Future & Emerging Technologiesin the InformationSocietyTechnologies programme of European Commission

Future & Emerging Technologiesin the InformationSocietyTechnologies programme of European Commission

Pekka KarpRalph Dum

Walter van de Velde

Simulation of emergent properties

in Complex Systems

Outline: Towards an expansionist view on Outline: Towards an expansionist view on systems sciencesystems science

Drivers from science and engineeringDrivers from science and engineering– Beyond centralised top-down design and single flow controlBeyond centralised top-down design and single flow control

Inspired by natural and social systemsInspired by natural and social systems– Decentralised Design Decentralised Design 1 1stst call on Complex systems in FET call on Complex systems in FET

Computational science and computational engineeringComputational science and computational engineering– Convergence of computation with science and engineeringConvergence of computation with science and engineering– Simulation is central to scientific discovery and systems engineeringSimulation 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 Need for a systematic understanding of emergence – Focus on system structure rather than componentsFocus on system structure rather than components

This call on simulation of emergent properties of CSThis call on simulation of emergent properties of CS

System design near Complexity barrierSystem design near Complexity barrier

• Radical increase in number of system componentsRadical increase in number of system components– Car industry: 14 system layers 20.000 parameters …..Car industry: 14 system layers 20.000 parameters …..

– Top-down design (divide and conquer) is computationally hard Top-down design (divide and conquer) is computationally hard

– A posterior testing replaces a priori verification

Components act autonomously in dynamic environmentsComponents act autonomously in dynamic environments – Need for decentralised controlNeed for decentralised control– Components can be unpredictable and unreliable Components can be unpredictable and unreliable

‘‘Can we control the system Can we control the system

without fully controlling the components?’without fully controlling the components?’

Data in natural and social science: Data in natural and social science: Too much and yet too littleToo much and yet too little

• Understand how components interact and form Understand how components interact and form dynamic networks sustaining system functionalitydynamic networks sustaining system functionality• Causal relations in networks e.g. in biology depend strongly Causal relations in networks e.g. in biology depend strongly

on system topology on system topology network causality network causality

• Incomplete dataIncomplete data• novel tools for probabilistic reasoningnovel tools for probabilistic reasoning

““Even if we understood the system components: Even if we understood the system components:

How does the system work? “How does the system work? “

Inspired by natural and social systems (1)

In natural and social systems, components achieve collectively:•Reliability/robustness•Plasticity•Scalability

Large

scale

artif

icial

syste

ms

Societies

Life – Eco-Systems

Many heterogeneous, autonomous interacting

partsDecentralised approach to system science

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 controlSystems can organise in absence of central control• Systems organise in complex hierarchic structuresSystems 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

Convergence of computing with science and engineering

Compu

tatio

nal

scien

ce

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 experimentOpportunities for

expansionist system approach

Three pillars of this callThree 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

Objectives summarisedObjectives summarised

Mathematical framework for hierarchical systems Mathematical framework for hierarchical systems Scalable computational modelling and inference toolsScalable computational modelling and inference tools

Inference of system models from the dynamic laws Inference of system models from the dynamic laws governing the interaction structure of componentsgoverning the interaction structure of components

Develop design strategies for aggregate behaviour to Develop design strategies for aggregate behaviour to obtain reliability and predictability in presence of obtain reliability and predictability in presence of uncertainty or in absence of full control at component uncertainty or in absence of full control at component level level

Research PrioritiesResearch Priorities

Hierarchic systems:Hierarchic systems:– How to describe systems acting on multiple scales? How to describe systems acting on multiple scales? – Formal languages for hierarchic model descriptionFormal languages for hierarchic model description– Understanding of architecture and functionality of networksUnderstanding of architecture and functionality of networks

Dealing with incompleteness and uncertaintyDealing with incompleteness and uncertainty– Tools for probabilistic modelling and reasoningTools for probabilistic modelling and reasoning– Bayesian techniques, evolutionary techniques…..Bayesian techniques, evolutionary techniques…..

Integrated modelling environmentsIntegrated modelling environments– Integrate simulations on different levels/ scales (model Integrate simulations on different levels/ scales (model

embedding)embedding)– Integrate data acquisition and modellingIntegrate data acquisition and modelling

Submission by 21st September 2005 at 17:00Submission by 21st September 2005 at 17:00Evaluation session: 24th – 28th October 2005Evaluation session: 24th – 28th October 2005Start of projects: early summer 2006Start of projects: early summer 2006Size of consortia: typically 4-8 partners Size of consortia: typically 4-8 partners Funding: typically 1.0MEuros - 2.5MEurosFunding: typically 1.0MEuros - 2.5MEurosDuration: 3 yearsDuration: 3 years2nd European conference on CS, Paris, Nov. 14th -182nd European conference on CS, Paris, Nov. 14th -18 thth

cordis.lu/ist/fet/co.htm complexityscience.orgcordis.lu/ist/fet/co.htm complexityscience.org

Administrative issues and information