Using Evolutionary Model Discovery to Develop Robust Policies

Created by W.Langdon from gp-bibliography.bib Revision:1.7656

@InProceedings{Isherwood:2023:WSC,

• author = "Alex Isherwood and Matthew Koehler and David Slater",
• booktitle = "2023 Winter Simulation Conference (WSC)",
• title = "Using Evolutionary Model Discovery to Develop Robust Policies",
• year = "2023",
• pages = "130--137",
• abstract = "Agent-based models can be a powerful tool for evaluating the impact of policy decisions on a population. However, analyses are traditionally beholden to one set of rules hypothesized at the conception of the model. Modellers must make assumptions of agent behaviour that are not necessarily governed by data and the actual behaviour of the true population can thusly vary. Evolutionary model discovery (EMD) seeks to provide a solution to this problem by leveraging genetic algorithms and genetic programming to explore the plausible set of rules that can explain agent behaviour. Here we describe an initial use of the EMD system to develop robust policies in a resource constrained environment. In this instance, we extend the NetLogo implementation of the Epstein Rebellion model of civil violence as a sample problem. We use the EMD framework to generate 23 plausible populations and then develop policy responses for the government that are robust across the plausible populations.",
• keywords = "genetic algorithms, genetic programming, Sociology, Government, Data models, Behavioural sciences, Space exploration, Statistics, Tuning",
• DOI = "doi:10.1109/WSC60868.2023.10407233",
• ISSN = "1558-4305",
• month = dec,
• notes = "Also known as \cite{10407233}",

}

Genetic Programming entries for Alex Isherwood Matthew Koehler David Slater

Citations