F uture & E merging T echnologies in the I nformation S ociety T echnologies programme of E uropean C ommission F uture & E merging T echnologies in the I nformation S ociety T echnologies programme of E uropean C ommission Ralph Dum Complex systems research funding in European Commission

Why funding complex systems research? More is different: Interactions determine system behaviour Social systems Living systems Large scale artificial systems From individual to collectives: The Internet - P2P systems The cell - The brain Stock market Culture - Norms Ant colonies - Ecosystems The climate Are there general organisational and functional principles for such systems? Can we design/build better functioning systems based on such understanding?

Why funding complex systems research? More is different: Interactions determine system behaviour Social systems Living systems Large scale artificial systems From individual to collectives: The Internet - P2P systems The cell - The brain Stock market Culture - Norms Ant colonies - Ecosystems The climate Are there general organisational and functional principles for such systems? Can we design/build better functioning systems based on such understanding?

Example of a FET funded project Self-organising semantics in embodied agents Three SONY AIBOs paying attention to an object Four assembled s-bots Interacting swarm-bots Can ‘human-like’ language arise in communicating embodied robots ? What are the prerequisites for developing communication? Grammar  combinatorial systems FET project: ECAGENT

Example of FET funded projects: Internet -unbounded world of information ‘The Internet is the most complex artificial computational artefact ever created by man. It evolves in absence of any central control guided by the economic interests of participants’ (Papadimitriou 2001) Internet is ever growing: Nodes are constantly added and removed An Expanding universe FET projects: EVERGROW DELIS

Funding of complex systems: EC framework/national programmes EC Funding within ICT (directorate for information & media) EC Funding within ICT (directorate for information & media) 40Million Euros – FET EC Funding outside ICT (directorate for research) EC Funding outside ICT (directorate for research) 35 Million Euros - NEST National funding: EPSRC (10Mpounds), CNRS…… National funding: EPSRC (10Mpounds), CNRS…… Coordination activities: ONCE-CS.net Coordination activities: ONCE-CS.net Outlook EC 7 th framework programme: Complex systems science for socially intelligent IT Complex systems science for socially intelligent IT Complex systems ideas taken up in many areas Complex systems ideas taken up in many areas

What does all this have to do with economics ? IT drives a novel set of transformations: What consequences for society and business? Convergence of Systems Transportation The ‘physical’ Internet The Internet Information unbound Economy Eco system Sustainability Power grids The ‘energy’ Internet Economy Media

From Individual to Mass and back ‘Social computing’ or Anarchy -From centralised media/telecoms to P2P participatory Media -Online diversity versus cultural diversity Societies of minds Are Wikipedia, Google, e-bay … ‘social’ cognitive tools? Mass self-communication Do blogging, podcasting … lead to ‘after Gutenberg’ era?

Towards an information science of social processes Bridging human and computational processes Humanities Social sciences Economics Computer science Data + Models + Computation Agent-based/ behavioural economics AI Interfaces Formal models Services Services in the large: How to manage millions of online services?

Thank you Annexes

Annexe:Three signatures of complexity Emergence: Self-organisation - Collective behaviour Multiple levels of description & operation: ‘micro to macro’ ‘Complex systems often show simple global behaviour’ Quarks  Bulldozer Input and output not proportional: ‘small change -big effect’ Positive Feedback loops Extreme events dominate the mean: cascading failures, crashes… Existence of multiple equilibria: Systems can evolve along several paths – no global optimum Systems can evolve along several paths – no global optimum Role of prediction? (experiment versus simulation) Role of prediction? (experiment versus simulation) Sensitivity to parameters ‘More is different’ ‘Pool of possibilities’