We introduced my 4-year-old son to the classic game of Trouble somewhat recently and he turned out to be probably the luckiest person ever with the game. I swear he rolls a 6 more than everyone else combined but that may just be a skewed view of reality from a Dad that keeps losing to him.
Enter the Monte Carlo Simulation.
“Monte Carlo Simulation is a mathematical technique that generates random variables for modeling risk or uncertainty of a certain system.” [EconomicTimes]
The results show the single active pawn strategy wins 53.1% of the time, giving it a slight edge if you want to call it that. So next time I play him, I will keep only one pawn active at a time and work my way to victory.
Disclosure: We don’t let anyone win because of their age in our house. It is earned, so it actually means something. I learned this from my sweet old grandmother at an early age.
A great in-depth write up on Monte Carlo in Python has been done by Chris Moffitt on pbpython.com if you want to get more into it beyond this.
Quick Note on the Code
This was a quick side project I did in my free time. I am sure there are better ways to approach some things. If you have ideas, politely comment below or contact me.