Bektas, Alperen; Piana, Valentino; Schumann, René (2021). A meso-level empirical validation approach for agent-based computational economic models drawing on micro-data: a use case with a mobility mode-choice model Springer Nature Business & Economics, 1(6) Springer Nature 10.1007/s43546-021-00083-4
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Bektas2021_Article_AMeso-levelEmpiricalValidation.pdf - Published Version Available under License Creative Commons: Attribution (CC-BY). Download (1MB) | Preview |
The complex nature of agent-based modeling may reveal more descriptive accuracy than analytical tractability. That leads to an additional layer of methodological issues regarding empirical validation, which is an ongoing challenge. This paper offers a replicable method to empirically validate agent-based models, a specific indicator of “goodness-of-validation” and its statistical distribution, leading to a statistical test in some way comparable to the p value. The method involves an unsupervised machine learning algorithm hinging on cluster analysis. It clusters the ex-post behavior of real and artificial individuals to create meso-level behavioral patterns. By comparing the balanced composition of real and artificial agents among clusters, it produces a validation score in [0, 1] which can be judged thanks to its statistical distribution. In synthesis, it is argued that an agent-based model can be initialized at the micro-level, calibrated at the macro-level, and validated at the meso-level with the same data set. As a case study, we build and use a mobility mode-choice model by configuring an agent-based simulation platform called BedDeM. We cluster the choice behavior of real and artificial individuals with the same ex-ante given characteristics. We analyze these clusters’ similarity to understand whether the model-generated data contain observationally equivalent behavioral patterns as the real data. The model is validated with a specific score of 0.27, which is better than about 95% of all possible scores that the indicator can produce. By drawing lessons from this example, we provide advice for researchers to validate their models if they have access to micro-data.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
Business School > Institute for Public Sector Transformation Business School > Institute for Public Sector Transformation > Data and Infrastructure Business School |
Name: |
Bektas, Alperen 0000-0002-4476-5916; Piana, Valentino and Schumann, René |
Subjects: |
H Social Sciences > HB Economic Theory H Social Sciences > HE Transportation and Communications Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Publisher: |
Springer Nature |
Funders: |
[UNSPECIFIED] This research is part of the activities of SCCER CREST, which is financially supported by the Swiss Innovation Agency (Innosuisse). |
Language: |
English |
Submitter: |
Alperen Bektas |
Date Deposited: |
26 May 2021 11:42 |
Last Modified: |
22 Sep 2021 02:18 |
Publisher DOI: |
10.1007/s43546-021-00083-4 |
ARBOR DOI: |
10.24451/arbor.14876 |
URI: |
https://arbor.bfh.ch/id/eprint/14876 |
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