Research Uncovers New Field of 'Complexity Data Science' via Digital Twins and Integration of Complexity Science, Data Science

A groundbreaking study published in the journal PNAS Nexus has introduced a new field of science called "complexity data science," which combines the principles of complexity science and data science to provide novel insights and capabilities for tackling complex societal challenges.

According to researchers Frank Emmert-Streib and colleagues, digital twins - or virtual replicas of real-world objects or systems - are more than just tools for scientific inquiry; they represent an exemplar of integrating complexity science and data science into a new field. By leveraging real-world data to continuously update and refine their simulations, researchers can explore "what if" scenarios that have previously been experimentally inaccessible in the real world.

The study, led by Emmert-Streib, reveals a profound connection between digital twins and the field of complexity science, particularly when tasked with modeling complex systems such as global climate patterns and economic systems. By simulating these systems through digital twins, researchers can better understand their intricate dynamics, identifying potential flashpoints and optimizing solutions.

This new approach to data analysis also has significant implications for fields like medicine, immunology, and epidemiology, where digital twin simulations can provide unprecedented insights into the behavior of complex biological systems.

Furthermore, Emmert-Streib and colleagues propose that complexity data science holds promise not just in academia but also in real-world applications - such as machine learning, explainable AI, and forecasting economic trends. By shedding light on previously unexplored dimensions of these fields, researchers hope to unlock new capabilities for harnessing insights from data.

The study's findings have far-reaching implications for tackling pressing global challenges like climate change, public health crises, and systemic economic instability, emphasizing the transformative potential of interdisciplinary approaches in science.

[Image Credit: Emmert-Streib et al.]

Source: Emmert-Streib F, et al. "Complexity data science: A spin-off from digital twins," PNAS Nexus (2024). DOI: 10.1093/pnasnexus/pgae456.