From Novice to Golden Dragon: How I Used Monte Carlo Simulations to Master Mahjong Probability and Win the Cyberspace

I never set out to write about mahjong.

I set out to break the code behind it.

At my day job, I build risk models for European gaming platforms—Monte Carlo simulations predicting win rates across 127 tile combinations. At night, I write cyberpunk fiction on anonymous forums where the only real currency is insight.

The mahjong table? It’s not luck. It’s a Markov chain dressed in silk robes.

My father taught me that probability doesn’t care if you pray—it cares if you calculate.

I mapped player types using MBTI: ENTP (Inventor archetype). High openness. Low responsibility. But extreme outcome orientation. Every session was a live experiment: Rs. 100 bets at midnight, no reward? Then you double down—because your algorithm saw what your heart wanted before you did.

The ‘Golden Dragon’ isn’t a title—it’s the moment when your last discard aligns with expected value.

I don’t chase jackpots.

I chase entropy reduction.

Join the Golden Dragon Community: upload your screenshots. Let them see how you turned chaos into code—and won without touching a tile.