The Living Roadmap for Complex Systems Science 2026 - 2030

The Living Roadmap for Complex Systems Science 2006

Executive Summary

Complex systems science - the essential 21st Century scienceThe new science of complex systems will be at the heart of the future of the Worldwide Knowledge Society. It is providing radical new ways of understanding the physical, biological, ecological, and social universe. Complex Systems are ambiguously situated in turbulent, unstable, and changing environments. They evolve andadapt through internal and external dynamic interactions. They are value-laden multi-level multi-componentsystems of systems and they are not predictable in a conventional scientific sense.

Science is the process of reconstructing theory from data. But complex systems must be observed in vivo, requiring new multilevel data collection protocols, and new formalisms to reconstruct intra-level and inter-leveldynamics, and their capacity to adapt to changing environments. Complex systems science bridges the gap between the individual and the collective: from genes to organisms toecosystems, from atoms to materials to products, from notebooks to the Internet, from citizens to society. It cutsacross all the disciplines. It is part of every discipline. It creates new and shorter paths between scientists andaccelerates the flow of scientific knowledge. It reduces the gap between pure and applied science, establishingnew foundations for the design, control and management of systems with unprecedented levels of complexityexceed the capacity of current approaches. It will benefit industry, the public sector, and all social actors. Complex systems science will be the foundation of Europe’s wealth and influence in the 21st century.

The potential impact of this new Complex Systems Science appears in four ways (i) a better understanding ofmany complex systems and their dynamics to support the pressing needs to engineer and manage complexsystems, e.g. cancer, multinational companies, drugs, transport, and climate change; (ii) better control of themeans of fabrication as dynamic complex socio-technical systems, e.g. new processes and materials, multi-sitefactory production, and supply chain dynamics, (iii) a better understanding of the complex environment in which engineered systems exist, e.g. regulation, ethics, markets; and (iv) a better understanding of the design,engineering and management process which is often itself a creative complex multilevel complex humansystem, capable of great successes but inherently liable to spectacular failures.

This Living Roadmap for Complex Systems Science is intended to enable the scientific community’s vision on complex systems research and its applications to inform policy for FET ICT funding in FP7. To help focus thisvision, a number of strategic areas and Grand Challenges are identified.

Complex systems science is computer enabled and ICT will be part of all the research programmes of FP7. Forexample, in Health the new science of complex systems will revolutionise the medical treatment of diseases,and revolutionise the delivery of treatment. Individual problems of individual people will be treated. This requires (i) huge distributed databases of every individual’s genotype, phenotype, medical and general history, (ii) new ways of searching, communicating and processing this information, and (iii) new and more efficientways organising the delivery of treatment to Europe’s half billion inhabitants.

Thus, this crucial ICT-based complex systems programme requires a new family of European platforms, similarto the big instruments used for physics (e.g. CERN) to support the new theories and methods of control anddesign, many of which have yet to be invented. Europe also has an urgent need to increase its human resourcesin complex systems research, which in turn requires an urgent and radical programme of education at the doctoral and masters levels. In FP7, the creation of an Open University of Complex Systems is a priority. Last but not least, there is also an urgent need to bridge the gap between Complex Systems Science and its applications in industry and the public sector.

The strong momentum recently gained in this domain, due to FP6 funding, may enable Europe to become along-term world leader in this new science by coherent use of the main instruments of FP7: the “Ideas” programto address the fundamental questions through complex systems; the “Cooperation” program to address the mainsocietal Grand Challenges; the “Human Resources” program to rapidly bootstrap and train a new generation ofseveral thousand young researchers; the “Capacity” program to develop the big instruments and platformsnecessary for this new science. If this path is actively followed, Strategic investment in Complex SystemsScience will be part of the foundations of Europe’s wealth and influence throughout the 21st century.

A PDF of the wholeLiving Roadmap for Complex Systems Science is available from: https://www.researchgate.net/publication/323966762_Living_Roadmap_for_Complex_Systems_Science [accessed Nov 10 2026].

