Diana Mangalagiu

Reims Management School, France

Institute for Scientific Interchange Foundation, Italy

diana.mangalagiu@reims-ms.fr

diana@isi.it

My background

Engineer, Polytechnic University, Bucharest, Romania (1992)

MSc in Microelectronics, University of Strasbourg, France (1994)

PhD in Artificial Intelligence, Ecole Polytechnique, Paris (1999)

MSc in Management, University of Sorbonne, Paris (2000), in Sociology, University of Sorbonne, Paris (2001)

Reader, HEC School of Management, Paris

Manager, Centre for Central and Eastern European Studies

Expert, World Bank, EU and UN agencies

Higher education reform, collaborative learning, entrepreneurship capabilities and business incubators

development, (Argentina, Brazil, Chile, Kazakhstan, Latvia, Lithuania, Moldova, Poland, Romania, Russia,

USA)

My research interests

Social and spatial interactions between socio-economic agents (individuals, firms or groups of economic entities)

Diffusion / contagion / segregation phenomena

Organizational dynamics: co-evolution of hierarchy and informal networks in organizations

Corporate social responsibility

Market dynamics: social influence on real estate price dynamics

Innovation

Reims Management School

Founded in 1928

Master, Bachelor, MBA, PhD, Executive Education programs

3500 students

25% of the students from abroad

15 years of collaboration with China (Fudan, Tsinghua)

Double Degree Bachelor and Master programs (Fudan)

MBA students exchange (Tsinghua)

PhD students

I.S.I. (Institute for Scientific Interchange) Foundation

International Center of Excellence on Complex Systems

http://www.isi.it

Four divisions:

Epidemiology and Life Sciences Division (11 researchers)

Multi-Agent Systems Division (20 researchers)

Quantum physics Division (7 researchers)

Statistical Physics Division (16 researchers)

Epidemiology and Life Sciences Division

Environmental Epidemiology

Genetic susceptibility to chronic disease

Development of biomarkers

Bioethics

Multi-Agent Systems Division

Biological systems

Financial and real estate markets

Socio-economic systems

Quantum physics Division

Properties of matter at the microscopic scale

Quantum algorithms

Statistical Physics Division

Computational Neuroscience

Combinatorial optimization algorithms, medical imaging

I.S.I. Foundation

Objectives:

Coordinate activities of the Complexity Pathfinder in NEST

Support recruiting and rising of the young researchers

Discover, connect and transfer complexity information

Find, catalogue, rank and present relevant activity

Initiate and coordinate new complexity research

GIACSGeneral Integration of the Applications of Complexity in Science

NEST Coordination Action(http://www.giacs.org/)

Biophysics Biochemistry and Informatics

Brain, respiration and cardiaccausalities in anaesthesia:BRACCIAAneta StefanovskaExperimental measurement,time series analysis andmathematical modeling ofanaesthesia.

Complexity: Agents, Volatility,Evidence and Scale: CAV ESScott MossModelling procedures for theformation of social policy inconditions of uncertainty.

Financial Markets and Complexity:Uncertainty, Heterogeneous MicroAgentsand Aggregate Outcomes:ComplexMarketsMark Salmonrisk , welfare and stability, growth,efficient resources and informationtransfer. Financial phenomena:volatility excess, crashes, speculativebubbles, departures from equilibrium

Common Complex CollectivePhenomena in StatisticalMechanics, Society , Economics,and Biology: Co3Sorin SolomonAdaptation of autocatalyticfluctuations to noise.Localized objects with complexadaptive properties not explained byPDE distinction between the typicaland the average behavior; rare events

Starlings in flight: understandingpatterns of animal groupmovements: StarFlagGiorgio ParisiLarge number of heterogeneousagents that interact exchanginginformation-> pattern formation

Measuring and ModellingComplex Networks AcrossDomains: MMCOMNETFelix Reed-TsochasUnified and cross-disciplinaryunderstanding of the behaviourand functional properties ofcomplex networks: BiologicalSupply chains, High-techinnovation,

Critical Events in EvolvingNetworks CREENJanusz Holystscientific community and itsepidemic-like behaviour (scientificavalanches).Spreading of information inscientific and publiccommunication networks.

Emergent organisation in complexbiomolecular systems EMBIORobert GlenAssimilate molecular data fromsources world-wide. Linking andanalysing data - extract knowledge -predict properties.

Extreme Events: Causes and Consequences: E2-C2 (Michael Ghil)Extract the distribution of these events from existing data sets.Reproduce the data-derived distribution of events. Predict thelikelihood of extreme events in prescribed time intervals.

Interacting Agents and Markets Networks and Social Self-organization

Human behaviour ThroughDynamics of Complex SocialNetworks : and InterdisciplinaryApproach: DYSONET PanosArgyrakisStatistical Physics concepts ->panic, search, traffic, humanrelationship,epidemics, Economicsand Finance, and EnvironmentOptimization principle?

