Validation and Calibration of an Agent-Based Model: A Surrogate Approach
Agent-based modelling has been proved to be extremely useful for learning about real world societies through the analysis of simulations. Recent agent-based models usually contain a large number of parameters that capture the interactions among microheterogeneous subjects and the multistructure of t...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/6946370 |
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| _version_ | 1832553946449707008 |
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| author | Yi Zhang Zhe Li Yongchao Zhang |
| author_facet | Yi Zhang Zhe Li Yongchao Zhang |
| author_sort | Yi Zhang |
| collection | DOAJ |
| description | Agent-based modelling has been proved to be extremely useful for learning about real world societies through the analysis of simulations. Recent agent-based models usually contain a large number of parameters that capture the interactions among microheterogeneous subjects and the multistructure of the complex system. However, this can result in the “curse of dimensionality” phenomenon and decrease the robustness of the model’s output. Hence, it is still a great challenge to efficiently calibrate agent-based models to actual data. In this paper, we present a surrogate analysis method for calibration by combining supervised machine-learning and intelligent iterative sampling. Without any prior assumptions regarding the distribution of the parameter space, the proposed method can learn a surrogate model as the approximation of the original system with a relatively small number of training points, which will serve the needs of further sensitivity analysis and parameter calibration research. We take the heterogeneous asset pricing model as an example to evaluate the model’s performance using actual Chinese stock market data. The results demonstrate the good capabilities of the surrogate model at modelling the observed reality, as well as the remarkable reduction of the computational time for validating the agent-based model. |
| format | Article |
| id | doaj-art-6943b227ff9c450c8a0611d355891ccd |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-6943b227ff9c450c8a0611d355891ccd2025-02-03T05:52:44ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/69463706946370Validation and Calibration of an Agent-Based Model: A Surrogate ApproachYi Zhang0Zhe Li1Yongchao Zhang2School of Economics, Northeastern University at Qinhuangdao, Qinhuangdao 066004, ChinaSchool of Economics, Northeastern University at Qinhuangdao, Qinhuangdao 066004, ChinaSchool of Mathematics and Statistics, Northeastern University at Qinhuangdao, Qinhuangdao 066004, ChinaAgent-based modelling has been proved to be extremely useful for learning about real world societies through the analysis of simulations. Recent agent-based models usually contain a large number of parameters that capture the interactions among microheterogeneous subjects and the multistructure of the complex system. However, this can result in the “curse of dimensionality” phenomenon and decrease the robustness of the model’s output. Hence, it is still a great challenge to efficiently calibrate agent-based models to actual data. In this paper, we present a surrogate analysis method for calibration by combining supervised machine-learning and intelligent iterative sampling. Without any prior assumptions regarding the distribution of the parameter space, the proposed method can learn a surrogate model as the approximation of the original system with a relatively small number of training points, which will serve the needs of further sensitivity analysis and parameter calibration research. We take the heterogeneous asset pricing model as an example to evaluate the model’s performance using actual Chinese stock market data. The results demonstrate the good capabilities of the surrogate model at modelling the observed reality, as well as the remarkable reduction of the computational time for validating the agent-based model.http://dx.doi.org/10.1155/2020/6946370 |
| spellingShingle | Yi Zhang Zhe Li Yongchao Zhang Validation and Calibration of an Agent-Based Model: A Surrogate Approach Discrete Dynamics in Nature and Society |
| title | Validation and Calibration of an Agent-Based Model: A Surrogate Approach |
| title_full | Validation and Calibration of an Agent-Based Model: A Surrogate Approach |
| title_fullStr | Validation and Calibration of an Agent-Based Model: A Surrogate Approach |
| title_full_unstemmed | Validation and Calibration of an Agent-Based Model: A Surrogate Approach |
| title_short | Validation and Calibration of an Agent-Based Model: A Surrogate Approach |
| title_sort | validation and calibration of an agent based model a surrogate approach |
| url | http://dx.doi.org/10.1155/2020/6946370 |
| work_keys_str_mv | AT yizhang validationandcalibrationofanagentbasedmodelasurrogateapproach AT zheli validationandcalibrationofanagentbasedmodelasurrogateapproach AT yongchaozhang validationandcalibrationofanagentbasedmodelasurrogateapproach |