Using experimental gaming simulations to elicit risk mitigation behavioral strategies for agricultural disease management.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2020
Historique:
received: 25 10 2019
accepted: 16 01 2020
entrez: 18 3 2020
pubmed: 18 3 2020
medline: 13 6 2020
Statut: epublish

Résumé

Failing to mitigate propagation of disease spread can result in dire economic consequences for agricultural networks. Pathogens like Porcine Epidemic Diarrhea virus, can quickly spread among producers. Biosecurity is designed to prevent infection transmission. When considering biosecurity investments, management must balance the cost of protection versus the consequences of contracting an infection. Thus, an examination of the decision making processes associated with investment in biosecurity is important for enhancing system wide biosecurity. Data gathered from experimental gaming simulations can provide insights into behavioral strategies and inform the development of decision support systems. We created an online digital experiment to simulate outbreak scenarios among swine production supply chains, where participants were tasked with making biosecurity investment decisions. In Experiment One, we quantified the risk associated with each participant's decisions and delineated three dominant categories of risk attitudes: risk averse, risk tolerant, and opportunistic. Each risk class exhibited unique approaches in reaction to risk and disease information. We also tested how information uncertainty affects risk aversion, by varying the amount of visibility of the infection as well as the amount of biosecurity implemented across the system. We found evidence that more visibility in the number of infected sites increases risk averse behaviors, while more visibility in the amount of neighboring biosecurity increased risk taking behaviors. In Experiment Two, we were surprised to find no evidence for differences in behavior of livestock specialists compared to Amazon Mechanical Turk participants. Our findings provide support for using experimental gaming simulations to study how risk communication affects behavior, which can provide insights towards more effective messaging strategies.

Identifiants

pubmed: 32182247
doi: 10.1371/journal.pone.0228983
pii: PONE-D-19-29852
pmc: PMC7077803
doi:

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0228983

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

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Auteurs

Eric M Clark (EM)

SEGS Lab, University of Vermont, Burlington, Vermont, United States of America.
Department of Plant and Soil Science, University of Vermont, Burlington, Vermont, United States of America.

Scott C Merrill (SC)

SEGS Lab, University of Vermont, Burlington, Vermont, United States of America.
Department of Plant and Soil Science, University of Vermont, Burlington, Vermont, United States of America.
Gund Institute for Environment, University of Vermont, Burlington, Vermont, United States of America.

Luke Trinity (L)

SEGS Lab, University of Vermont, Burlington, Vermont, United States of America.
Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America.

Gabriela Bucini (G)

SEGS Lab, University of Vermont, Burlington, Vermont, United States of America.
Department of Plant and Soil Science, University of Vermont, Burlington, Vermont, United States of America.

Nicholas Cheney (N)

SEGS Lab, University of Vermont, Burlington, Vermont, United States of America.
Department of Computer Science, University of Vermont, Burlington, Vermont, United States of America.

Ollin Langle-Chimal (O)

SEGS Lab, University of Vermont, Burlington, Vermont, United States of America.
Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America.

Trisha Shrum (T)

SEGS Lab, University of Vermont, Burlington, Vermont, United States of America.
Department of Community Development and Applied Economics, University of Vermont, Burlington, Vermont, United States of America.

Christopher Koliba (C)

SEGS Lab, University of Vermont, Burlington, Vermont, United States of America.
Gund Institute for Environment, University of Vermont, Burlington, Vermont, United States of America.
Department of Community Development and Applied Economics, University of Vermont, Burlington, Vermont, United States of America.

Asim Zia (A)

SEGS Lab, University of Vermont, Burlington, Vermont, United States of America.
Gund Institute for Environment, University of Vermont, Burlington, Vermont, United States of America.
Department of Community Development and Applied Economics, University of Vermont, Burlington, Vermont, United States of America.

Julia M Smith (JM)

SEGS Lab, University of Vermont, Burlington, Vermont, United States of America.
Department of Animal and Veterinary Sciences, University of Vermont, Burlington, Vermont, United States of America.

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Classifications MeSH