Abstract
Different theoretical mechanisms have been proposed for explaining complex social phenomena. For example, explanations for observed trends in population alcohol use have been postulated based on norm theory, role theory, and others. Many mechanism-based models of phenomena attempt to translate a single theory into a simulation model. However, single theories often only represent a partial explanation for the phenomenon. The potential of integrating theories together, computationally, represents a promising way of improving the explanatory capability of generative social science. This paper presents a framework for such integrative model discovery, based on multi-objective grammar-based genetic programming (MOGGP). The framework is demonstrated using two separate theory-driven models of alcohol use dynamics based on norm theory and role theory. The proposed integration considers how the sequence of decisions to consume the next drink in a drinking occasion may be influenced by factors from the different theories. A new grammar is constructed based on this integration. Results of the MOGGP model discovery process find new hybrid models that outperform the existing single-theory models and the baseline hybrid model. Future work should consider and further refine the role of domain experts in defining the meaningfulness of models identified by MOGGP.
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Notes
- 1.
Model discrepancy is the error in a model output that arises because the model is not a perfect representation of reality.
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Acknowledgment
The research reported in this paper was funded by the National Institute on Alcohol Abuse and Alcoholism of the National Institutes of Health under Award Number R01AA024443.
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Vu, T.M., Davies, E., Buckley, C., Brennan, A., Purshouse, R.C. (2021). Using Multi-objective Grammar-Based Genetic Programming to Integrate Multiple Social Theories in Agent-Based Modeling. In: Ishibuchi, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2021. Lecture Notes in Computer Science(), vol 12654. Springer, Cham. https://doi.org/10.1007/978-3-030-72062-9_57
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