Markov chain based on neutrosophic numbers in decision making

Abstract

Markov chain is a stochastic model for estimating the equilibrium of any system. It is a unique mathematical model in which the future behavior of the system depends only on the present. Often biased possibilities can be used over biased probabilities, for handling uncertain information to define Markov chain using fuzzy environment. Indeterminacy is different from randomness due to its construction type where the items involved in the space are true and false in the same time. In this context as an extension of conventional and fuzzy probabilities neutrosophic probability (NP) was introduced. These neutrosophic probabilities can be captured as neutrosophic numbers. In this paper, Markov chain based on neutrosophic numbers is introduced and a new approach to the ergoticity for the traffic states in the neutrosophic Markov chain based on neutrosophic numbers is verified. The proposed approach is applied to decision-making in the prediction of traffic volume.