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Who are we ?
The XSYS – Toulouse Institute for Complex Systems Studies, brings together key players of Toulouse and the Occitanie Region with complementary expertises to build transversal methodological approaches in order to work on the inherent complexity of the key societal challenges.
In addition to the global renewal of the scientific approach, the XSYS has defined three primiray research topics closely aligned with the local resources and the significance of academic, societal and economic challenges at the regional level :
Collective Motion & Mobility
Adaptation to Risks
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What is a Complex System ?
A complex system is a group composed of multiple entities which interact with each other. From these relationships, a collective behavior arises that can not be explained by the properties of the individual components, this is referred to as “emergence”. « Complex » is different from complicated in the sense that a system comprising a large number of entities is not necessarily complex.
Complex systems may display multi-scale dynamics and cascade phenomena that are difficult to analyse, to understand and to predict, that are produced by non-linear feedback loops from the componenys on the overall system, or from stochastic impacts of the environment.
Complexity Science promotes the development of tools and methodologies to : analyse, describe, model, predict and control these complex systems.
(See the definition from the Complex Systems Society)
Why is complexity science emerging nowadays ?
The development of computer science and digital technologies that took place at the end of the 90’s as well as their fast and large-scale spread enabled scientistits to get a renewed access to the previously “confined” field of complex systems. Progresses in terms of computing power, storage capacity, process automation, interoperability of data now allow not only the creation and processing of large, extensive and dynamic databases but also the description and design of models and simulations of complex systems. Nowadays, studies focus on physical, biological and societal systems and can be related to various fields ranging from new digital social networks to systemical and computional biology, collective intelligence or logistics.
What are the relevant areas ? The targeted audience and markets ?
Complex systems science is interdisciplinary. The understanding of the object is a lock for research teams and in the meantime a development issue for public authorities (collectivities) and a preliminary requirement for the creation of innovative services and technologies for the econonic stakeholders.
What are the laboratories and institutes that contribute to complex systems science ?
International and european : There are several hundreds research centers and star-ups which main activity relates to complex systems. The Complex Systems Society is an animation platform of this community. The Santa Fe Institute in the United States or the ISI Fundation in Italy are some of the major references in complex systems science
National : Réseau National des Systèmes Complexes
Regional : More than 15 research units in Toulouse unités de recherche toulousaines address programs related to the field of complex systems studies (see Nos Partenaires).
What are the tools used to study complexity ?
The study of a complex system requires to mobilise expertises and tools for its description, its modeling, and its simulation. d’un système complexe nécessite de disposer de compétences et d’outils permettant sa description, sa modélisation et sa simulation. The issue is to define its perimeter, its individual entities as well as the multi-scale dynamic interactions that take place within it. Il s’agit de définir son périmètre, ses entités élémentaires, les interactions dynamiques multi-échelles dont il est le siège. Depending on the questions, another issue will be to decide which tools should be used whether relevant existing ones or new ones.
The latest tools lie at the intersection of statistics, computer science, graph theory or physics (machine learning, statistical learning, data mining, random network, agent-based models, …), and mobilise concepts from biology or sociology (multi-scale, community, self-organisation, weak and strong ties, …).