Calibrating Agent-Based Models of Innovation Diffusion with Gradients

Florian Kotthoff and Thomas Hamacher ()
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Thomas Hamacher: https://www.ei.tum.de/ens/staff/ensteam/thomas-hamacher/

Journal of Artificial Societies and Social Simulation, 2022, vol. 25, issue 3, 4

Abstract: Consumer behavior and the decision to adopt an innovation are governed by various motives, which models find difficult to represent. A promising way to introduce the required complexity into modeling approaches is to simulate all consumers individually within an agent-based model (ABM). However, ABMs are complex and introduce new challenges. Especially the calibration of empirical ABMs was identified as a key difficulty in many works. In this work, a general ABM for simulating the Diffusion of Innovations is described. The ABM is differentiable and can employ gradient-based calibration methods, enabling the simultaneous calibration of large numbers of free parameters in large-scale models. The ABM and calibration method are tested by fitting a simulation with 25 free parameters to the large data set of privately owned photovoltaic systems in Germany, where the model achieves a coefficient of determination of R 2 ≃ 0.7.

Keywords: Agent-Based Modeling; Multi-Agent Simulation; Innovation Diffusion; Adoption Model; Decision Making; Calibration (search for similar items in EconPapers)
Date: 2022-06-30
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Citations: View citations in EconPapers (1)

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