The world around us seems to be becoming more complex and uncertain every day. Complexity often comes from multi-actor complexity, inherent non-linearity of most phenomena, and system complexity: Multi-actor complexity refers to the complexity caused by many actions of – and interactions between – actors and organizations. Inherent non-linearity refers to the fact that most phenomena do not behave nicely and linearly. System complexity refers to how complex systems, processes, and information and incentive structures are set up and interact. This complexity and uncertainty seems to suggest that the future is inherently unpredictable. This is not true, in the contrary, good systems modelling helps to design systems and prepare organizations for the uncertain future.
The PEAS Center is specialized in modelling complex issues and simulation under deep uncertainty. Modelling and simulating is extremely useful for exploring a plethora of futures and for designing robust policies. System modeling refers here to quantitative modeling of complex systems. System Dynamics (SD), Agent-Based (ABM), Discrete Event (DES) are three commonly used system modeling methods: ABM models describe characteristics of and rules to which individual agents act; DES models are constructed from simulable chance processes from which detailed processes appear to be constructed; SD models summarize complex systems with the help of equations, whereby individuals with the same characteristics are brought together in cohorts and treated as cohorts instead of individuals. The PEAS Center predominantly develops and uses SD models, however, different kind of models are possible.