MATEC Web Conf.
Volume 373, 202210th edition of the International Multidisciplinary Symposium “UNIVERSITARIA SIMPRO 2022”: Quality and Innovation in Education, Research and Industry – the Success Triangle for a Sustainable Economic, Social and Environmental Development
|Number of page(s)||7|
|Published online||20 December 2022|
Decision analysis within the small company for specialized equipment destinated for monitoring the safety mining parameters
1 Doctoral School - University of Petrosani, Universității no.20 Street, Petrosani, 332006, Romania
2 University of Petrosani, Universității no.20 Street, Petrosani, 332006, Romania
* Corresponding author: firstname.lastname@example.org
This paper offers a review of fundamentals in decision analysis and the construction of evidence-based probabilities for use in decision-making. The term quantitative risk analysis generally connotes reliance on probability and statistics. However, select quantitative risk-based decision-making methodologies, such as game theory, do not require knowledge of probabilities. Maximizing the minimum (maximin) gain, minimizing the maximum (minimax) loss, or maximizing the maximum (maximax) gain are but a few examples of decision-making criteria for handling risk and uncertainty without adhering to probabilities. Quantitative risk assessment builds on the existence of probabilities that describe the likelihood of outcomes, such as consequences. In general, probabilities are derived on the basis of historical records, statistical analysis, and/or systemic observations and experimentation. We commonly refer to probabilities that are derived from this process as "objective probabilities". Often, however, situations arise where the database is so sparse and experimentation is so impractical that "objective probabilities" must be supplemented with "subjective probabilities" or probabilities that are based on expert evidence, often referred to as "expert judgment". This paper focus on generating probabilities on the basis of expert evidence, using by the decision rules under uncertainty.
© The Authors, published by EDP Sciences, 2022
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