Special issue: a comprehensive study on enhanced optimization-based model calibration using gradient information

Lee, Guesuk; Yi, Guilian; Youn, Byeng D.

doi:10.1007/s00158-018-1920-8

Cited by 15 publications

(1 citation statement)

References 25 publications

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“…Afterwards we show that the model is differentiable. Hence, it can be efficiently calibrated using gradient-based approaches (Lee et al 2018). We then calculate the gradients and apply a gradient-based calibration in one test case.…”

Section: 10mentioning

confidence: 99%

Calibrating Agent-Based Models of Innovation Diffusion with Gradients

Kotthoff

Hamacher

2022

JASSS

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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.

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Section: 10mentioning

confidence: 99%