Evaluation tool for assessing the influence of structural health monitoring on decision-maker risk preferences

Abstract

Different rationally behaving individuals can make different decisions even under same conditions. Concept of risk preference is one way of describing this discrepancy. Risk preference refers to the way decision-makers weight different risky options. Expected utility theory is a popular way to treat this weighting mathematically. Previous research in the field of structural health monitoring has shown that in a multi-stakeholder decision-making, discrepancies in risk preferences between the stakeholders can have large effects on the feasibility of structural health monitoring implementation. Results from that previous research obtained using expected utility theory showed that under certain conditions, differences in stakeholder risk attitudes can make the value of structural health monitoring system even negative. As the overall aim of structural health monitoring is to enable systematic data-based management of infrastructure, understanding of risk preferences and their effect on the structural health monitoring decision-making is of paramount importance. In this work, we created a risk preference assessment tool that is derived from Domain-Specific Risk-Taking scale. We propose the tool and explain its development and validation. The validation results indicate that the proposed assessment tool is technically sound and has high potential in the future valuation of human factors influencing decision-making based on structural health monitoring.

Keywords

Structural health monitoring decision-making risk attitude risk preference human factors survey risk aversion asset management

Get full access to this article

View all access options for this article.

References

Glišić

Inaudi

. Fibre optic methods for structural health monitoring. Hoboken, NJ: John Wiley & Sons, 2007.

Pozzi

Kiureghian

. Assessing the value of information for long-term structural health monitoring. In: Proceedings of the SPIE health monitoring of structural and biological systems 2011, Vol. 7984, San Diego, CA, 18 April 2011, Pp. 79842W. SPIE.doi: 10.1117/12.881918

Thöns

. On the value of monitoring information for the structural integrity and risk management. Comput-aid Civ Infrast Eng 2018; 33(1): 79–94.

Zonta

Glisic

Adriaenssens

. Value of information: impact of monitoring on decision-making. Struct Control Health Monitor 2014; 21(7): 1043–1056.

Limongelli

Omenzetter

Yazgan

, et al. Quantifying the value of monitoring for post-earthquake emergency management of bridges. IABSE Sympos Rep 2017; 109: 3238–3245.

Cappello

Zonta

Glišić

. Expected utility theory for monitoring-based decision-making. Proc IEEE 2016; 104(8): 1647–1661.

Bolognani

Verzobio

Tonelli

, et al. An application of prospect theory to a SHM-based decision problem. In: Proceedings of the SPIE health monitoring of structural and biological systems 2017, Vol. 10170, Portland, OR, 15 May 2017, pp. 101702G. DOI: 10.1117/12.2261212

Bolognani

Verzobio

Tonelli

, et al. IWSHM 2017: quantifying the benefit of structural health monitoring: what if the manager is not the owner? Struct Health Monitor 2018; 17(6): 1393–1409.

Metzger

Fehr

. Measuring financial risk attitude: how to apply both regulatory and scientific criteria to ensure suitability. J Behav Finance 2018; 19(2): 221–234.

10.

Weber

Blais

A-R

Betz

. A domain-specific risk-attitude scale: measuring risk perceptions and risk behaviors. J Behav Decis Making 2002; 15(4): 263–290.

11.

Tompkins

Bjälkebring

Peters

. Emotional aspects of risk perceptions. In: Raue

Lermer

Streicher

(eds) Psychological perspectives on risk and risk analysis: theory, models, and applications. Cham: Springer International Publishing, 2018, pp. 109–130.

12.

Figner

Weber

. Who takes risks when and why?: determinants of risk taking. Curr Dir Psychol Sci 2011; 20(4): 211–216.

13.

Raue

D’Ambrosio

Ward

, et al. The influence of feelings while driving regular cars on the perception and acceptance of self-driving cars. Risk Anal 2019; 39(2): 358–374.

14.

Lee

C-J

Scheufele

Lewenstein

. Public attitudes toward emerging technologies: examining the interactive effects of cognitions and affect on public attitudes toward nanotechnology. Sci Commun 2005; 27(2): 240–267.

15.

Sokolowska

Sleboda

. The inverse relation between risks and benefits: the role of affect and expertise: the inverse relation between risks and benefits. Risk Anal 2015; 35(7): 1252–1267.

16.

Chauvin

. Individual differences in the judgment of risks: sociodemographic characteristics, cultural orientation, and level of expertise. In: Raue

Lermer

Streicher

(eds) Psychological perspectives on risk and risk analysis: theory, models, and applications. Cham: Springer International Publishing, 2018, pp. 37–61.

17.

Slovic

. Understanding perceived risk: 1978–2015. Environ Sci Policy Sust Devel 2016; 58(1): 25–29.

18.

Pedroni

Frey

Bruhin

, et al. The risk elicitation puzzle. Nat Hum Behav 2017; 1(11): 803–809.

19.

Hertwig

Wulff Dirk

Mata

. Three gaps and what they may mean for risk preference. Philosoph Trans Royal Soc B: Biolog Sci 2019; 374(1766): 20180140.

20.

Blais

A-R

Weber

. A Domain-Specific Risk-Taking (DOSPERT) scale for adult populations. Judg Decis Making 2006; 1(1): 15.

21.

Highhouse

Nye

Zhang

, et al. Structure of the DOSPERT: is there evidence for a general risk factor? J Behav Dec Making 2017; 30(2): 400–406.

22.

Markiewicz

Weber

. DOSPERT’s gambling risk-taking propensity scale predicts excessive stock trading. J Behav Finance 2013; 14(1): 65–78.

23.

Van Bossuyt

Carvalho

Dong

, et al. On measuring engineering risk attitudes. In: Proceedings of the ASME international design engineering technical conferences & computers and information in engineering conference, Washington, DC, 28–31 August 2011, pp. 425–434. New York: ASME.

24.

Butler

Rosman

Seleski

, et al. A medical risk attitude subscale for DOSPERT. Judg Decis Mak 2012; 7: 7.

25.

Everitt

. The Cambridge dictionary of statistics. 2nd ed. New York: Cambridge University Press, 2002.

26.

Qualtrics XM// The Leading Experience Management Software, https://www.qualtrics.com/ (accessed 5 February 2020).

27.

Core Team R. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing, 2018.

28.

Eisenführ

Weber

Langer

. Rational decision making. Berlin, Heidelberg: Springer-Verlag, 2010.

29.

Ali

Qin

Faber

. On information modeling in structural integrity management. Struct Health Monitor. Epub ahead of print 12 November 2020. DOI: 10.1177/1475921720968292

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

2.41 MB