Dirk Helbing, Sachit Mahajan, Regula Hänggli Fricker, Andrea Musso, Carina I. Hausladen, Cesare Carissimo, Dino Carpentras, Elisabeth Stockinger, Javier Argota Sanchez-Vaquerizo, Joshua C. Yang, Mark C. Ballandies, Marcin Korecki, Rohit K. Dubey, Evangelos Pournaras

Journal of Computational Science

Volume 71, July 2023, 102061

The technological revolution, particularly the availability of more data and more powerful computational tools, has led to the emergence of a new scientific field called “Computational Diplomacy”. Our work tries to define its scope and focuses on a popular subarea of it, namely “Digital Democracy”. In recent years, there has been a surge of interest in using digital technologies to promote more participatory forms of democracy. While there are numerous potential benefits to using digital tools to enhance democracy, significant challenges must be addressed. It is essential to ensure that digital technologies are used in an accessible, equitable, and fair manner rather than reinforcing existing power imbalances. This paper investigates how digital tools can be used to help design more democratic societies by investigating three key research areas: (1) the role of digital technologies for facilitating civic engagement in collective decision-making; (2) the use of digital tools to improve transparency and accountability in governance; and (3) the potential for digital technologies to enable the formation of more inclusive and representative democracies. We argue that more research on how digital technologies can be used to support democracy upgrade is needed. Along these lines, we lay out a research agenda for the future.

Read the full article at: www.sciencedirect.com

L. Barnett and A. K. Seth
Phys. Rev. E 108, 014304

We introduce a notion of emergence for macroscopic variables associated with highly multivariate microscopic dynamical processes. Dynamical independence instantiates the intuition of an emergent macroscopic process as one possessing the characteristics of a dynamical system “in its own right,” with its own dynamical laws distinct from those of the underlying microscopic dynamics. We quantify (departure from) dynamical independence by a transformation-invariant Shannon information-based measure of dynamical dependence. We emphasize the data-driven discovery of dynamically independent macroscopic variables, and introduce the idea of a multiscale “emergence portrait” for complex systems. We show how dynamical dependence may be computed explicitly for linear systems in both time and frequency domains, facilitating discovery of emergent phenomena across spatiotemporal scales, and outline application of the linear operationalization to inference of emergence portraits for neural systems from neurophysiological time-series data. We discuss dynamical independence for discrete- and continuous-time deterministic dynamics, with potential application to Hamiltonian mechanics and classical complex systems such as flocking and cellular automata.

Read the full article at: link.aps.org

MELANIE MITCHELL

SCIENCE Vol 381, Issue 665

In 1967, Marvin Minksy, a founder of the field of artificial intelligence (AI), made a bold prediction: “Within a generation…the problem of creating ‘artificial intelligence’ will be substantially solved.” Assuming that a generation is about 30 years, Minsky was clearly overoptimistic. But now, nearly two generations later, how close are we to the original goal of human-level (or greater) intelligence in machines?

Read the full article at: www.science.org