Literature DB >> 33527036

Why is Complexity Science valuable for reaching the goals of the UN 2030 Agenda?

Pier Luigi Gentili1.   

Abstract

The goals and targets included in the 2030 Agenda compiled by the United Nations want to stimulate action in areas of critical importance for humanity and the Earth. These goals and targets regard everyone on Earth from both the health and economic and social perspectives. Reaching these goals means to deal with Complex Systems. Therefore, Complexity Science is undoubtedly valuable. However, it needs to extend its scope and focus on some specific objectives. This article proposes a development of Complexity Science that will bring benefits for achieving the United Nations' aims. It presents a list of the features shared by all the Complex Systems involved in the 2030 Agenda. It shows the reasons why there are certain limitations in the prediction of Complex Systems' behaviors. It highlights that such limitations raise ethical issues whenever new technologies interfere with the dynamics of Complex Systems, such as human beings and the environment. Finally, new methodological approaches and promising research lines to face Complexity Challenges included in the 2030 Agenda are put forward.
© The Author(s) 2021.

Entities:  

Keywords:  Complex Systems; Computational Complexity; Emergence; Natural Computing; Networks; Out-of-equilibrium thermodynamics

Year:  2021        PMID: 33527036      PMCID: PMC7838468          DOI: 10.1007/s12210-020-00972-0

Source DB:  PubMed          Journal:  Rend Lincei Sci Fis Nat        ISSN: 2037-4631            Impact factor:   1.627


  36 in total

1.  Emergence of scaling in random networks

Authors: 
Journal:  Science       Date:  1999-10-15       Impact factor: 47.728

2.  Evolution of biological complexity.

Authors:  C Adami; C Ofria; T C Collier
Journal:  Proc Natl Acad Sci U S A       Date:  2000-04-25       Impact factor: 11.205

3.  Social insect networks.

Authors:  Jennifer H Fewell
Journal:  Science       Date:  2003-09-26       Impact factor: 47.728

4.  Extracting dynamical equations from experimental data is NP hard.

Authors:  Toby S Cubitt; Jens Eisert; Michael M Wolf
Journal:  Phys Rev Lett       Date:  2012-03-22       Impact factor: 9.161

Review 5.  The brain as a complex system: using network science as a tool for understanding the brain.

Authors:  Qawi K Telesford; Sean L Simpson; Jonathan H Burdette; Satoru Hayasaka; Paul J Laurienti
Journal:  Brain Connect       Date:  2011

6.  Extending human perception of electromagnetic radiation to the UV region through biologically inspired photochromic fuzzy logic (BIPFUL) systems.

Authors:  Pier Luigi Gentili; Amanda L Rightler; B Mark Heron; Christopher D Gabbutt
Journal:  Chem Commun (Camb)       Date:  2016-01-25       Impact factor: 6.222

7.  Hierarchical structure of biological systems: a bioengineering approach.

Authors:  Carlos Alcocer-Cuarón; Ana L Rivera; Victor M Castaño
Journal:  Bioengineered       Date:  2013-10-21       Impact factor: 3.269

8.  Protein folding in the hydrophobic-hydrophilic (HP) model is NP-complete.

Authors:  B Berger; T Leighton
Journal:  J Comput Biol       Date:  1998       Impact factor: 1.479

9.  Optical Communication among Oscillatory Reactions and Photo-Excitable Systems: UV and Visible Radiation Can Synchronize Artificial Neuron Models.

Authors:  Pier Luigi Gentili; Maria Sole Giubila; Raimondo Germani; Aldo Romani; Andrea Nicoziani; Anna Spalletti; B Mark Heron
Journal:  Angew Chem Int Ed Engl       Date:  2017-05-31       Impact factor: 15.336

10.  Constructing, conducting and interpreting animal social network analysis.

Authors:  Damien R Farine; Hal Whitehead
Journal:  J Anim Ecol       Date:  2015-08-11       Impact factor: 5.091

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