Using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance.

Agent-based models (ABMs) have been widely used to study socioecological systems. They are useful for studying such systems because of their ability to incorporate micro-level behaviors among interacting agents, and to understand emergent phenomena due to these interactions. However, ABMs are inhere...

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Autores principales:	Arika Ligmann-Zielinska, Daniel B Kramer, Kendra Spence Cheruvelil, Patricia A Soranno
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4bd3b2b6d9f34539811f1572bca93f00
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id	oai:doaj.org-article:4bd3b2b6d9f34539811f1572bca93f00
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spelling	oai:doaj.org-article:4bd3b2b6d9f34539811f1572bca93f002021-11-25T05:55:24ZUsing uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance.1932-620310.1371/journal.pone.0109779https://doaj.org/article/4bd3b2b6d9f34539811f1572bca93f002014-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0109779https://doaj.org/toc/1932-6203Agent-based models (ABMs) have been widely used to study socioecological systems. They are useful for studying such systems because of their ability to incorporate micro-level behaviors among interacting agents, and to understand emergent phenomena due to these interactions. However, ABMs are inherently stochastic and require proper handling of uncertainty. We propose a simulation framework based on quantitative uncertainty and sensitivity analyses to build parsimonious ABMs that serve two purposes: exploration of the outcome space to simulate low-probability but high-consequence events that may have significant policy implications, and explanation of model behavior to describe the system with higher accuracy. The proposed framework is applied to the problem of modeling farmland conservation resulting in land use change. We employ output variance decomposition based on quasi-random sampling of the input space and perform three computational experiments. First, we perform uncertainty analysis to improve model legitimacy, where the distribution of results informs us about the expected value that can be validated against independent data, and provides information on the variance around this mean as well as the extreme results. In our last two computational experiments, we employ sensitivity analysis to produce two simpler versions of the ABM. First, input space is reduced only to inputs that produced the variance of the initial ABM, resulting in a model with output distribution similar to the initial model. Second, we refine the value of the most influential input, producing a model that maintains the mean of the output of initial ABM but with less spread. These simplifications can be used to 1) efficiently explore model outcomes, including outliers that may be important considerations in the design of robust policies, and 2) conduct explanatory analysis that exposes the smallest number of inputs influencing the steady state of the modeled system.Arika Ligmann-ZielinskaDaniel B KramerKendra Spence CheruvelilPatricia A SorannoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 10, p e109779 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Arika Ligmann-Zielinska Daniel B Kramer Kendra Spence Cheruvelil Patricia A Soranno Using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance.
description	Agent-based models (ABMs) have been widely used to study socioecological systems. They are useful for studying such systems because of their ability to incorporate micro-level behaviors among interacting agents, and to understand emergent phenomena due to these interactions. However, ABMs are inherently stochastic and require proper handling of uncertainty. We propose a simulation framework based on quantitative uncertainty and sensitivity analyses to build parsimonious ABMs that serve two purposes: exploration of the outcome space to simulate low-probability but high-consequence events that may have significant policy implications, and explanation of model behavior to describe the system with higher accuracy. The proposed framework is applied to the problem of modeling farmland conservation resulting in land use change. We employ output variance decomposition based on quasi-random sampling of the input space and perform three computational experiments. First, we perform uncertainty analysis to improve model legitimacy, where the distribution of results informs us about the expected value that can be validated against independent data, and provides information on the variance around this mean as well as the extreme results. In our last two computational experiments, we employ sensitivity analysis to produce two simpler versions of the ABM. First, input space is reduced only to inputs that produced the variance of the initial ABM, resulting in a model with output distribution similar to the initial model. Second, we refine the value of the most influential input, producing a model that maintains the mean of the output of initial ABM but with less spread. These simplifications can be used to 1) efficiently explore model outcomes, including outliers that may be important considerations in the design of robust policies, and 2) conduct explanatory analysis that exposes the smallest number of inputs influencing the steady state of the modeled system.
format	article
author	Arika Ligmann-Zielinska Daniel B Kramer Kendra Spence Cheruvelil Patricia A Soranno
author_facet	Arika Ligmann-Zielinska Daniel B Kramer Kendra Spence Cheruvelil Patricia A Soranno
author_sort	Arika Ligmann-Zielinska
title	Using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance.
title_short	Using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance.
title_full	Using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance.
title_fullStr	Using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance.
title_full_unstemmed	Using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance.
title_sort	using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/4bd3b2b6d9f34539811f1572bca93f00
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Using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance.

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