Bayesian calibration of differentiable agent-based models
Agent-based modelling (ABMing) is a powerful and intuitive approach to modelling complex systems; however, the intractability of ABMs' likelihood functions and the non-differentiability of the mathematical operations comprising these models present a challenge to their use in the real world. Th...
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creator | Quera-Bofarull, Arnau Chopra, Ayush Calinescu, Anisoara Wooldridge, Michael Dyer, Joel |
description | Agent-based modelling (ABMing) is a powerful and intuitive approach to
modelling complex systems; however, the intractability of ABMs' likelihood
functions and the non-differentiability of the mathematical operations
comprising these models present a challenge to their use in the real world.
These difficulties have in turn generated research on approximate Bayesian
inference methods for ABMs and on constructing differentiable approximations to
arbitrary ABMs, but little work has been directed towards designing approximate
Bayesian inference techniques for the specific case of differentiable ABMs. In
this work, we aim to address this gap and discuss how generalised variational
inference procedures may be employed to provide misspecification-robust
Bayesian parameter inferences for differentiable ABMs. We demonstrate with
experiments on a differentiable ABM of the COVID-19 pandemic that our approach
can result in accurate inferences, and discuss avenues for future work. |
doi_str_mv | 10.48550/arxiv.2305.15340 |
format | Article |
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modelling complex systems; however, the intractability of ABMs' likelihood
functions and the non-differentiability of the mathematical operations
comprising these models present a challenge to their use in the real world.
These difficulties have in turn generated research on approximate Bayesian
inference methods for ABMs and on constructing differentiable approximations to
arbitrary ABMs, but little work has been directed towards designing approximate
Bayesian inference techniques for the specific case of differentiable ABMs. In
this work, we aim to address this gap and discuss how generalised variational
inference procedures may be employed to provide misspecification-robust
Bayesian parameter inferences for differentiable ABMs. We demonstrate with
experiments on a differentiable ABM of the COVID-19 pandemic that our approach
can result in accurate inferences, and discuss avenues for future work.</description><identifier>DOI: 10.48550/arxiv.2305.15340</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Multiagent Systems ; Statistics - Machine Learning</subject><creationdate>2023-05</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2305.15340$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2305.15340$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Quera-Bofarull, Arnau</creatorcontrib><creatorcontrib>Chopra, Ayush</creatorcontrib><creatorcontrib>Calinescu, Anisoara</creatorcontrib><creatorcontrib>Wooldridge, Michael</creatorcontrib><creatorcontrib>Dyer, Joel</creatorcontrib><title>Bayesian calibration of differentiable agent-based models</title><description>Agent-based modelling (ABMing) is a powerful and intuitive approach to
modelling complex systems; however, the intractability of ABMs' likelihood
functions and the non-differentiability of the mathematical operations
comprising these models present a challenge to their use in the real world.
These difficulties have in turn generated research on approximate Bayesian
inference methods for ABMs and on constructing differentiable approximations to
arbitrary ABMs, but little work has been directed towards designing approximate
Bayesian inference techniques for the specific case of differentiable ABMs. In
this work, we aim to address this gap and discuss how generalised variational
inference procedures may be employed to provide misspecification-robust
Bayesian parameter inferences for differentiable ABMs. We demonstrate with
experiments on a differentiable ABM of the COVID-19 pandemic that our approach
can result in accurate inferences, and discuss avenues for future work.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Multiagent Systems</subject><subject>Statistics - Machine Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj81uwjAQhH3pAVEegFP9Agn-iXFypKgUJCQu3KP1ereyFBLkoArevintZWZOo-8TYqlVWdXOqRXke_oujVWu1M5Waiaad3jQmKCXCF0KGW5p6OXAMiZmytTfEoSOJHxNswgwUpSXIVI3vooXhm6kxX_PxXn3cd7ui-Pp87DdHAtYe1VgUyErDx45YB2sxtrUiixxXCuMATQ0CMZ4jd471mSnaCY0720wzHYu3v5un-ztNacL5Ef769A-HewPQmtCAQ</recordid><startdate>20230524</startdate><enddate>20230524</enddate><creator>Quera-Bofarull, Arnau</creator><creator>Chopra, Ayush</creator><creator>Calinescu, Anisoara</creator><creator>Wooldridge, Michael</creator><creator>Dyer, Joel</creator><scope>AKY</scope><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20230524</creationdate><title>Bayesian calibration of differentiable agent-based models</title><author>Quera-Bofarull, Arnau ; Chopra, Ayush ; Calinescu, Anisoara ; Wooldridge, Michael ; Dyer, Joel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a670-c94cf07a7cfbc8b31c8280e3efd60cdba1a9ca2271c775f1e35f19340773b2ff3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Multiagent Systems</topic><topic>Statistics - Machine Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Quera-Bofarull, Arnau</creatorcontrib><creatorcontrib>Chopra, Ayush</creatorcontrib><creatorcontrib>Calinescu, Anisoara</creatorcontrib><creatorcontrib>Wooldridge, Michael</creatorcontrib><creatorcontrib>Dyer, Joel</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv Statistics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Quera-Bofarull, Arnau</au><au>Chopra, Ayush</au><au>Calinescu, Anisoara</au><au>Wooldridge, Michael</au><au>Dyer, Joel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bayesian calibration of differentiable agent-based models</atitle><date>2023-05-24</date><risdate>2023</risdate><abstract>Agent-based modelling (ABMing) is a powerful and intuitive approach to
modelling complex systems; however, the intractability of ABMs' likelihood
functions and the non-differentiability of the mathematical operations
comprising these models present a challenge to their use in the real world.
These difficulties have in turn generated research on approximate Bayesian
inference methods for ABMs and on constructing differentiable approximations to
arbitrary ABMs, but little work has been directed towards designing approximate
Bayesian inference techniques for the specific case of differentiable ABMs. In
this work, we aim to address this gap and discuss how generalised variational
inference procedures may be employed to provide misspecification-robust
Bayesian parameter inferences for differentiable ABMs. We demonstrate with
experiments on a differentiable ABM of the COVID-19 pandemic that our approach
can result in accurate inferences, and discuss avenues for future work.</abstract><doi>10.48550/arxiv.2305.15340</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Artificial Intelligence Computer Science - Multiagent Systems Statistics - Machine Learning |
title | Bayesian calibration of differentiable agent-based models |
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