Multi-AI competing and winning against humans in iterated Rock-Paper-Scissors game

Predicting and modeling human behavior and finding trends within human decision-making process is a major social sciences'problem. Rock Paper Scissors (RPS) is the fundamental strategic question in many game theory problems and real-world competitions. Finding the right approach to beat a particular human opponent is challenging. Here we use Markov Chains with set chain lengths as the single AIs (artificial intelligences) to compete against humans in iterated RPS game. This is the first time that an AI algorithm is applied in RPS human competition behavior studies. We developed an architecture of multi-AI with changeable parameters to adapt to different competition strategies. We introduce a parameter "focus length" (an integer of e.g. 5 or 10) to control the speed and sensitivity for our multi-AI to adapt to the opponent's strategy change. We experimented with 52 different people, each playing 300 rounds continuously against one specific multi-AI model, and demonstrated that our strategy could win over more than 95% of human opponents.