Complex Systems Science as a New Transdisciplinary Science

The 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 techno-social universe. Complex Systems are open, value-laden, multi-level, multi-component, reconfigurable systems of systems, situated in turbulent, unstable, and changing environments. They evolve, adapt and transform through internal and external dynamic interactions. They are the source of very difficult scientific challenges for observing, understanding, reconstructing and predicting their multi-scale dynamics. The challenges posed by the multi-scale modelling of both natural and artificial adaptive complex systems can only be met with radically new collective strategies for research and teaching.

Complex systems science bridges the gap between the individual and the collective: from genes to organisms to ecosystems, from atoms to materials to products, from digital media to the Internet, from citizens to society. It cuts across all disciplines. It enables new and shorter paths between scientists and integrates the flow of scientific knowledge. It reduces the gap between pure and applied science, establishing new foundations for the design, control and management of systems with unprecedented levels of complexity, which exceed the capacity of current approaches. It will benefit the environment and industry, the health and education sector and all public and social actors. Understanding complex systems will be the basis of worldwide wealth and socio-economic wellbeing in the 21st century.

The Complex Systems Digital Campus (CS-DC) has been recognized by UNESCO as an UniTwin. An UNESCO UniTwin is “twinning universities” for launching a new science, here Complex Systems Science. The CS-DC UNESCO UniTwin will federate the Research and Education Institutions all around the world wishing to deal with the scientific and societal challenges of complex systems science. It has in October 2013 more than eighty founder members in twenty two countries and four continents. It will coordinate an evolving social network involved in identifying the scientific challenges through living complex systems roadmaps, and facilitate sharing all the research and educative resources for overcoming them. The Digital Campus will be structured through transdisciplinary education and research e-departments. Each e-department with its e-laboratories is federating the e-community addressing the research and education challenges of its chapter. The Digital Campus will be strongly connected to a Citizen Cyber-science by involving citizens with their sensing, computing and thinking resources towards ubiquitous observing, learning and computing. This large scale collaborative work will embody social intelligent strategies towards new scientific and educational practices, dealing with the difficult societal and environmental challenges of an increasingly interconnected world.

Environmental, societal, technical and economic benefits stem from complex systems engineering. These benefits come from predictive, adaptive and robust integrated models that allow us to live with and protect the complex systems within and around us. The most noteworthy results will be improved understanding of complex systems, increasingly personalized health and education, the prevention of, and resilience to epidemics and more generally, extreme events. Reducing uncertainty regarding the impact of our actions on complex systems will lead to a transformation in the relationship between science and society, engineering, economics, politics and ethics.

What are Complex Systems?

In general terms, a “complex system” is any system comprised of a great number of heterogeneous entities, where local interactions among entities create multiple levels of collective structure and organization. Examples include natural systems, ranging from bio-molecules and living cells to human social systems and the ecosphere, as well as sophisticated artificial systems such as the Internet, power grid or any large-scale distributed software system. The specificity of complex systems, generally underinvestigated or simply not addressed by traditional science, resides in the emergence of non-trivial superstructures that often dominate the system’s behavior and cannot be easily traced back to the properties of the constituent entities. Not only do higher emergent features of complex systems arise from lower-level interactions, but the global patterns that they create affect in turn these lower levels—a feedback loop sometimes called immergence. In many cases, complex systems possess striking properties of robustness against various large-scale, multi-dimensional perturbations. They have an inherent capacity to adapt and maintain their stability. Because complexity requires analysis at many different spatial and temporal scales, scientists face radically new challenges when trying to observe complex systems, learning how to describe them effectively, and developing original theories of their behavior and control.

Complex Systems Science & Engineering as a Transdisciplinary Approach
Complex systems demand a transdisciplinary approach. First, because the universal questions those they raise can be expressed under almost the same formulation for widely different objects across a broad spectrum of disciplines—from biology to computer networks to human societies. Second, because the models and methods used to tackle these questions also belong to different disciplines—mainly computer science, mathematics and physics. Last, because standard methods in specialized domains rarely take into account the multiple-level viewpoints needed in the context of complex systems, and attained only through a more integrated and transdisciplinary approach.

