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Multi-Actor Systems

Head of Department

Multi-Actor Systems

Welcome to the Multi-Actor Systems Department

At the Multi-Actor Systems (MAS) Department, we are interested in complex governance issues in technology- dominated world. Complexity for us means:

  • The technological and/ or physical system, e.g. the electricity system or the system of rivers, are large scale and there are uncertainties: an unambiguous modeling or understanding of these systems is impossible.
  • These systems are embedded in or connected with other systems - there are systems of systems.
  • There are many actors involved, resulting in coordination and cooperation issues.
  • These actors often have different interests and values and behave strategically, in order to maximize their own interests.
  • These actors are interdependent, embedded in networks of interdependencies. They have different interests, but they also need each other's support.
  • These multi-actor systems are inherently dynamic: they continuously change. A governance arrangement that is new today, might be obsolete tomorrow.

Understand, Analyze, Design

We want to understand these multi-actor systems, we analyze and model these systems and develop methods to deal with their complexity.
We design interventions for decision makers in multi-actor systems - incentive schemes, rules, simulations and games, strategies for playing the political game.
We design systems to improve the coordination between the many actors, enabling and supporting emergence in large scale environments such as energy management, or supply networks.

Application

We apply this in various domains, including water management, smart grids, logistics systems, global warming, circular economics, large infrastructural projects, crisis management and cyber security.

Themes

  • The players and the game: What actors are involved in governance? What incentives influence them? What strategies do they employ? What collective outcomes do their individual strategies jointly produce? How might one intervene in a network of actors?
  • Deep uncertainties: How can we help decision-makers manage deep uncertainties - inherently unpredictable developments? What modelling techniques can be used for this purpose? How do we ensure that important knowledge reaches decision-makers and why does this often go wrong in multi-actor environments?
  • IT-enabled design and coordination: Sometimes the nature of the system forces actors to adapt their behavior to one another, which raises issues of coordination. Can actors’ behavior be coordinated to streamline emergence? Can this coordination burden be automated? We design systems that improve coordination between actors and harness their data to enhance decision-making.

For a more detailed description of the departments research, click here.

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