The impact of biases on simulation-based risk aggregation: modeling cognitive influences on risk assessment

Meyer, Matthias; Grisar, Cathérine; Kuhnert, Felix

doi:10.1007/s00187-011-0127-6

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…Another challenge of elicitation for risk assessment is the introduction of biases arising from the inherent subjectivity of risk evaluation. Although experts are considered to be knowledgeable and experienced, their judgment may, as a result of biases, be inaccurate-especially when judging probability [27]. There are two major types of biases: cognitive and motivational.…”

Section: Mcs For Risk Assessment and Input Modelingmentioning

confidence: 99%

Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects

2020

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Currently, input modeling for Monte Carlo simulation (MSC) is performed either by fitting a probability distribution to historical data or using expert elicitation methods when historical data are limited. These approaches, however, are not suitable for wind farm construction, where—although lacking in historical data—large amounts of subjective knowledge describing the impacts of risk factors are available. Existing approaches are also limited by their inability to consider a risk factor’s impact on cost and schedule as dependent. This paper is proposing a methodology to enhance input modeling in Monte Carlo risk assessment of wind farm projects based on fuzzy set theory and multivariate modeling. In the proposed method, subjective expert knowledge is quantified using fuzzy logic and is used to determine the parameters of a marginal generalized Beta distribution. Then, the correlation between the cost and schedule impact is determined and fit jointly into a bivariate distribution using copulas. To evaluate the feasibility of the proposed methodology and to demonstrate its main features, the method was applied to an illustrative case study, and sensitivity analysis and face validation were used to evaluate the method. The results demonstrated that the proposed approach provides a reliable method for enhancing input modeling in Monte Carlo simulation (MCS).

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Section: Mcs For Risk Assessment and Input Modelingmentioning

confidence: 99%

Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects

2020

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“…Even with robust historical data, logical financial projections, and well-considered strategy, decisions can be shortcut by cognitive biases and heuristics, influencing the perception of risk (33)(34)(35)(36)(37)(38)(39)(40). A comprehensive discussion of how during CT scanning cannot be made on a financial basis.…”

Section: Cognitive Biases In Decision Makingmentioning

confidence: 99%

Financial Forecasting and Stochastic Modeling: Predicting the Impact of Business Decisions

Rubin

Patel

2017

Radiology

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In health care organizations, effective investment of precious resources is critical to assure that the organization delivers high-quality and sustainable patient care within a supportive environment for patients, their families, and the health care providers. This holds true for organizations independent of size, from small practices to large health systems. For radiologists whose role is to oversee the delivery of imaging services and the interpretation, communication, and curation of imaging-informed information, business decisions influence where and how they practice, the tools available for image acquisition and interpretation, and ultimately their professional satisfaction. With so much at stake, physicians must understand and embrace the methods necessary to develop and interpret robust financial analyses so they effectively participate in and better understand decision making. This review discusses the financial drivers upon which health care organizations base investment decisions and the central role that stochastic financial modeling should play in support of strategically aligned capital investments. Given a health care industry that has been slow to embrace advanced financial analytics, a fundamental message of this review is that the skills and analytical tools are readily attainable and well worth the effort to implement in the interest of informed decision making. RSNA, 2017 Online supplemental material is available for this article.

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On the aggregation of credit, market and operational risks

Zhu

Lee

et al. 2013

Rev Quant Finan Acc

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The impact of biases on simulation-based risk aggregation: modeling cognitive influences on risk assessment

Cited by 7 publications

References 29 publications

Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects

Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects

Financial Forecasting and Stochastic Modeling: Predicting the Impact of Business Decisions

On the aggregation of credit, market and operational risks

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