Methodology Does Matter: About Implicit Assumptions in Applied Formal Modelling. The case of Dynamic Stochastic General Equilibrium Models vs Agent-Based Models

Gräbner, Claudius (2015): Methodology Does Matter: About Implicit Assumptions in Applied Formal Modelling. The case of Dynamic Stochastic General Equilibrium Models vs Agent-Based Models.

Abstract

This article uses the functional decomposition approach to modeling Mäki (2009b) to discuss the importance of methodological considerations before choosing a modeling framework in applied research. It considers the case of agent-based models and dynamic stochastic general equilibrium models to illustrate the implicit epistemological and ontological statements related to the choice of the corresponding modeling framework and highlights the important role of the purpose and audience of a model. Special focus is put on the limited capacity for model exploration of equilibrium models and their difficulty to model mechanisms explicitly. To model mechanisms that have interaction effects with other mechanisms is identified as a particular challenge that sometimes makes the explanation of phenomena by isolating the underlying mechanisms a difficult task. Therefore I argue for a more extensive use of agent-based models as they provide a formal tool to address this challenge. The overall conclusion is that a plurality of models is required: single models are simply pushed to their limits if one wishes to identify the right degree of isolation required to understand reality.

• Item Type: MPRA Paper
• Language: English
• Keywords: Functional decomposition approach, general equilibrium, agent-based models, methodology, epistemology, ontology, formal modeling, isolation
• Subjects: B - History of Economic Thought, Methodology, and Heterodox Approaches > B4 - Economic Methodology > B41 - Economic Methodology; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling

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