The aim of this note is to study the behaviour of the gambler’s ruin, a finite state Markov chain with absorting state at its ends.
Def Gambler’s ruin chain. Let be the MC with state space and transition probabilities and , with the boundary conditions .
The value is the fortune of the player, if it reaches the state he is ruined!
The following are interesting questions:
- What is the probability of ruin starting from .
- What is the average lenght of the game?
Let’s define the event ruin:
which is the same as using the notation in Hitting probability and mean hitting times.
Using Markov property we obtain the following system of equations:
Solving RHEs via ansatz
we solve this systems of linear equations from an ansatz:
substituting we get
assuming
solving for
we get two values for . Since we are solving linear equations the general solution has the form
let’s find and using the boundary conditions
and the solution we were looking for is
Remark In the limit the ruin probability is simply a geometric random variable.
For the symmetric case this is not enough, since we only get . First note that in the symmetric case, our difference equations is a discretization of the laplace equation:
rearranging
which is the finite difference formula for the second derivative. Linear functions solve laplace equation in d, so an ansatz is
and a general solution is
using the boundary conditions we get