Stochastic decision tree acceptability analysis with uncertain state probability

In a fast-changing environment, state in the future is difficult to predict. Traditional approaches are unable to support decision-makers to find out optimal alternative effectively when the probability of future's environmental state is unknown or uncertain. In this study, we propose a stochastic decision tree acceptability analysis (SDTAA), which aims to manage this decision-making problem effectively. In SDTAA, state probability space with random distribution is utilized to capture unknown or uncertain state probabilities and stochastic values or ordinal values are used to model uncertain attributes values. Then, by computing rank acceptability, holistic expected value and value variance of each alternative, SDTAA can help decision makers find the optimal alternative effectively when state probability is uncertain, unknown or missing. In addition, Monte Carlo simulation based algorithms are proposed to calculate the rank acceptability, holistic expected value and value variance. A numerical example is presented to illustrate the SDTAA method.