Using Evolutionary Model Discovery to Develop Robust Policies
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- @InProceedings{Isherwood:2023:WSC,
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author = "Alex Isherwood and Matthew Koehler and David Slater",
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booktitle = "2023 Winter Simulation Conference (WSC)",
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title = "Using Evolutionary Model Discovery to Develop Robust
Policies",
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year = "2023",
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pages = "130--137",
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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.",
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keywords = "genetic algorithms, genetic programming, Sociology,
Government, Data models, Behavioural sciences, Space
exploration, Statistics, Tuning",
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DOI = "doi:10.1109/WSC60868.2023.10407233",
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ISSN = "1558-4305",
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month = dec,
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notes = "Also known as \cite{10407233}",
- }
Genetic Programming entries for
Alex Isherwood
Matthew Koehler
David Slater
Citations