mbyham8

My simulation has one entity - patients. Each patient is assigned to a state that defines their patient class, but this state is to be updated every three hours. The probability of moving to a state in the future is dependent on the current state, so it's a classic Marcov chain. Right now I have a very complicated and error-prone process that models the state reassignment logic. It involves many conditional and probabilistic decision nodes with manually entered numbers and subsequent assignment nodes. I would like to clean this process to reduce the probability of programming error by using data tables, but cannot figure out how to do so. Is it possible to reference entity states in a data table to model the decision-making?