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Science of complex systems for socially intelligent ICT

Science of complex systems for socially intelligent ICT. Overview of background document Objective IST-2007.8.4 FET proactive David Hales University of Bologna, Italy. Formulation. Result of wide consultation

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Science of complex systems for socially intelligent ICT

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  1. Science of complex systems for socially intelligent ICT Overview of background document Objective IST-2007.8.4 FET proactive David Hales University of Bologna, Italy Dresden, CRP @ ECCS’07 06/10/07

  2. Formulation • Result of wide consultation • O. Babaoglu, C. Barret, P. Bourgine, Ch. Diot, A.E. Eiben, D. Floreano, D. Hales, D. Helbing, A. Hoekstra, J.H. Johnson, S. Kirkpatrik, M. Morvan, C. Nikolau, N. Packard, M. Schoenauer, P. Sloot, L. Steels, A. Vespignani... and many others • Apologies if a name is not listed... Dresden, CRP @ ECCS’07 06/10/07

  3. Basic Idea • Application of Complex Systems approach for designing, understanding and modelling... • Socially Intelligent ICT Dresden, CRP @ ECCS’07 06/10/07

  4. Socially Intelligent ICT? • ICT composed of many interacting parts • Semi-autonomous, possibly diverging goals • Include humans in-the-loop • Required to coordinate and cooperate socially to achieve collective goals • Socially Intelligent ICT facilitate this through mechanisms of social coordination Dresden, CRP @ ECCS’07 06/10/07

  5. Why Now? • Emergence of global scale distributed ICT as major application domain • Massive (10m’s) • Distributed (lack of central control) • Open (unknown new behaviours) • Increasing use of ICT to mediate, create and enable communities (techo-social communities) Dresden, CRP @ ECCS’07 06/10/07

  6. Why Now? • Recent example applications: • Social networking • Wiki-based content creation • Social tagging • Peer-to-Peer systems • Compare: Social software (Web 2.0) Dresden, CRP @ ECCS’07 06/10/07

  7. Why Complexity Science? • Maturing body of work providing: • Scientific results: empirical & analytic • Tools: models, formalisms, measures • Methodology: simulation, analysis • For modelling, predicting and designing complex adaptive systems • Can help where traditional engineering approaches struggle Dresden, CRP @ ECCS’07 06/10/07

  8. Why Complexity Science? • Many relevant domains – e.g: • emergent network structures • trust and cooperation • formation of sustainable communities • evolutionary economics • computational sociology • econophysics Dresden, CRP @ ECCS’07 06/10/07

  9. Why Complexity Science? • Provide alternatives to “rational action”: • Realistic models of user behaviour • Localised and noisy information • Bounded computation • Bounded rational models • Evolutionary models of behaviour • Psychology and experimental economics Dresden, CRP @ ECCS’07 06/10/07

  10. Vision and Challenges • Produce effective models of techno-social systems • Avoiding commons tragedies without central control • Controlling malicious behaviour & noise • Efficient reputation systems preserving privacy Dresden, CRP @ ECCS’07 06/10/07

  11. Three broad challenge areas • Theoretical and algorithmic foundations • Data-driven simulation • Prediction and predictability Dresden, CRP @ ECCS’07 06/10/07

  12. Theoretical and algorithmic foundations • techno-social systems are complex • required multi-level modelling • micro, macro and meso scales • previous models often focus only on: • micro or macro levels • micro to macro level relationships • need feedback from macro to micro • realistic diversity of behaviours and social and spatial structures Dresden, CRP @ ECCS’07 06/10/07

  13. Data-driven simulation • Social and economic models often ignore empirical data (abstract / theoretical) • Derive models of techno-social systems empirically - need tools and methods for: • collection and processing of huge noisy datasets to derive multi-level dynamic models • probing technologies in date rich environments • design of protocols and experiments (humans) • scalable and distributed knowledge extraction • Validation methods (not just curve fitting) Dresden, CRP @ ECCS’07 06/10/07

  14. Prediction and predictability • Complex systems and human systems are difficult to predict • But statistical signatures, patterns, can be found. For given systems: • what is the appropriate predictive level? • what are the limits to predictability? • what are the relationships between predictive, descriptive and theoretical / abstract models? • Understanding the limits of predictability valuable for engineering and design Dresden, CRP @ ECCS’07 06/10/07

  15. Vision and Challenges • Potential application domains: • massive ICT mediated service economies • ICT mediated communities • Peer-to-Peer systems • emergency and disaster relief systems Dresden, CRP @ ECCS’07 06/10/07

  16. Source Documents Background document: http://cordis.europa.eu/fp7/ict/fet-proactive/home_en.html Complexity Research Living Roadmap: http://complexsystems.lri.fr/Portal/tiki-index.php?page=Living+Roadmap Complex Systems Research in FP7 document: ftp://ftp.cordis.europa.eu/pub/ist/docs/fet/co-16.pdf See FET FP7 closed consultations at: http://cordis.europa.eu/ist/fet/id-fp6.htm Dresden, CRP @ ECCS’07 06/10/07

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