Multiobjective Genetic Programming Can Improve the Explanatory Capabilities of Mechanism-Based Models of Social Systems
Multiobjective Genetic Programming Can Improve the Explanatory Capabilities of Mechanism-Based Models of Social Systems
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DOI：
10.1155/2020/8923197
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发表时间：
2020-06-05
期刊：
影响因子：
2.3
通讯作者：
Purshouse, Robin C.
中科院分区：
文献类型：
--
作者：
Tuong M Vu;Buckley, Charlotte;Purshouse, Robin C.
社会科学的生成方法，其中基于代理的模拟（或其他复杂系统模型）被执行以再现已知的社会现象，是现实主义解释的重要工具。然而，当使用经验数据进行适当校准和验证时，生成模型仅代表一组可行的候选实体和机制。该模型只部分地解决了溯因推理过程的需要，具体来说，它没有提供其他可行的实体或机制集的洞察力，也没有表明这些是根本构成的现象存在。在本文中，我们提出了一个新的模型发现框架，更充分地捕捉现实主义的解释的需要。该框架利用现有人工构建的生成模型的隐式本体来提出和测试多个新的候选模型结构。遗传编程用于自动化这个搜索过程。一个多目标的方法，使多个角度的任何特定的生成模型的价值，如拟合优度，简约性，可解释性，同时表示。我们证明了这个新的框架，使用复杂的系统建模案例研究的变化和停滞在社会酒精使用模式在美国在1980年至2010年期间。该框架成功地确定了这些酒精使用模式的三种相互竞争的解释，使用了人类建模者以前没有考虑过的社会角色理论的新整合。复杂系统建模的实践者应该使用模型发现来提高生成方法对现实主义社会科学的解释效用。
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.