Two main types of transdisciplinary approaches can be envisioned. The first path involves working on an object of research that is intrinsically multidisciplinary, for example “cognition”. Here, one poses various questions about the same object from multiple and somewhat disconnected disciplinary viewpoints (neuroscience, psychology, artificial intelligence, etc.)—in contrast to integrated and interdisciplinary. This first path leads to integrative and predictive sciences, like an integrative biology, ecology, cognitive science, social science, geoscience. The second path consists in studying the same question, for example “synchronization”, in connection with different objects of research in different disciplines (statistical physics, chemistry, biology, electrical engineering, etc.). This second approach establishes the foundations of a true science of complex systems. However, the success of these two approaches, which are complementary to one another, is critically dependent on the design of new protocols, new models and new formalisms for the reconstruction of emergent phenomena and dynamics at multiple scales. It is in this joint goal of (a) massive data acquisition on the basis of a set of prior assumptions, and (b) reconstruction and modeling of these data, that the future science of complex systems can develop and thrive. There remains much to do in the theoretical domain in order to build concepts and models able to provide an elegant and meaningful explanation to the so-called “emergent” phenomena that characterize complex systems.

The Complex Systems Roadmaps

The role of complex systems roadmaps, for example the European, African and Latino-American ones, is to identify a set of wide thematic chapters for complex systems research over the next ten years. Each chapter is organized around a theoretical transversal question or an experimental multi-level object and proposes a relevant set of “grand challenges”, i.e., clearly identifiable problems whose solution would stimulate significant progress in both theoretical methods and experimental strategies.

Theoretical transversal questions are varied. An important aspect is to take into account different levels of organization. In complex systems, individual behaviour leads to the emergence of collective organization and behaviour at higher levels. These emergent structures in turn influence individual behaviour. This raises important questions: what are the various levels of organization and what are their characteristic scales in space and time? How do reciprocal influences operate between the individual and collective behaviour? How can we simultaneously study multiple levels of organization, as is often required in problems in biology or social sciences? How can we efficiently characterize emergent structures? How can we understand the changing structures of emergent forms, their robustness or sensitivity to perturbations? Is it more important to study the attractors of a dynamics or families of transient states? How can we understand slow and fast dynamics in an integrated way? What special emergent properties characterize those complex systems that are especially capable of adaptation in changing environments? During such adaptation, individual entities often appear and disappear, creating and destroying links in the graph of their interactions. How can we understand the dynamics of these changing interactions and their relationship to the system’s functions?

Questions related to the reconstruction of dynamics from data also play a central role. They include questions related to the epistemic loop (the problem of moving from data to models and back to data, including model-driven data production), which is the source of very hard inverse problems. Other fundamental questions arise around the constitution of databases, or the selection and extraction of stylized facts from distributed and heterogeneous databases, or the deep problem of reconstructing appropriate dynamical models from incomplete, incorrect or redundant data.

Finally, some questions are related to the governance and design of complex systems. “Complex systems engineering” concerns a second class of inverse problems. On the basis of an incomplete reconstruction of dynamics based from data, how can we steer the system’s dynamics toward desirable consequences or at least keep the system away inside its viability constraints? How can control be distributed on many distinct hierarchical levels in either a centralized or decentralized way—a so-called “complex control”. Finally, how is it possible to design complex artificial systems, integrating new ways of studying their multilevel control?

