Prof. Volker Grimm

Invited talk at SSC2016 by Prof. Volker Grimm, Helmholtz Centre for Environmental Research, Leipzig, University of Oxford.

Abstract:

The concept of “Complex Adaptive Systems“ (CAS) has intrigued scientists for more than two decades. It contains the promise of a generic theory of systems comprised of living entities. However, progress towards predictive CAS theory has been slow. Agent-based modelling is a major tool for exploring CAS, but it took more than two decades for agent-based modelling to mature and develop a common strategy and language. Moreover, referring to systems as being „adaptive“ can prevent us from asking the right questions about how systems comprised of adaptive agents develop structures and functions that persist over time, despite disturbances and changes in drivers. Thus, a new generic theory of “Agent-based Complex Systems“ (ACS) can complement and develop CAS theory if it has a stronger focus on adaptive decision making of agents. Pattern-oriented theory development should be used to find appropriate representations of human decision making, and a more rigorous conceptual framework of stability properties and resilience is needed to ask the right questions.

Short Bio:

Prof. Dr. Volker Grimm is biologist and physicist and works at the Department of Ecological Modelling at the Helmholtz Centre for Environmental Research – UFZ in Leipzig and as professor for Theoretical Ecology at the University of Potsdam. He is a world-leading expert in ecological modelling and co-authored both the first monograph on individual-based modelling in ecology and the first textbook on agent-based modelling. His main research focus is on optimizing model development (“pattern-oriented modelling”), communication (“ODD protocol”), and validation (“evaludation”). He has been involved in modelling a broad range of plant and animal populations and communities. Using agent-based models, he tries to link adaptive behaviour of individuals to population and community dynamics and to develop a mechanistic basis for resilience theory.

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