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search.filters.applied.f.access: openAccess × End date: 2021 × Start date: 2021 × Has files: Yes × Version: publishedVersion × Subject: Complex systems ×

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    Stewardship of global collective behavior
    (Washington, DC : National Acad. of Sciences, 2021) Bak-Coleman, Joseph B.; Alfano, Mark; Barfuss, Wolfram; Bergstrom, Carl T.; Centeno, Miguel A.; Couzin, Iain D.; Donges, Jonathan F.; Galesic, Mirta; Gersick, Andrew S.; Jacquet, Jennifer; Kao, Albert B.; Moran, Rachel E.; Romanczuk, Pawel; Rubenstein, Daniel I.; Tombak, Kaia J.; Van Bavel, Jay J.; Weber, Elke U.
    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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    A unified and automated approach to attractor reconstruction
    (London : IOP, 2021) Kraemer, K. H.; Datseris, G.; Kurths, J.; Kiss, I. Z.; Ocampo-Espindola, J. L.; Marwan, N.
    We present a fully automated method for the optimal state space reconstruction from univariate and multivariate time series. The proposed methodology generalizes the time delay embedding procedure by unifying two promising ideas in a symbiotic fashion. Using non-uniform delays allows the successful reconstruction of systems inheriting different time scales. In contrast to the established methods, the minimization of an appropriate cost function determines the embedding dimension without using a threshold parameter. Moreover, the method is capable of detecting stochastic time series and, thus, can handle noise contaminated input without adjusting parameters. The superiority of the proposed method is shown on some paradigmatic models and experimental data from chaotic chemical oscillators.