You are here: AU PhD  Graduate Schools Science and Technology Courses and how to sign up Scientific courses Computational modelling of complex interactions (2015)

Computational modelling of complex interactions (2015)

Name of course:

Computational modelling of complex interactions

ECTS credits: 5

Course parameters:

Language: English

Level of course: PhD Course

Semester/quarter: Q4 2015

Hours per week: 7

Capacity limits: 15 students

Objectives of the course:

The course objectives are to give the students an introduction to computer modelling of complex interactions with focus on applications in physics, engineering, economics, business, cognitive neuroscience, and evolutionary biology. The course consists of 6 one day workshops. Each workshop will introduce the core concepts of the research activities of the respective groups (, and how they are related to agent based modelling:

  • Agent based modeling in social sciences. (Oana Vuculescu)
    The aim is to provide an overview of how ABM can be used in social sciences and the general process of designing and using a model. We will be using NetLogo for illustration purposes, but  more complex (and complete) tools will also be introduced.
  • Analysis of multiple social networks with “multiplex”. (Antonio Rivero Ostoic)
    Multiple networks are complex systems with different types of relations in a shared structure, and they serve to address a variety of topics in human and other societies. “multiplex” ( is computer program written in R that is especially designed for the analysis of multiple networks with techniques that combine algebraic structures like the partially ordered semigroup with the existing relational bundles found in multivariate structures.
  • Agent based modeling with game theory. (Julia Nafziger and Alexander Koch)
    The aim of this workshop is to give the foundation for the “micro” behavior of agents by introducing the core concepts of non-cooperative game theory. We introduce static and dynamic games with complete and incomplete information and the corresponding solution concepts such as (subgame perfect) Nash equilibrium, and (perfect) Bayesian Nash equilibrium. For each tool, we consider applications from e.g. economics, political science or psychology. We also discuss relaxing the assumption of perfect rationality and self-interest.
  • Evolutionary dynamics of model agents faced with socio-economic dilemmas and other game theoretical interaction models. (Lars Bach, Dan Mønster and Andreas Roepstorff)
    In this workshop we will deal with examples of agents ranging from simple no-history, hardwired and unconditionally behaving agents in simple interaction scenarios towards more cognitively competent and adaptive agents basing their choices on own and other’s history of actions and possibly expectations of the social system in which they are situated. Some basic notions from evolutionary game theory will be discussed.
  • Agent based modeling in engineering. (Martin Greiner)
    In this workshop we will discuss several examples of complex engineering systems: self-organization of network structure and communication traffic in wireless multihop ad hoc communication networks, network creation games, model-based and game-theoretic wind-farm optimization, optimized design and optimized backup operations in renewable energy networks.
  • Agent based modeling for quantum optimization problems. (Jacob Sherson)
    In this workshop participants will be introduced lightly to the quantum problems attacked in our group dealing with challenges in the development of a quantum computer. We approach the problems from two perspectives. First, we have developed online games allowing users of the internet to help solving the problems, and secondly we develop agent-based simulations to optimize as efficiently as possible in the complex optimization landscape.

At the end of the course participants in the course will be assigned an internship in one of the groups for a whole week.

Learning outcomes and competences:

  • Create a hypothesis of player behaviour in a citizen science game.
  • Generate a model for agents behaviour in agent-based optimization.
  • Compare player and agents behaviour.
  • Formulate real world problems as proper formal game forms, solve and analyze them.
  • Reflect upon the information structure and equilibrium concept of a problem.
  • Generalize the models to real world environments.
  • Reflect on the challenges of modeling heterogeneous agents.
  • Be familiarized with different frameworks for modeling heterogeneous agents, their strengths and weaknesses.
  • Think critically about different approaches to model calibration.
  • Be familiarized with basic simulation data analysis techniques and parameter sweeps.
  • Reflect on the internal tensions of socio-economic dilemmas.
  • Applications and impact of evolutionary notions.
  • Generate a model sketch for iterated games with generation turnover.


Compulsory programme:

Active participation in every part of the course.

Course contents:

Agent based modeling, particle swarm optimization, crowd sourced quantum problem solving, non-cooperative game theory, human computing, modeling of heterogeneous agents, analysis of multiple networks


Name of lecturer:

Jacob F. Sherson (Responsible)

Type of course/teaching methods:

7 hours of workshops will be held one at each of six of the participating groups in the CMCI network.
In the final week of the course the student will have an internship in one of the participating groups, in which they will complete a project.


Handouts at the workshops

Course homepage:

Course assessment:

Approval of presentation of internship project.


Department of Physics and Astronomy

Special comments on this course:

In the case of partial participation in agreement with ph.d supervisor and lecturer reduced credit can be obtained.






Register with Mads Kock Pedersen (

Comments on content: 
Revised 20.06.2016