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      Stewardship of global collective behavior

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          Abstract

          Collective behavior provides a framework for understanding how the actions and properties of groups emerge from the way individuals generate and share information. In humans, information flows were initially shaped by natural selection yet are increasingly structured by emerging communication technologies. Our larger, more complex social networks now transfer high-fidelity information over vast distances at low cost. The digital age and the rise of social media have accelerated changes to our social systems, with poorly understood functional consequences. This gap in our knowledge represents a principal challenge to scientific progress, democracy, and actions to address global crises. We argue that the study of collective behavior must rise to a “crisis discipline” just as medicine, conservation, and climate science have, with a focus on providing actionable insight to policymakers and regulators for the stewardship of social systems.

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          Most cited references191

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          Collective dynamics of 'small-world' networks.

          Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
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            The Strength of Weak Ties

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              Using social and behavioural science to support COVID-19 pandemic response

              The COVID-19 pandemic represents a massive global health crisis. Because the crisis requires large-scale behaviour change and places significant psychological burdens on individuals, insights from the social and behavioural sciences can be used to help align human behaviour with the recommendations of epidemiologists and public health experts. Here we discuss evidence from a selection of research topics relevant to pandemics, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision-making, leadership, and stress and coping. In each section, we note the nature and quality of prior research, including uncertainty and unsettled issues. We identify several insights for effective response to the COVID-19 pandemic and highlight important gaps researchers should move quickly to fill in the coming weeks and months.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                06 July 2021
                21 June 2021
                21 June 2021
                : 118
                : 27
                : e2025764118
                Affiliations
                [1] aCenter for an Informed Public, University of Washington , Seattle, WA 98195;
                [2] beScience Institute, University of Washington , Seattle, WA 98195;
                [3] cEthics & Philosophy of Technology, Delft University of Technology , 2628 CD Delft, The Netherlands;
                [4] dInstitute of Philosophy, Australian Catholic University , Banyo Queensland 4014, Australia;
                [5] eEarth System Analysis, Potsdam Institute for Climate Impact Research , Member of the Leibniz Association, 14473 Potsdam, Germany;
                [6] fTübingen AI Center, University of Tübingen , 72074 Tübingen, Germany;
                [7] gDepartment of Biology, University of Washington , Seattle, WA 98195;
                [8] hPrinceton School of Public and International Affairs, Princeton University , Princeton, NJ 08544;
                [9] iDepartment of Collective Behaviour, Max Planck Institute of Animal Behavior , 78315 Radolfzell am Bodensee, Germany;
                [10] jCentre for the Advanced Study of Collective Behaviour, University of Konstanz , 78464 Konstanz, Germany;
                [11] kDepartment of Biology, University of Konstanz , 78464 Konstanz, Germany;
                [12] lStockholm Resilience Centre, Stockholm University , 11419 Stockholm, Sweden;
                [13] mSanta Fe Institute , Santa Fe, NM 87501;
                [14] nDepartment of Ecology and Evolutionary Biology, Princeton University , Princeton, NJ 08544;
                [15] oDepartment of Environmental Studies, New York University , New York, NY 10012;
                [16] pInstitute for Theoretical Biology, Department of Biology, Humboldt Universität zu Berlin , 10115 Berlin, Germany;
                [17] qDepartment of Anthropology, Hunter College of the City University of New York , New York, NY 10065;
                [18] rDepartment of Psychology, New York University , New York, NY 10003;
                [19] sCenter for Neural Science, New York University , New York, NY 10003;
                [20] tDepartment of Psychology, Princeton University , Princeton, NJ 08544;
                [21] uAndlinger Center for Energy and Environment, School of Engineering and Applied Science, Princeton University , Princeton, NJ 08544
                Author notes
                1To whom correspondence may be addressed. Email: joebak@ 123456uw.edu .

                Edited by Bonnie J. McCay, Rutgers, The State University of New Jersey, New Brunswick, NJ, and approved May 17, 2021 (received for review December 14, 2020)

                Author contributions: J.B.B.-C., M.A., W.B., C.T.B., M.A.C., I.D.C., J.F.D., M.G., A.S.G., J.J., A.B.K., R.E.M., P.R., D.I.R., K.J.T., J.J.V.B., and E.U.W. wrote the paper.

                Author information
                http://orcid.org/0000-0002-7590-3824
                http://orcid.org/0000-0001-5879-8033
                http://orcid.org/0000-0002-9077-5242
                http://orcid.org/0000-0002-2070-385X
                http://orcid.org/0000-0001-8556-4558
                http://orcid.org/0000-0002-1712-9455
                http://orcid.org/0000-0001-8232-8365
                http://orcid.org/0000-0002-4733-998X
                http://orcid.org/0000-0001-9049-5219
                http://orcid.org/0000-0002-1678-3631
                Article
                202025764
                10.1073/pnas.2025764118
                8271675
                34155097
                a20164ab-985c-4318-916c-e624f50a96c8
                Copyright © 2021 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 10
                Categories
                447
                434
                9
                Perspective
                Social Sciences
                Sustainability Science

                collective behavior,computational social science,social media,complex systems

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