Stopping times in the game Rock-Paper-Scissors
Kyeonghoon Jeong, Hyun Jae Yoo

TL;DR
This paper analyzes the stopping times in Rock-Paper-Scissors using recurrence relations and Markov chains, revealing that mean stopping times grow exponentially with the number of players.
Contribution
It introduces a novel approach combining recurrence relations and Markov chain analysis to compute and understand stopping times in the game.
Findings
Mean stopping times increase exponentially with players
Distribution of stopping times derived from Markov chain analysis
Recurrence relations effectively compute mean stopping times
Abstract
In this paper we compute the stopping times in the game Rock-Paper-Scissors. By exploiting the recurrence relation we compute the mean values of stopping times. On the other hand, by constructing a transition matrix for a Markov chain associated with the game, we get also the distribution of the stopping times and thereby we compute the mean stopping times again. Then we show that the mean stopping times increase exponentially fast as the number of the participants increases.
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Taxonomy
TopicsArtificial Intelligence in Games · Algorithms and Data Compression · Chaos-based Image/Signal Encryption
