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    Rights statement: This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 251, 3, 2016 DOI: 10.1016/j.ejor.2015.12.034

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Agent-based computational modelling of social risk responses

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Agent-based computational modelling of social risk responses. / Busby, Jeremy Simon; Onggo, Bhakti Satyabuhdi Stephan; Liu, Yun.
In: European Journal of Operational Research, Vol. 251, No. 3, 16.06.2016, p. 1029-1042.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Busby JS, Onggo BSS, Liu Y. Agent-based computational modelling of social risk responses. European Journal of Operational Research. 2016 Jun 16;251(3):1029-1042. Epub 2015 Dec 23. doi: 10.1016/j.ejor.2015.12.034

Author

Busby, Jeremy Simon ; Onggo, Bhakti Satyabuhdi Stephan ; Liu, Yun. / Agent-based computational modelling of social risk responses. In: European Journal of Operational Research. 2016 ; Vol. 251, No. 3. pp. 1029-1042.

Bibtex

@article{78db69630aca49b1b39affcf5c7814a5,
title = "Agent-based computational modelling of social risk responses",
abstract = "A characteristic aspect of risks in a complex, modern society is the nature and degree of the public response – sometimes significantly at variance with objective assessments of risk. A large part of the risk management task involves anticipating, explaining and reacting to this response. One of the main approaches we have for analysing the emergent public response, the social amplification of risk framework, has been the subject of little modelling. The purpose of this paper is to explore how social risk amplification can be represented and simulated. The importance of heterogeneity among risk perceivers, and the role of their social networks in shaping risk perceptions, makes it natural to take an agent-based approach. We look in particular at how to model some central aspects of many risk events: the way actors come to observe other actors more than external events in forming their risk perceptions; the way in which behaviour both follows risk perception and shapes it; and the way risk communications are fashioned in the light of responses to previous communications. We show how such aspects can be represented by availability cascades, but also how this creates further problems of how to represent the contrasting effects of informational and reputational elements, and the differentiation of private and public risk beliefs. Simulation of the resulting model shows how certain qualitative aspects of risk response time series found empirically – such as endogenously-produced peaks in risk concern – can be explained by this model.",
keywords = "OR in societal problem analysis, Multiagent systems, Risk management",
author = "Busby, {Jeremy Simon} and Onggo, {Bhakti Satyabuhdi Stephan} and Yun Liu",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 251, 3, 2016 DOI: 10.1016/j.ejor.2015.12.034",
year = "2016",
month = jun,
day = "16",
doi = "10.1016/j.ejor.2015.12.034",
language = "English",
volume = "251",
pages = "1029--1042",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier Science B.V.",
number = "3",

}

RIS

TY - JOUR

T1 - Agent-based computational modelling of social risk responses

AU - Busby, Jeremy Simon

AU - Onggo, Bhakti Satyabuhdi Stephan

AU - Liu, Yun

N1 - This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 251, 3, 2016 DOI: 10.1016/j.ejor.2015.12.034

PY - 2016/6/16

Y1 - 2016/6/16

N2 - A characteristic aspect of risks in a complex, modern society is the nature and degree of the public response – sometimes significantly at variance with objective assessments of risk. A large part of the risk management task involves anticipating, explaining and reacting to this response. One of the main approaches we have for analysing the emergent public response, the social amplification of risk framework, has been the subject of little modelling. The purpose of this paper is to explore how social risk amplification can be represented and simulated. The importance of heterogeneity among risk perceivers, and the role of their social networks in shaping risk perceptions, makes it natural to take an agent-based approach. We look in particular at how to model some central aspects of many risk events: the way actors come to observe other actors more than external events in forming their risk perceptions; the way in which behaviour both follows risk perception and shapes it; and the way risk communications are fashioned in the light of responses to previous communications. We show how such aspects can be represented by availability cascades, but also how this creates further problems of how to represent the contrasting effects of informational and reputational elements, and the differentiation of private and public risk beliefs. Simulation of the resulting model shows how certain qualitative aspects of risk response time series found empirically – such as endogenously-produced peaks in risk concern – can be explained by this model.

AB - A characteristic aspect of risks in a complex, modern society is the nature and degree of the public response – sometimes significantly at variance with objective assessments of risk. A large part of the risk management task involves anticipating, explaining and reacting to this response. One of the main approaches we have for analysing the emergent public response, the social amplification of risk framework, has been the subject of little modelling. The purpose of this paper is to explore how social risk amplification can be represented and simulated. The importance of heterogeneity among risk perceivers, and the role of their social networks in shaping risk perceptions, makes it natural to take an agent-based approach. We look in particular at how to model some central aspects of many risk events: the way actors come to observe other actors more than external events in forming their risk perceptions; the way in which behaviour both follows risk perception and shapes it; and the way risk communications are fashioned in the light of responses to previous communications. We show how such aspects can be represented by availability cascades, but also how this creates further problems of how to represent the contrasting effects of informational and reputational elements, and the differentiation of private and public risk beliefs. Simulation of the resulting model shows how certain qualitative aspects of risk response time series found empirically – such as endogenously-produced peaks in risk concern – can be explained by this model.

KW - OR in societal problem analysis

KW - Multiagent systems

KW - Risk management

U2 - 10.1016/j.ejor.2015.12.034

DO - 10.1016/j.ejor.2015.12.034

M3 - Journal article

VL - 251

SP - 1029

EP - 1042

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 3

ER -