Mathematical, Statistical, and Soft Computing Methods for Uncertainty Management
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".
Deadline for manuscript submissions: 15 November 2024 | Viewed by 13716
Special Issue Editors
Interests: business; finance and tourism; resource and service management; natural resources; sustainable rural development; water resources management; agricultural resources management; land resources management; financial economics; accounting and management; sustainability; entrepreneurship; innovation; quality and environmental management systems
Special Issues, Collections and Topics in MDPI journals
Interests: portfolio management; Markov-switching models; financial market distress prediction; commodity futures’ trading; active future trading; socially responsible investment
Special Issues, Collections and Topics in MDPI journals
Interests: business; finance and tourism; resource and service management; natural resources; sustainable rural development; water resources management; agricultural resources management; land resources management; financial economics; accounting and management; sustainability; entrepreneurship; innovation; quality and environmental management systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
One of the critical areas of quantitative analysis is the behavior of the statistical parameters of a random event. The statistical parameters are random, or the probability function is not fixed or homogeneous in specific scenarios or periods. A fundamental assumption in this type of analysis is either fixed statistical parameters or a fixed probability function in their values. Relaxing this assumption implies that the model is subject to uncertainty. This issue applies in the study of phenomena in several knowledge areas such as natural sciences, social sciences, data science, or physics (among others).
Even if this assumption led to improvements, several phenomena, such as the COVID-19 pandemic and its natural or social impact, showed that the fixed-parameter or probability function assumption does not hold. This conclusion suggests that using or developing more advanced mathematical, statistical, or soft computing methods is a necessity. The proper use of such techniques could lead to better quantitative results and, if applied, to better decisions.
For the particular case of social and natural sciences, not incorporating the presence of uncertainty in the model or the decision process leads to skewed estimation, and the use of a model that does not describe the actual phenomena of interest.
The purpose of this Special Issue is the development and discussion of methods that lead with uncertainty either in the statistical parameters, the probability function, or even the debate of applied soft computing methods that allow for incorporating latent or unknown information in the model of interest.
With this focus in mind, this Special Issue will accept papers in the following (not exclusive) list of topics of interest:
- Bayesian statistics methods with application in robust parameter estimation in physics, computational, natural, and social sciences;
- Fuzzy logic methods to deal with the uncertainty of parameter or probability function in physics, computational, natural and social sciences;
- Development of soft computing methods in Bayesian, fuzzy logic, or other quantitative areas that deal with model uncertainty;
- Developments of theoretical Bayesian statistics methods and related philosophical discussions;
- Development of theoretical fuzzy logic and related philosophical discussions;
- Development of soft computing models to deal with uncertainty, such as, Markov-chain Monte Carlo, artificial intelligence, data science algorithms, or computational uncertainty modeling;
- Statistical uncertainty models applied in economics, econometrics, finance, management, biology, chemistry, health sciences, sociology, psychology or any social or natural science;
- Optimization or optimal control methods that incorporate uncertainty in their estimation;
- Other related topics.
Prof. Dr. José Álvarez-García
Prof. Dr. Oscar V. De la Torre-Torres
Prof. Dr. María de la Cruz del Río-Rama
Guest Editors
Manuscript Submission Information
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Keywords
- Bayesian statistics methods
- fuzzy logic methods
- Markov-chain Monte Carlo
- optimization or optimal control methods
- soft computing methods
- statistical uncertainty models
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