Any crypto project works with uncertainties, and when there is a lack of data, conventional methods of analysis are not suitable, because we have to operate with assumptions. Fortunately, science has long since developed techniques for dealing with data scarcity. Tools such as "Monte Carlo Method", "Markov chains" are widely used in science, economics, games and many other fields to solve mathematical problems.
The Monte Carlo method is used for approximate numerical solution of mathematical problems that are difficult or impossible to solve analytically.
Markov chains are a mathematical tool used to model random processes with discrete states.
How are they used in crypto projects?
Let's imagine that you have developed a protocol, a game, or any other crypto-project with different variants of user behavior. The options can be random or have a choice of predefined events. The events can be related to each other or have an arbitrary nature. That is, the user after a certain action has a set of possible options to continue the interaction, for example: the user bought tokens and put them in the stack; his next step is options: either he just waits, or he adds more tokens to the stack, or he takes tokens from the stack.
There can be many such choices during the course of a cryptoproject at each step. We cannot know in advance how the user will act at any given moment, but in the case of Markov Chains, we can determine the probabilities of an event at each specific point, and in the case of the Monte Carlo Method, we can go through the possible choices by choosing all possible outcomes.