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Research Papers

On Social Value of Risk Information in Risk Communication

[+] Author and Article Information
Yan Wang

Woodruff School of Mechanical Engineering,
Georgia Institute of Technology,
801 Ferst Drive NW,
Atlanta, GA 30332-0405
e-mail: yan.wang@me.gatech.edu

Manuscript received February 23, 2016; final manuscript received July 1, 2017; published online July 19, 2017. Editor: Bilal M. Ayyub.

ASME J. Risk Uncertainty Part B 3(4), 041009 (Jul 19, 2017) (9 pages) Paper No: RISK-16-1068; doi: 10.1115/1.4037210 History: Received February 23, 2016; Revised July 01, 2017

The conventional research of risk communication centers on how scientific community can improve trust and credibility in public perception, enhance public understanding of risks, and change public behaviors to conform to technocratic values. More recently, the emphasis of risk communication has evolved from conveying scientific data and risk information to establishing effective information flows. It has been recognized that establishing two-way communication channels among experts, governments, corporate, and general public is important to build trust relationship. With conflicting interests and coordination motive among stakeholders, the societal aspects of risk communication need to be considered. In this paper, a mathematical model of social value of risk information is proposed to explicitly incorporate factors such as public and private information, personal bias, knowledge, and social behavior in risk communication. Uncertainties associated with the perceived risks due to both the lack of knowledge and individual differences in population are considered in the proposed model. The impacts of precision and accuracy of risk information as well as subjective bias on social welfare are characterized. Some of the model predictions on the effectiveness of communication are verified with the observations in other's survey studies. The proposed model could potentially be used to help devise risk communication strategies and policies. Its use is demonstrated in a case study of Fukushima nuclear accident.

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Figures

Grahic Jump Location
Fig. 1

The effects of τx and τy on the expected welfare with limited scientific knowledge: (a) μ = −1.0, τν = 1.0, r = 0, and τλ = 201; (b) μ = −1.0, τν = 1.0, r = 0.4, and τλ = 201; and (c) μ = −1.0, τν = 1.0, r = 0.9, and τλ = 201

Grahic Jump Location
Fig. 2

The effects of τx and τy on the expected welfare with sufficient knowledge: (a) μ = −1.0, τν = 4451.0, r = 0, and τλ = 201; (b) μ = −1.0, τν = 4451.0, r = 0.4, and τλ = 201; and (c) μ = −1.0, τν = 4451.0, r = 0.9, and τλ = 201

Grahic Jump Location
Fig. 3

The effects of personal bias on τx and τy: (a) μ = −1.0, τν = 1.0, r = 0, and τλ = 1.0; (b) μ = −1.0, τν = 1.0, r = 0.4, and τλ = 451.0; and (c) μ = −1.0, τν = 1.0, r = 0.4, and τλ = 4451.0

Grahic Jump Location
Fig. 4

The effects of scientific knowledge and personal bias: (a) μ = −1.0, τν = 1.0, r = 0.7, and τy = 201.0; (b) μ = −1.0, τν = 301.0, r = 0.7, and τy = 201.0; and (c) μ = −1.0, τν = 4451.0, r = 0.7, and τy = 201.0

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