Unifying Networks for Science& Society: UniNet(Markus Kirkilionis)Links among entities on differentscales.Reinterpretation of transferredtheories in the context of differentapplications. Math.“unification of theoryfeedback cycle”

Collaborative Complexity – Collaborations as Complex SystemsCOLL-PLEXITY (Gunther Schuh, Aaachen)Production Industry. Failure Rate. Control networks system analysis.Individual companies as individuals in a network .From GenericNetwork models to problem-to-system match. Dynamical adaptivecollaborations networks

.Complexity and evolution of photonic nanostructures in bio-organisms: templates for material sciences. BIOPHOT (Jean Pol Vigneron). Physical explanation for biological complexity.Use of light scattering by living organisms

MEETING INSTRUMENTS

WP6 Think TanksWP7 Schools/ CoursesWP8 Conferences

CONNECTION TO THE “OUTER WORLD”

WP1 Politicians, Media,

Business leadersWP2 Economic Policy WP3 Industry

INPUT OF YOUNG RESEARCHERS

WP4 New EU member states WP5 Female ScientistsWP11 Connecting research and PhD programs

COORDINATION AND SELF_ORGANIZATION

WP9 Coordination of Data BasesWP10 Electronic CoordinationWP12 European Complexity SocietyWP13 Experts’ Report

WP14 ManagementWP15 Assessment and Evaluation

GIACS

STARFLAGSTARFLAG: : Starlings in flight, understanding patterns of animal group movements

Termini railway station, RomeEvening roosting time, November 2004

Understanding what are the rules governing coordination and what are the microscopic mechanisms that determine flocking pattern-formation in starlings.

Starling flocks are a perfect example of collective phenomenacollective phenomena, occurring in:

Physics: ordering transitions (ferrmagnetic, liquid/gas, superconductivity etc.)

Biology: bacteria, blood cells, insects swarms, fish schools, etc. Investigate flocking behaviour in terms of vigilance, antipredatory patterns, selection of safe

landing/food rich sites Understand brain mechanisms controlling social behaviour. Compare field data with laboratory observations.

Robotics: distributed autonoumous robots, swarm intelligence

Economics: panic events, herding behaviour in financial markets etc. Improve the herding benchmark in economics exploiting insights from flock models. Understand the contribution of collective effects (herding/feedback) on prices. Provide indications for regulatory strategies.

STARFLAG ObjectivesSTARFLAG Objectives

Researchers in mathematics, physics, environmental and socio-

economic sciences

€1.5M over three years (March 2005–Feb. 2008)

Coordinating institute: Ecole Normale Supérieure, Paris, France

17 partners in 9 countries

72 scientists + 17 postdocs/postgrads

Belgium France Germany Italy Luxembourg Romania Russia UK USA

Extreme Events: Causes and Consequences (E2-C2)

Extreme events, a key manifestation of complex systems. Describe, understand & predict extreme events. Combine expertise in complex systems with broad knowledge in the natural and social

sciences.

Main study areas include: Natural disasters (earthquakes, wildfires, landslides, climatic extremes, etc.) Socio-economic crises Interaction between economic & climatic changes

Six scientific work-packages bridging the natural and social sciences.

Expected outcomes include: Validated data sets Novel insights Forecast algorithms

Techniques used: Frequency-size distributions for natural hazards probabilistic hazard forecasting Pattern recognition of precursor clustering + simple-model understanding help beat purely

probabilistic prediction. Simple models (ODEs, cellular automata, and BDEs) can help us understand and predict complex

interactions in 'real' systems.

E2-C2 Summary & Key IdeasE2-C2 Summary & Key Ideas

Prof. Yi-Cheng Zhang, The Interdisciplinary Physics Group, University of Fribourg, Switzerland

Associate researcher, ISI Foundation

Topics: Statistical Physics of Information networks

Game theory and interacting competing complex systems

Physics approach to modeling economic processes: modeling

financial systems

Cooperation with China

Cooperation with China

Professor Yu Lu, Institute of Theoretical Physics &

Interdisciplinary Center of Theoretical Studies,

Chinese Academy of Science, Beijing

Strongly correlated systems and low-dimensional condensed

matter physics

Physical and mathematical issues in superstring theory,

applications to cosmology

Interactions and modeling in living systems

Quantum information physics

Prof. Shigang He, Institute of Biophysics, Chinese Academy of Science, Beijing

Research on complex biological systems Example: the rat retina

Prof. Meiqi Fang, Economic Science Lab, Renmin University of China

Research in complexity at ECOLAB

Cooperation with China

March 2006

Main aims of the meeting: Foster a high level scientific encounter of Chinese, Indian

and European scientists and science leaders

Identify areas of common interest and designing

agreements of cooperation

Promote joint research projects in the area of complexity

and complex systems

Particular attention to inter and multi-disciplinary projects

China-India-Europe triangular meeting