Quantifying Uncertainty in Risk Assessment using Fuzzy Theory
- URL: http://arxiv.org/abs/2009.09334v1
- Date: Sun, 20 Sep 2020 02:12:44 GMT
- Title: Quantifying Uncertainty in Risk Assessment using Fuzzy Theory
- Authors: Hengameh Fakhravar
- Abstract summary: Risk specialists are trying to understand risk better and use complex models for risk assessment.
Traditional risk models are based on classical set theory.
We will discuss the methodology, framework, and process of using fuzzy logic systems in risk assessment.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Risk specialists are trying to understand risk better and use complex models
for risk assessment, while many risks are not yet well understood. The lack of
empirical data and complex causal and outcome relationships make it difficult
to estimate the degree to which certain risk types are exposed. Traditional
risk models are based on classical set theory. In comparison, fuzzy logic
models are built on fuzzy set theory and are useful for analyzing risks with
insufficient knowledge or inaccurate data. Fuzzy logic systems help to make
large-scale risk management frameworks more simple. For risks that do not have
an appropriate probability model, a fuzzy logic system can help model the cause
and effect relationships, assess the level of risk exposure, rank key risks in
a consistent way, and consider available data and experts'opinions. Besides, in
fuzzy logic systems, some rules explicitly explain the connection, dependence,
and relationships between model factors. This can help identify risk mitigation
solutions. Resources can be used to mitigate risks with very high levels of
exposure and relatively low hedging costs. Fuzzy set and fuzzy logic models can
be used with Bayesian and other types of method recognition and decision
models, including artificial neural networks and decision tree models. These
developed models have the potential to solve difficult risk assessment
problems. This research paper explores areas in which fuzzy logic models can be
used to improve risk assessment and risk decision making. We will discuss the
methodology, framework, and process of using fuzzy logic systems in risk
assessment.
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