Model
Make relationships and assumptions explicit.
Complex systems, made intelligible
Understanding complexity.
Exploring what comes next.
CCISSM is a consulting and research office dedicated to complex systems modelling, artificial intelligence and simulation.
Complexity is not noise.
It is a structure waiting to be understood.
CCISSM
CCaseau
CComplexity
ISIntelligent Systems
SMSimulation & Modeling
01 — Why CCISSM exists
Better decisions begin with a better model of the world.
Climate, organisations, technology and society are not collections of isolated problems. They are living systems shaped by feedback loops, delays and adaptation.
CCISSM exists to make those dynamics visible. We combine formal models, simulation and artificial intelligence to explore assumptions, test possible futures and turn uncertainty into useful knowledge.
Simulate
Explore trajectories that intuition alone cannot reveal.
Learn
Compare results with reality and improve the model.
02 — A continuous practice
Knowledge grows in loops,
not straight lines.
Modelling is a disciplined conversation with reality. Each pass makes our assumptions clearer and our questions sharper.
Step 01
Observe
Look closely at behaviours, signals and context.
03 — The founder
“The model is not the answer. It is a way to ask better questions.”
Yves Caseau
Scientist · executive · modeller
Yves Caseau has worked for more than two decades on complex systems modelling and evolutionary game theory, with applications ranging from telecommunications and information systems to smart grids and climate–economy models.
A member of France’s National Academy of Technologies, he has served on the scientific councils of EDF, Inria and IRT SystemX, taught at École Polytechnique and held senior technology and digital leadership roles at Michelin, AXA and Bouygues Telecom.
04 — Shared language
A field guide to complexity.
Seven ideas that shape how CCISSM sees, models and explores the world.
01Complex system
A system whose overall behaviour emerges from many interacting parts, feedback loops and changing relationships.
02Artificial intelligence
Methods that enable machines to perceive patterns, learn from data, reason and support decisions—often as part of a wider human system.
03Simulation
The use of an executable model to explore how a system may behave under different assumptions, events or decisions.
04Evolutionary simulation
A simulation in which strategies, agents or populations adapt over time through selection, learning and interaction.
05System dynamics
A modelling approach focused on stocks, flows, delays and feedback loops to understand behaviour over time.
06Discrete-event simulation
A method that represents a system as a sequence of events—each changing its state at a particular point in time.
07Differential state equations
Equations that describe how state variables change continuously, forming a mathematical foundation for dynamic models.