10.1098/rsif.2020.0667
                d91f34ea-9580-4fe5-b9a8-9ca9f2b0e484
                © 2020

                https://royalsociety.org/-/media/journals/author/Licence-to-Publish-20062019-final.pdf

                https://royalsociety.org/journals/ethics-policies/data-sharing-mining/

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