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Complexity Economics and Agent-Based Modeling
CONTENT This module provide an introduction into complexity thinking and complexity economics. Complexity economics is a modern school of thought that differs strongly from neoclassical economics. The economy is conceived as a complex adaptive system. Agents are typically not fully rational and there is no a-priori assumption that equilibria exist. Complexity economics is well suited to study innovation and societal transformation processes. Examples of such transformations are the sustainability transition and the effect of digitalization. Complexity thinking provides a new understanding of societal challenges and of economic policy. There are different approaches to analyze complex systems, for instance network analysis, system dynamics and narrative research. We briefly introduce these methods, but mostly focus on agent-based computer models. Agent-based models are an extremely flexible research tool that can be applied to many different topics and for many purposes. We present examples of agent-based models and show how that can be used for theoretical and policy analyses. The module covers technical aspects of agent-based modeling and simulation such as how to set up a model, how it can be analyzed and how it can be implemented on a computer. After finishing the course, you will be able to program your own small model for a research project or the master’s thesis. MODULE OBJECTIVES  You understand what a complex system is and how the complexity view differs from other economic approaches.  You learn how to apply complexity thinking to get a better understanding of the economy and economic policy.  You learn how to work with ABM and how to interpret their results.  You acquire basic knowledge to implement your own agent-based models.  You will learn how to use the ABM programming platform NetLogo. PREREQUISITES You will need very good skills in written and spoken English. Some affinity to computer programming would be helpful.


Frau Michelle Alfers

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Vorlesung und Übung