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Diana Mangalagiu Reims Management School, France Institute for Scientific Interchange Foundation, Italy My background. Engineer, Polytechnic University, Bucharest, Romania (1992) MSc in Microelectronics, University of Strasbourg, France (1994)

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Diana Mangalagiu

Reims Management School, France

Institute for Scientific Interchange Foundation, Italy

my background
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
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
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

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

International Center of Excellence on Complex Systems

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)

I.S.I. Foundation

  • 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


General Integration of the Applications of Complexity in Science

NEST Coordination Action



  • 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

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




WP4 New EU member states

WP5 Female Scientists

WP11 Connecting research and PhD programs


WP9 Coordination of Data Bases

WP10 Electronic CoordinationWP12 European Complexity SocietyWP13 Experts’ Report


WP1 Politicians, Media, Business leaders

WP2 Economic Policy

WP3 Industry


WP6 Think Tanks

WP7 Schools/ Courses

WP8 Conferences

WP14 Management

WP15 Assessment and Evaluation




Starlings in flight, understanding patterns of animal group movements

Termini railway station, Rome

Evening roosting time, November 2004


STARFLAG Objectives

  • Understandingwhat are the rules governing coordination and what are
  • the microscopic mechanisms that determine flocking pattern-formationin starlings.
  • Starling flocks are a perfect example of collective 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.

Extreme Events: Causes and Consequences (E2-C2)

  • 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

E2-C2 Summary & Key Ideas

  • 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.
cooperation with china
Cooperation with China

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

Associate researcher, ISI Foundation


  • Statistical Physics of Information networks
  • Game theory and interacting competing complex systems
  • Physics approach to modeling economic processes: modeling financial systems
cooperation with china1
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
cooperation with china2
Cooperation with China

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
china india europe triangular meeting
China-India-Europe triangular meeting
  • 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