- author = "Tuong Manh Vu and Charlotte Probst and Joshua M. Epstein and Alan Brennan and Mark Strong and Robin C. Purshouse",
- title = "Toward inverse generative social science using multi-objective genetic programming",
- booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion",
- year = "2019",
- editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre",
- pages = "1356--1363",
- address = "Prague, Czech Republic",
- publisher_address = "New York, NY, USA",
- month = "13-17 " # jul,
- organisation = "SIGEVO",
- publisher = "ACM",
- keywords = "genetic algorithms, genetic programming, generative social science, multi-objective optimization",
- isbn13 = "978-1-4503-6111-8",
-
URL = "
http://human-competitive.org/sites/default/files/vu-paper.pdf",
-
DOI = "
doi:10.1145/3321707.3321840",
- size = "8 pages",
- abstract = "Generative mechanism-based models of social systems, such as those represented by agent-based simulations, require that intra-agent 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 behaviours based on social norms theory, the initial model structure for which was developed by a team of human modellers. 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 behaviours over time.",
-
notes = "Entered 2019 Humies.
Also known as \cite{3321840} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA-2019) and the 24th Annual Genetic Programming Conference (GP-2019)",
