Academic Engagement Open access

How can the science of complexity help young people realize their talents and choose their future?

Authors

Published
January 21, 2025
Section
Academic Engagement
DOI
10.4081/peasa.50

Abstract

We live in a world of great discoveries in all sciences, but also serious threats to our very existence. This is, of course, not the first nor the last time we will be faced with such challenges, but every time the stakes are higher. Who can we turn to for help in such situations, if not the young people whom we have inherited these threats and who will have to struggle with them in the future?  We all agree that Education is our best hope. We also know that Education is no longer an accumulation of knowledge, or an acquisition of aptitudes aiming to equip us with the means of securing employment. The explosive revolution in all realms of digital technology and access to information has changed everything. It is now, more than ever, imperative that the next generations develop a horizontally broad education, transgressing old boundaries, based on interdisciplinarity and a firm belief that, as Leonardo da Vinci noted, “everything is connected to everything else”. In this paper, I will try to elucidate how the new science of Complexity can help us achieve this goal, using several examples that can be used to infuse our youth with a true appreciation of all sciences and arts, excite their imagination and creativity, and enable them to face the difficulties that lie ahead. What, if not Education, can accomplish that? When, if not now?

Keywords

References

  1. Anastassiou S, Bountis T (2019). Dynamical systems and an introduction to the theory of chaos. Athens, A. Pnevmatikos.
  2. Antonopoulos C, Fokas AS, Bountis T (2016). Dynamical complexity in the C. elegans neural network. Eur Phys J Spec Top 225:1255-69. DOI: https://doi.org/10.1140/epjst/e2016-02670-3
  3. Bandt C, Pompe B (2002). Permutation entropy: A natural complexity measure for time series. Phys. Rev. Lett. 88:174102. DOI: https://doi.org/10.1103/PhysRevLett.88.174102
  4. Barnsley MF (1993). Fractals everywhere. 2nd ed. San Francisco, Elsevier.
  5. Barrat A, Barthelemy M, Vespignani A (2008). Dynamical processes on complex networks. Cambridge, Cambridge University Press. DOI: https://doi.org/10.1017/CBO9780511791383
  6. Bountis T (1995). Dynamical systems and chaos, vol. 1. [Book in Greek]. Athens, G. Papasotiriou.
  7. Bountis T (1997). Dynamical systems and chaos, vol. 2. [Book in Greek]. Patras, University of Patras.
  8. Bountis T (2004). The wonderful world of fractals. Athens, Leader Books.
  9. Bountis T, Fokas AS, Psarakis EZ (2017). Fractal analysis of tree paintings by Piet Mondrian (1872-1944). Int J Arts Technol 10: 27-42. DOI: https://doi.org/10.1504/IJART.2017.083902
  10. Bountis T, Mihailidis T (2019). Speaking to Athena about chaos and complexity. [Book in Greek]. Athens, Pataki Publ. House.
  11. Chen S, Wei H, Cattani C, Altieri G (2012). Traffic dynamics on complex networks: a survey. Mat. Meth. Eng. 2012:732698. DOI: https://doi.org/10.1155/2012/732698
  12. Dabrowski M (2003). Schoenberg, Kandinsky, and the Blue Rider. Vol. 1. London, Scala Publishers.
  13. Hahn G, Zamora-López G, Uhrig L, et al. (2021). Signature of consciousness in brain-wide synchronization patterns of monkey and human fMRI signals. NeuroImage 126:117470. DOI: https://doi.org/10.1016/j.neuroimage.2020.117470
  14. Haken H (2002). Brain dynamics. Heidelberg, Springer. DOI: https://doi.org/10.1007/978-3-540-46284-2
  15. Hizanidis J, Kanas V, Bezerianos A, Bountis T (2014). Chimera states in nonlocally coupled networks of hindmarsh-rose neuron models. Int. J. Bifurc. Chaos 24:1450030. DOI: https://doi.org/10.1142/S0218127414500308
  16. Iasemidis LD (2003). Epileptic seizure prediction and control. IEEE T Bio-Med Eng 50:549-58. DOI: https://doi.org/10.1109/TBME.2003.810705
  17. Iasemidis LD, Shiau DS, Pardalos PM, et al. (2005). Long-term prospective on-line real-time seizure prediction. Clin Neurophysiol 116:532-44. DOI: https://doi.org/10.1016/j.clinph.2004.10.013
  18. Kuramoto Y, Battogtokh D (2002). Coexistence of coherence and incoherence in nonlocally coupled phase oscillators. Nonlinear Phenom Complex Syst 5:380-5.
  19. Latora V, Nicosia V, Russo G (2017). Complex networks: principles, methods and applications. Cambridge, Cambridge University Press. DOI: https://doi.org/10.1017/9781316216002
  20. Lopez-Ruiz R, Mancini HL, Calbet X (1995). A statistical measure of complexity. Phys Lett A 209:321-6. DOI: https://doi.org/10.1016/0375-9601(95)00867-5
  21. Loscalzo J, Barabasi A, Silverman E (2017). Network medicine: complex systems in human disease and therapeutics. Cambridge, Cambridge University Press. DOI: https://doi.org/10.4159/9780674545533
  22. Massimini M, Tononi G (2018). Sizing up consciousness: towards an objective measure of the capacity for experience. Oxford, Oxford University Press. DOI: https://doi.org/10.1093/oso/9780198728443.001.0001
  23. Nicolis G, Nicolis C (2007). Foundations of complex systems: Nonlinear dynamics, statistical physics, information and prediction. Singapore, World Scientific. DOI: https://doi.org/10.1142/9789812775658
  24. Salari N, Maye A (2008). Brain waves: how synchronized neuronal oscillations can explain the perception of illusory objects. Saarbrucken, VDM Verlag.
  25. Sigaki HYD, Perc M, Ribeiro HV (2018). History of art paintings through the lens of entropy and complexity. P Natl Acad Sci USA 115:E8585-94. DOI: https://doi.org/10.1073/pnas.1800083115
  26. Vega-Redondo F (2013). Complex social networks. Cambridge, Cambridge University Press.
  27. Wright JK (2007). Schoenberg, Wittgenstein and the Vienna Circle. Bern, Peter Lang Academic Publishers.
  28. Zeki S, Romaya JP, Benincasa DMT, et al. (2014). The experience of mathematical beauty and its neural correlates. Front Hum Neurosci 8:68. DOI: https://doi.org/10.3389/fnhum.2014.00068

Metrics

Citations and online attention from Dimensions, Altmetric and PlumX.

PlumX Metrics

Downloads

Download data is not yet available.

License

Copyright (c) 2025 The Author(s)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.