Toward inverse generative social science using multi-objective genetic programming

Tuong Manh Vu, Alan Brennan, Charlotte Probst, Mark Strong, Joshua M. Epstein, Robin C. Purshouse

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Generative mechanism-based models of social systems, such as those represented by agent-based simulations, require that intraagent equations (or rules) be specified. However there are often many different choices available for specifying these equations, which can still be interpreted as falling within a particular class of mechanisms. Whilst it is important for a generative model to reproduce historically observed dynamics, it is also important for the model to be theoretically enlightening. Genetic programs (our own included) often produce concatenations that are highly predictive but are complex and hard to interpret theoretically. Here, we develop a new method - based on multi-objective genetic programming - for automating the exploration of both objectives simultaneously. We demonstrate the method by evolving the equations for an existing agent-based simulation of alcohol use behaviors based on social norms theory, the initial model structure for which was developed by a team of human modelers. We discover a trade-off between empirical fit and theoretical interpretability that offers insight into the social norms processes that influence the change and stasis in alcohol use behaviors over time.

Original languageEnglish (US)
Title of host publicationGECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery, Inc
Pages1356-1363
Number of pages8
ISBN (Electronic)9781450361118
DOIs
StatePublished - Jul 13 2019
Event2019 Genetic and Evolutionary Computation Conference, GECCO 2019 - Prague, Czech Republic
Duration: Jul 13 2019Jul 17 2019

Publication series

NameGECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference

Conference

Conference2019 Genetic and Evolutionary Computation Conference, GECCO 2019
CountryCzech Republic
CityPrague
Period7/13/197/17/19

Keywords

  • Generative social science
  • Genetic programming
  • Multi-objective optimization

ASJC Scopus subject areas

  • Computational Mathematics

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  • Cite this

    Vu, T. M., Brennan, A., Probst, C., Strong, M., Epstein, J. M., & Purshouse, R. C. (2019). Toward inverse generative social science using multi-objective genetic programming. In GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 1356-1363). (GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/3321707.3321840