All these general questions are detailed in the roadmaps. The first questions concern different aspects of emergent phenomena in the context of multiscale systems. The question of reconstructing multiscale dynamics addresses the problem of dealing with incomplete, badly organized and underqualified data sets. Another important aspect to consider is the importance played in complex systems by the reaction to perturbations: it can be weak in certain components or scales of the system and strong in others. These effects, central to the prediction and control of complex systems and models, must be specifically studied. In addition, it is also important to develop both strategies for representing and extracting pertinent parameters and formalisms for modelling morphodynamics. Learning to successfully predict multi-scale dynamics raises other important challenges, as the question of being able to go from controlled systems to governed systems in which the control is less centralized and more distributed among hierarchical levels. The last general question addressed in this roadmap concerns the conception of artificial complex systems.

Experimental multilevel objects for complex systems research draw their inspiration from different kinds of complex phenomena arising from different scientific fields. Their presentation follows the hierarchy of organizational levels of complex systems, either natural, social or artificial. Understanding this hierarchy is itself a primary goal of complex systems science.

In modern physics, the understanding of collective behaviour and out-of-equilibrium fluctuations is increasingly important. Biology (in the broad meaning of the word, going from biological macromolecules to ecosystems) is one of the major fields of application where complex behaviours must be tackled. Indeed, the question of gaining an integrated understanding of the different scales of biological systems is probably one of the most difficult and exciting tasks for researchers in the next decade. Before we can hope to integrate a complete hierarchy of living systems, from the bio-macromolecules to ecosystems, each integration between one level and the next has to be studied. The first level concerns the cellular and subcellular spatiotemporal organization. At a higher level, the study of multicellular systems (integrating intracellular dynamics, such as gene regulation networks, with cell-cell signalling and biomechanical interactions) is of great importance, as is the question of the impact of local perturbations in the stability and dynamics of multicellular organizations. Continuing on the way to larger scales raises the question of physiological functions emerging from sets of cells and tissues in their interaction with a given environment. At the highest level, the understanding and control of ecosystems requires integrating interacting living organisms in a given biotope. In the context of human and social sciences, too, the complex systems approach is central (even if currently less developed than biology). One crucial domain to be investigated is learning how the individual cognition of interacting agents leads to social cognition. An important situation requiring particular attention due to its potential societal consequences is related to innovation, its dynamical appearance and diffusion, frequency and coevolution with cognition. Complex systems approaches can also help us gain an integrated understanding of all components, hierarchical levels and time scales in a way that would help moving society toward sustainable development. In the context of globalization and the growing importance of long-distance interactions through a variety of networks, complex systems analysis (including direct observations and simulation experiments) can help us explore a variety of issues related to economic development, social cohesion, or the environment at different geographical scales.

Finally, the fast growing influence of information and communication technologies in our societies and the large number of decentralized networks relying on these new technologies are also in great need of studies and solutions coming from complex systems research. In particular, the trend going from processors to networks leads to the emergence of so-called “ubiquitous intelligence”, which plays an increasing role in how the networks of the future will be designed and managed.

Author Bio

Paul Bourgine is the President of the UNESCO UniTwin CS-DC, the founder and honorary director of the French National Network of Complex Systems, the founder of the Complex Systems Institute of Paris Ile-de-France. He is also a co-founder of the CECOIA conferences in economics and artificial intelligence (1986), the ECAL conferences in artificial life (1990), the ECCE conferences in cognitive economics (2004) and the ECCS conferences in complex systems science (2005). His current research field include genetic networks, neural networks and social cognition and learning and co-evolutionary dynamics. He Graduated from Ecole Polytechnique and obtained a PhD in Economics in 1983, and a Habilitation in cognitive science (1989). He published several books including, Toward a Practice of Autonomous Systems and Advances in Artificial Life, ECAL 2011


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  1. [...] The 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 techno-social universe. Complex Systems are open, value-laden, multi-level, multi-component, reconfigurable systems of systems, situated in turbulent, unstable, and changing environments. They evolve, adapt and transform through internal and external dynamic interactions. They are the source of very difficult scientific challenges for observing, understanding, reconstructing and predicting their multi-scale dynamics. The challenges posed by the multi-scale modelling of both natural and artificial adaptive complex systems can only be met with radically new collective strategies for research and teaching (…)  [...]

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