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Multiobjective Genetic Programming Can Improve the Explanatory Capabilities of Mechanism-Based Models of Social Systems
المؤلفون المشاركون
Vu, Tuong M.
Buckley, Charlotte
Bai, Hao
Nielsen, Alexandra
Probst, Charlotte
Brennan, Alan
Shuper, Paul
Strong, Mark
Purshouse, Robin C.
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-20، 20ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-06-05
دولة النشر
مصر
عدد الصفحات
20
التخصصات الرئيسية
الملخص EN
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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Vu, Tuong M.& Buckley, Charlotte& Bai, Hao& Nielsen, Alexandra& Probst, Charlotte& Brennan, Alan…[et al.]. 2020. Multiobjective Genetic Programming Can Improve the Explanatory Capabilities of Mechanism-Based Models of Social Systems. Complexity،Vol. 2020, no. 2020, pp.1-20.
https://search.emarefa.net/detail/BIM-1145269
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Vu, Tuong M.…[et al.]. Multiobjective Genetic Programming Can Improve the Explanatory Capabilities of Mechanism-Based Models of Social Systems. Complexity No. 2020 (2020), pp.1-20.
https://search.emarefa.net/detail/BIM-1145269
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Vu, Tuong M.& Buckley, Charlotte& Bai, Hao& Nielsen, Alexandra& Probst, Charlotte& Brennan, Alan…[et al.]. Multiobjective Genetic Programming Can Improve the Explanatory Capabilities of Mechanism-Based Models of Social Systems. Complexity. 2020. Vol. 2020, no. 2020, pp.1-20.
https://search.emarefa.net/detail/BIM-1145269
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1145269
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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