The Institute is dedicated to the science of complex systems and it supports a diverse range of complementary and interdisciplinary research projects. In 2006 and 2008, brainstorming sessions during the “Entretiens de Cargèse” event have been organized jointly by RNSC, ISC-PIF and IXXI to identify questions and topics that are particularly relevant to complex systems research. This led to the publication of the French Roadmap for Complex Systems, which aim is to foster the coordination of the complex systems community on focused topics and questions, as well as to present contributions and challenges in complex systems to the public, political, and industrial spheres. This document was signed by more than 70 scientists from major French institutions
Research Areas
Complex systems are natural or artificial systems that have a large number of entities whose multitude of “local” interactions lead to the emergence of “global” properties. The latter are generally difficult to predict simply by knowing the properties of these entities. The complex systems approach aims at advancing our understanding of the global behaviors of these multi-scale systems: it relies on the “big data” phenomenon and the rise of high-performance computing to reconstruct and model complex systems and thus facilitate their study.
Between 2006 and 2017, several working sessions have been jointly organized by the RNSC, ISC-PIF and IXXI to identify questions and topics particularly relevant to research on complex systems. This led to the publication of the French Roadmap for Complex Systems, which aims to foster coordination of the complex systems community on specific topics and issues, as well as to present the contributions and challenges of complex systems to the public, political and industrial spheres.
Main objects
Complex Matter
- Non-equilibrium statistical physics
- Damage and fracture of heterogeneous materials
- Glassy dynamics
- Bifurcations in turbulence: from dynamo action to slow dynamics
From molecules to organisms
- Fluctuations and noise in biological systems
- Stability in biology
- Multiscaling
- Human physiopathology and animal models
Physiological functions
- Integrating multimodal measurements and observations of physiological activities at different spatial and temporal scales.
- Characterizing the contextual features determining the onset of a physiological function, or its maintenance and modulation.
- Investigating the relationship between the ontogenesis of a physiological function and its potential disorders.
Ecosystemic Complexity
- Develop observation and experimental systems for the reconstruction of the long-term dynamics of ecosystems
- Model the relationships between biodiversity, functioning and dynamics of the ecosystems
- Associate integrative biology and ecology to decipher evolutionary mechanisms
- Simulate virtual landscapes (integration and coupling of biogeochemical and ecological models into dynamic landscape mock-ups)
- Design decision-support systems for multifunctional ecosystems
From individual cognition to social cognition
- Individual cognition, cognitive constraints and decision processes
- Modeling the dynamics of scientific communities
- Society of the Internet, Internet of the society
Individuals, structures, societies
- Emergence of collective behavior in biological populations
- Co-evolution of individuals and society
- Co-evolution of individuals, structures and territories
- Heterarchies, multiscale organisations
Innovation, learning and co-evolution
- Understanding the dynamic conditions of innovation
- Modeling innovations and their rhythms
- Understanding the relation between cognition and innovation
Territorial intelligence and sustainable development
- Understanding territorial differentiation
- Towards a reflexive territorial governance
- Viability and observation of territories
Ubiquitous computing
- Local design for global properties
- Autonomic Computing
- New computing paradigms
- Specification of adaptive programming environments
Geosciences and the environment
- Understanding and reducing uncertainties
- Out-of-equilibrium statistical physics of the Earth system
Main questions
Formal epistemology, experimentation, machine learning
- Computer tools for exploration and formalization
- Computer assisted human interactions
Stochastic and multiscale dynamics, instabilities and robustness
- The cascade paradigm
- Random dynamical systems and stochastic bifurcations
- Phase transitions, emerging patterns and behaviour
- Space-time scaling in physics and biology
Collective behavior in homogeneous and heterogeneous systems
- Collective dynamics of homogeneous and/or heterogeneous units
- Collective dynamics in heterogeneous environments
- Emergence of heterogeneity and differentiation processes, dynamical heterogeneity, information diffusion
From optimal control to multiscale governance
- Extending the scope of optimal control
- Projecting complex dynamics into space of smaller dimension
- Projecting optimal control into high and multiscale dimensional space
- Extending the exploration/exploitation trade-off to governance analysis
- Co-adaptation of governance and stakeholder’s objectives
Reconstruction of multiscale dynamics, emergence and immergence processes
- Building common and pertinent conceptual frameworks in life sciences
- Achieving coherence in the modeling of complex systems
- Development of mathematical and computer formalisms for modeling multi-level and multi-scale systems
Designing artificial complex systems
- Using artificial complex systems for the understanding and regulation of natural complex systems
- Finding inspiration in natural complex systems for the design of artificial complex systems
- Design of Hybrid Complex Systems
Emergence in physics : Collective behavior and out of equilibrium fluctuations
- Self-organized and spatio temporal dynamics of complex matter
- Out-of-equilibrium fluctuations
- Metastable matter, slow relaxation and glassy dynamics
Theory and network measurement
- Structure and hierarchy, classification
- Formalism, measure and data
- Dynamics network : features, measure, proprieties and monitoring
- Exploration and visualization of complex networks
Disruption and robustness
- Analyze and describing (characterization) of transitory states in multiscale dynamics system
- Identification and validation of sensibility to the disruptions of systems and the models
- Role of variation and transition in emergence dans stability patterns
Information, thermodynamics and causality in complex systems
- How to identify and to describe information in complex system ?
