An application of Markov chain model in board game revised

In this thesis, we explored the use of Markov chain model and Monte-Carlo simulation to analyze and tune the prototype of a chance-based board game. The ultimate design goals of game length and game play complexity are set. In order to achieve the goals we also introduce a method to transform the original Markov chain into a new chain to analyze game process and results. Based on the results, we tuned the prototype by adding new conditions and tweaking some critical parameters. Some minor modifications in the rules are necessary as well.In the end, we finalize our model and verified the results.