The Living Roadmap for Complex Systems Science 2026-2030

The Complex Systems Society was first launched at a European level on 7th Dec 2004 during The European Conference on Complex Systems at Foundation ISI in Torino, Italy. It became an international society in 2006 during the ECCS06 Conference in Oxford. Since 2004 the Conference on Complex Systems, organized by the CSS, is one of the most important annual meeting for the complex systems research community.

The Society aims to promote complex systems research pure and applied, assist and advise on problems of complex systems education, concern itself with the broader relations of complex systems to society, foster the interaction between complex systems scientists of different countries, establish a sense of identity amongst complexity scientists, and represent the complexity community at all international levels. It is regulated by a CSS Council and by a CSS Executive Committee.

The CS-DC was recognized as a UNESCO UniTwin in July 2014. In June 2026 The UniTwin mandate of CS-DC as a Cooperation Programme was renewed by UNESCO until 2030.

The new roadmap for the period 2026 - 2030 is a work in progress starting here.

Artificial Intelligence in the Science of Complex Systems

The Sciences of Complex Systems and Artificial Intelligence are deeply entangled through their epistemology and computational methods.

General Systems Theory and Artificial Intelligence emerged together in the nineteen fifties, although both built on research going back decades. While computer scientists were exploring if machines could do intelligent things, it was realised that apparently difference phenomena across the physical and social worlds had common structural properties leading to a theory of 'General Systems' In 1963 Edward Lorenz published the paper "Deterministic Nonperiodic Flow." that showed that even fully deterministic systems can exhibit what is now called chaotic behavior. This fundamentally changed our understanding of predictability in all fields, and advanced systems theory into the computer-enabled Science of Complex Systems. All this challenged Popper's characterisation of 'science' as contingent, based on experiments to test predictions.

Throughout this period the computational methods and epistemological approaches of complex system science and AI were highly entangled. This is illustrated by brain science, In the nineteen forties various models of neurons and brains were proposed in biology, alongside electrical engineers and computer scientists investigating brains, cognition and intelligence in terms of machines and computation. This continued through the fifties, sixties and seventies to the present day. Along the way, in the nineteen eighties, the back propagation method for training neural networks was discovered, opening up the field of machine learning. Deep learning through multilevel neural networks resonates with research into the dynamics of complex multilevel systems.

The game changer in all this is the emergence of Large Language Models and Generative Pre-trained Transformers which has propelled AI to be considered the outstanding problem-solving paradigm today. No longer do we need science - just ask ChatGPT, Claude, or one of many other systems. Indeed, when machines achieve Artificial General Intelligence (AGI), if science is needed, AI can do it for us.

The CS-DC does not believe AGI will be achieved in the foreseeable future. Despite the amazing ability of generative AI to communicate in natural language, we do not believe that machines have human-like understanding of the information they process, and this puts considerable limitations on what they will ever be able to do.

Despite this, the CS-DC sees AI and its methods as fundamental to the science of complex systems, and AI plays a major role in our roadmap.

Consolidation of Teaching and Learning

The CS-DC has developed many teaching and learning initiatives 2014 - 2026. Over the next five years we will consolidate what we have done by a major application of AI, as explained below.

Consolidation of Research

The CS-DC has various international laboratories and research groups working on a variety of applications. We intend to create much better web support for research including better communication between groups, and better dissemination of results.

Open Access Online and Print Publication Through The CS-DC Press

CS-DC has experimented with book and report publication through Amazon. We have demonstrated that we can produce low-cost books at cost price of a few Pounds, Euros or Dollars, with the pdf source available at no cost on our website.

AI to Deliver the CS-DC Open Online Education System 2026-2030

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The CS-DC will make intensive use of AI to design and implement a new programme of open online education.

This will involve curriculum design, course design, implementation of an online delivery system for 10,000+ learners with peer assessment and networked learner interaction, a record system for personalized learning and certification.

We will will construct an explicit multilevel model of the team, its activities, educational modules and learner activity; record where commercial AI systems contribute to planning and delivery; and investigate system explanations of AI generated propositions about this real-world system.

The repeated monthly cycle provides a longitudinal case study in which the model, evidence and explanations are recorded and can be examined as the programme develops.