1 / 16

Science of complex systems for socially intelligent ICT

An overview of the background document on the application of complexity science in designing, understanding, and modeling socially intelligent ICT systems. The document discusses the challenges and potential applications of this approach.

adank
Download Presentation

Science of complex systems for socially intelligent ICT

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related