1. Future & Emerging Technologies in the Information Society Technologies programme of European Commission Future & Emerging Technologies in the Information Society Technologies programme of European Commission Pekka Karp Ralph Dum Walter van de Velde Simulation of emergent properties in Complex Systems

2. Outline: Towards an expansionist view on systems science Drivers from science and engineering Drivers from science and engineering –Beyond centralised top-down design and single flow control Inspired by natural and social systems Inspired by natural and social systems –Decentralised Design  1 st call on Complex systems in FET Computational science and computational engineering Computational science and computational engineering –Convergence of computation with science and engineering –Simulation is central to scientific discovery and systems engineering Towards an expansionist view of systems science Towards an expansionist view of systems science –Need for a systematic understanding of emergence –Focus on system structure rather than components  This call on simulation of emergent properties of CS  This call on simulation of emergent properties of CS

3. System design near Complexity barrier Radical increase in number of system components Radical increase in number of system components –Car industry: 14 system layers 20.000 parameters ….. –Top-down design (divide and conquer) is computationally hard – –A posterior testing replaces a priori verification Components act autonomously in dynamic environments Components act autonomously in dynamic environments –Need for decentralised control –Components can be unpredictable and unreliable ‘Can we control the system without fully controlling the components?’

4. Data in natural and social science: Too much and yet too little Understand how components interact and form dynamic networks sustaining system functionality Understand how components interact and form dynamic networks sustaining system functionality Causal relations in networks e.g. in biology depend strongly on system topology  network causality Causal relations in networks e.g. in biology depend strongly on system topology  network causality Incomplete data Incomplete data novel tools for probabilistic reasoning novel tools for probabilistic reasoning “Even if we understood the system components: How does the system work? “

5. Inspired by natural and social systems (1) In natural and social systems, components achieve collectively: Reliability/robustness Plasticity Scalability Large scale artificial systems Societies Life – Eco-Systems Many heterogeneous, autonomous interacting parts Decentralised approach to system science

6. Inspired by natural and social systems (2) ‘Expansionist’ approach to system science A systematic approach to hierarchical structure is missing Systems can organise in absence of central control Systems can organise in absence of central control Systems organise in complex hierarchic structures Systems organise in complex hierarchic structures Systematic studies of emergent system behaviour via models that integrate descriptions on various levels of aggregation and multiple temporal/spatial scales

7. Convergence of computing with science and engineering Computational science Computing - Simulation Computational design Simulation to bridge different levels of description Multi-scale simulation integrated for a whole system view Simulation complements testing, prototyping, and experiment Opportunities for expansionist system approach

8. Three pillars of this call Expansionist system view Mathematics Computing Simulation CS in Science - Engineering Simulation allows to connect different levels of description Multi-scale Modelling for a hierarchical system Bayesian reasoning Dynamical systems Formal language

9. Objectives summarised Mathematical framework for hierarchical systems Scalable computational modelling and inference tools Mathematical framework for hierarchical systems Scalable computational modelling and inference tools Inference of system models from the dynamic laws governing the interaction structure of components Inference of system models from the dynamic laws governing the interaction structure of components Develop design strategies for aggregate behaviour to obtain reliability and predictability in presence of uncertainty or in absence of full control at component level Develop design strategies for aggregate behaviour to obtain reliability and predictability in presence of uncertainty or in absence of full control at component level

10. Research Priorities Hierarchic systems: Hierarchic systems: –How to describe systems acting on multiple scales? –Formal languages for hierarchic model description –Understanding of architecture and functionality of networks Dealing with incompleteness and uncertainty Dealing with incompleteness and uncertainty –Tools for probabilistic modelling and reasoning –Bayesian techniques, evolutionary techniques….. Integrated modelling environments Integrated modelling environments –Integrate simulations on different levels/ scales (model embedding) –Integrate data acquisition and modelling

11. Submission by 21st September 2005 at 17:00 Submission by 21st September 2005 at 17:00 Evaluation session: 24th – 28th October 2005 Evaluation session: 24th – 28th October 2005 Start of projects: early summer 2006 Start of projects: early summer 2006 Size of consortia: typically 4-8 partners Size of consortia: typically 4-8 partners Funding: typically 1.0MEuros - 2.5MEuros Funding: typically 1.0MEuros - 2.5MEuros Duration: 3 years Duration: 3 years 2nd European conference on CS, Paris, Nov. 14th -18 th 2nd European conference on CS, Paris, Nov. 14th -18 th cordis.lu/ist/fet/co.htm complexityscience.org cordis.lu/ist/fet/co.htm complexityscience.org Administrative issues and information