Aims and Scopes of Φ·SOE


The Φ·SOE (Physics of Socio-Economic Systems Division) aims at the application and progression of physical methods for the analysis, modeling, simulation and optimization of socio-economic systems.

We are always open for interesting fields. Some of the current subject areas of the Φ·SOE are:

  • Financial Systems:
    • Analysis of financial data (price fluctuations, scaling, correlations)
    • Models of interaction for stock markets
    • Forecast models for the performance of shares
    • Risk management, portfolio strategies
  • Economic Systems:
    • Nonlinear market dynamics, instability and robustness
    • Macroeconomic productions functions
    • Microeconomic models of interactions (e.g. agent-based models)
    • Innovation processes, business cycles
    • Conditions for economic growth
    • Economic agglomeration, sustainability
    • Material flows in networks
    • Multi-agent approaches to logistics
  • Urban Systems:
    • Urban growth, dynamics of population dynamics
    • Modeling of road and pedestrians traffic
    • Optimization of road and transport networks
    • Network structures, statistics and scaling laws of urban structures and supply systems
  • Social Systems:
    • Models of social interaction
    • Collective opinion formation
    • Coordination of decisions, voter behavior
    • Game theory, minority game
    • Group dynamics, crowd behavior
    • Convention and norms
  • Coping with Crises:
    • Disaster spreading
    • Interdependency analysis
    • Resilience
    • Disaster response management
    • Models of conflict
  • Network Theory:
    • Information and innovation networks
    • Social networks
    • Critical infrastructure networks
    • Logistic, production, and transport networks
  • Transfer of Methods:
    • Evolutionary algorithms, adaptive methods, multi-agent models, Monte-Carlo simulations, cellular automata
    • Time series analysis, multivariate statistics, statistical physics
    • Stochastic methods, random matrices
    • Neuronal networks, evolutionary dynamics
    • Nonlinear dynamics, self-organization, chaos control
    • Phase transitions, critical phenomena


Peter Felten