- Causality in complex system : generality and specificity
- Source of multiple informations in complex systems : coexistence, cooperation, overlapping and competition ?
Prediction compared with understanding in complex systems
- Law and models
- Towards to thermodynamics of complex systems ?
The “complex systems” approach at the heart of contemporary science
A system is defined as “complex” when it is composed of numerous entities whose local interactions generate global properties that are difficult to predict through the knowledge of these entities’ properties alone. A swarm of birds, a social network, an anthill, a pedestrian flow or a neural network can be considered a complex systems.
The increasing interconnection and interdependence of the technological, economic, social and media networks of modern societies make their behavior as complex systems increasingly significant. This has been illustrated as much by the recent financial crises as by the cascading revolutions experienced by Arab countries or by the pan-continental failures that some technological networks, such as electricity grids, may have experienced.
Studying complex dynamic systems, whether natural (ecosystems, living organisms, climate, etc.) or man-made – built or influenced by man – (social, economic systems, cities, the Web, etc.), addresses issues relating to their functionality, organization and disorganization as a multi-scale system, development dynamics, resilience and viability.
These issues cut across many complex systems: the study of the disorganization of complex systems concerns many contemporary issues – climatic, ecological, societal – with which humans, at the heart of these multi-scale interactions, are confronted. Hence, understanding these phenomena is essential for good governance.
MEETING NEW CHALLENGES THROUGH AN INTERDISCIPLINARY APPROACH
Mastering the dynamics of complex systems is a major factor in societal innovation, with a large number of future and emerging technologies betting on decentralization and self-organization (block chains, autonomous car fleets, collective robotics, smart grids or smart grids, peer-to-peer technologies, devices for smart cities, etc.).
The study of these systems is interdisciplinary on two levels. First, the articulation of different levels of organization implies mobilizing several disciplines. Second, methodologies can be developed transversally for their study (e.g. complex dynamic network theory, percolation theory, agent simulation techniques, etc.).
Complex systems science is particularly focusing on the interactions between micro/meso/macro levels of organization and is using reconstruction and analysis methods capable of taking into account multiple scales of time and space. To this end, it mobilises the masses of data produced by these systems – which are fully involved in the Big Data phenomenon – and high-performance computing to model their behaviour. This scientific bias leads researchers confronted with complex systems to take up new and radical challenges, not only from a theoretical and methodological point of view, but also from a technological one.
RESEARCH AT ISC-PIF, A CROSSROAD BETWEEN INNOVATION AND SHS
The rise of information and communication technologies (ICTs) has revolutionized our relationship to the cultural productions of our societies by causing the migration of a large number of contexts of knowledge production to digital media – from informal discussions to the production of scientific theories and political debate. The entry of ICTs into the Web 2.0 era has opened up, among other things, many opportunities for research at the crossroads between innovation and social sciences.
Thanks to Big Data tools and methods derived from the analysis and modeling of complex systems, it is now possible to simultaneously extract, on a large scale, semantic contents and networks of agents present in the different spheres of ICT.
ISC-PIF is specialised in the development of tools that allow the analysis of complex systems such as socio-semantic networks and propose new modes of interaction with the scientific and cultural products of societies. These tools are called “social macroscopes”, in reference to the virtual tool described by Joël de Rosnay, which enables the observation of a complex system from an “external” point of view.
These macroscopes aim to help citizens, politicians, industrialists and scientists better understand our societies in the same way the microscope or telescope have helped us better understand living things and the Universe.
ISC-PIF Partners
