Skip to main navigation Skip to search Skip to main content

Moving beyond simulation and learning: Unveiling the potential of complexity data science

  • Frank Emmert-Streib*
  • , Hocine Cherifi
  • , Stuart Kauffman
  • , Olli Yli-Harja
  • *Corresponding author for this work
  • Tampere University
  • CNRS
  • Institute for Systems Biology

Research output: Contribution to journalArticlepeer-review

Abstract

AU Complexity: Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly science is a multidisciplinary field that examines: various aspects of complex systems. While complexity science places a significant emphasis on simulation, it has a somewhat neglectful treatment of learning. In this paper, we explore a recent example of the potential synergy between simulation and learning, illustrated by the concept of digital twins. We argue that integrating simulation and learning holds significant promise beyond the scope of digital twins alone. In our view, the general amalgamation of complexity science and data science heralds the dawn of a distinct and innovative field in its own right, which we call complexity data science.

Original languageEnglish
Article numbere0000002
JournalPLOS Complex Systems
Volume1
Issue number2 February
DOIs
Publication statusPublished - Feb 2024
Externally publishedYes

Fingerprint

Dive into the research topics of 'Moving beyond simulation and learning: Unveiling the potential of complexity data science'. Together they form a unique fingerprint.

Cite this