Multiobjective Genetic Programming Can Improve the Explanatory Capabilities of Mechanism-Based Models of Social Systems
The generative approach to social science, in which agent-based simulations (or other complex systems models) are executed to reproduce a known social phenomenon, is an important tool for realist explanation. However, a generative model, when suitably calibrated and validated using empirical data, r...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8923197 |
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| author | Tuong M. Vu Charlotte Buckley Hao Bai Alexandra Nielsen Charlotte Probst Alan Brennan Paul Shuper Mark Strong Robin C. Purshouse |
| author_facet | Tuong M. Vu Charlotte Buckley Hao Bai Alexandra Nielsen Charlotte Probst Alan Brennan Paul Shuper Mark Strong Robin C. Purshouse |
| author_sort | Tuong M. Vu |
| collection | DOAJ |
| description | The generative approach to social science, in which agent-based simulations (or other complex systems models) are executed to reproduce a known social phenomenon, is an important tool for realist explanation. However, a generative model, when suitably calibrated and validated using empirical data, represents just one viable candidate set of entities and mechanisms. The model only partially addresses the needs of an abductive reasoning process—specifically it does not provide insight into other viable sets of entities or mechanisms nor suggests which of these are fundamentally constitutive for the phenomenon to exist. In this paper, we propose a new model discovery framework that more fully captures the needs of realist explanation. The framework exploits the implicit ontology of an existing human-built generative model to propose and test a plurality of new candidate model structures. Genetic programming is used to automate this search process. A multiobjective approach is used, which enables multiple perspectives on the value of any particular generative model—such as goodness of fit, parsimony, and interpretability—to be represented simultaneously. We demonstrate this new framework using a complex systems modeling case study of change and stasis in societal alcohol use patterns in the US over the period 1980–2010. The framework is successful in identifying three competing explanations of these alcohol use patterns, using novel integrations of social role theory not previously considered by the human modeler. Practitioners in complex systems modeling should use model discovery to improve the explanatory utility of the generative approach to realist social science. |
| format | Article |
| id | doaj-art-034a14e9c3b44f8692b4a71b1b93717d |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-034a14e9c3b44f8692b4a71b1b93717d2025-08-20T02:02:57ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/89231978923197Multiobjective Genetic Programming Can Improve the Explanatory Capabilities of Mechanism-Based Models of Social SystemsTuong M. Vu0Charlotte Buckley1Hao Bai2Alexandra Nielsen3Charlotte Probst4Alan Brennan5Paul Shuper6Mark Strong7Robin C. Purshouse8School of Health and Related Research, University of Sheffield, Sheffield, UKAutomatic Control and Systems Engineering, University of Sheffield, Sheffield, UKAutomatic Control and Systems Engineering, University of Sheffield, Sheffield, UKAlcohol Research Group, Public Health Institute, Emeryville, CA, USAInstitute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, CanadaSchool of Health and Related Research, University of Sheffield, Sheffield, UKInstitute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, CanadaSchool of Health and Related Research, University of Sheffield, Sheffield, UKAutomatic Control and Systems Engineering, University of Sheffield, Sheffield, UKThe generative approach to social science, in which agent-based simulations (or other complex systems models) are executed to reproduce a known social phenomenon, is an important tool for realist explanation. However, a generative model, when suitably calibrated and validated using empirical data, represents just one viable candidate set of entities and mechanisms. The model only partially addresses the needs of an abductive reasoning process—specifically it does not provide insight into other viable sets of entities or mechanisms nor suggests which of these are fundamentally constitutive for the phenomenon to exist. In this paper, we propose a new model discovery framework that more fully captures the needs of realist explanation. The framework exploits the implicit ontology of an existing human-built generative model to propose and test a plurality of new candidate model structures. Genetic programming is used to automate this search process. A multiobjective approach is used, which enables multiple perspectives on the value of any particular generative model—such as goodness of fit, parsimony, and interpretability—to be represented simultaneously. We demonstrate this new framework using a complex systems modeling case study of change and stasis in societal alcohol use patterns in the US over the period 1980–2010. The framework is successful in identifying three competing explanations of these alcohol use patterns, using novel integrations of social role theory not previously considered by the human modeler. Practitioners in complex systems modeling should use model discovery to improve the explanatory utility of the generative approach to realist social science.http://dx.doi.org/10.1155/2020/8923197 |
| spellingShingle | Tuong M. Vu Charlotte Buckley Hao Bai Alexandra Nielsen Charlotte Probst Alan Brennan Paul Shuper Mark Strong Robin C. Purshouse Multiobjective Genetic Programming Can Improve the Explanatory Capabilities of Mechanism-Based Models of Social Systems Complexity |
| title | Multiobjective Genetic Programming Can Improve the Explanatory Capabilities of Mechanism-Based Models of Social Systems |
| title_full | Multiobjective Genetic Programming Can Improve the Explanatory Capabilities of Mechanism-Based Models of Social Systems |
| title_fullStr | Multiobjective Genetic Programming Can Improve the Explanatory Capabilities of Mechanism-Based Models of Social Systems |
| title_full_unstemmed | Multiobjective Genetic Programming Can Improve the Explanatory Capabilities of Mechanism-Based Models of Social Systems |
| title_short | Multiobjective Genetic Programming Can Improve the Explanatory Capabilities of Mechanism-Based Models of Social Systems |
| title_sort | multiobjective genetic programming can improve the explanatory capabilities of mechanism based models of social systems |
| url | http://dx.doi.org/10.1155/2020/8923197 |
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