Suppose you have a customer attribute classified as high, medium and low. Different class of customers are supposed to have different probabilities of state transition. Are you creating micro ML models to get different probabilities or you are just using relative percentages of past transitions based on historical data?
using distributions based on historical data is the thing most typically used if you have transition data... nevertheless depending on the situation you can also use prediction models to know what transition the agent will use, as long as you have independent variables that you can use to predict this...
This prediction can be done with ML models or statistical models, depending on the situation
You can also go further and use artificial intelligence if there is a constant sequential decision making on where to transition, you can do this if you want to optimize the behavior of these customers... reinforcement learning is used for that and you can use your simulation model to generate a policy for the decision making process of these customers.