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Related Experiment Video

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Using Multi-objective Grammar-based Genetic Programming to Integrate Multiple Social Theories in Agent-based

Tuong Manh Vu¹, Eli Davies¹, Charlotte Buckley¹

¹University of Sheffield, Sheffield, UK.

Evolutionary Multi-Criterion Optimization : ... International Conference, EMO ... : Proceedings. EMO (Conference)

|May 7, 2021

Summary

This summary is machine-generated.

Integrating multiple social theories computationally can improve explanations of complex phenomena. This study introduces a framework using multi-objective grammar-based genetic programming (MOGGP) to discover hybrid models that outperform single-theory approaches for alcohol use dynamics.

Keywords:

Agent-based modeling Grammar-based genetic programming Inverse generative social science Multi-objective optimization

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Area of Science:

Computational social science
Generative social science
Computational modeling

Background:

Existing models often rely on single theories, providing incomplete explanations for complex social phenomena like population alcohol use.
Integrating multiple theoretical mechanisms computationally offers a promising avenue to enhance the explanatory power of social science models.

Purpose of the Study:

To present a novel framework for integrative model discovery in social science, enhancing explanatory capabilities.
To demonstrate the framework's efficacy by integrating norm theory and role theory for modeling alcohol use dynamics.

Main Methods:

Utilized multi-objective grammar-based genetic programming (MOGGP) for integrative model discovery.
Developed a new grammar integrating factors from norm theory and role theory to model decision-making in alcohol consumption.
Applied the framework to discover hybrid models of alcohol use dynamics.

Main Results:

The MOGGP framework successfully discovered new hybrid models that integrate multiple theories.
These novel hybrid models demonstrated superior performance compared to existing single-theory models.
The discovered models also outperformed a baseline hybrid model, validating the integrative approach.

Conclusions:

Computational integration of theories via MOGGP significantly improves the explanatory power of social science models.
The developed framework offers a robust method for discovering enhanced, theory-driven generative models.
Further refinement involving domain experts is recommended for validating the meaningfulness of discovered models.