Intransitiveness in the Penney Game and in Random Walks on rings, networks, communities and cities

TL;DR

This paper explores the concept of intransitiveness in games like Penney and extends it to random walks on various networks, analyzing how traps and biases influence game outcomes and network properties.

Contribution

It introduces the extension of intransitiveness to random walks on complex networks, analyzing the effects of traps, biases, and initial conditions on game dynamics.

Findings

Intransitiveness can be mapped to competition among traps in random walks.

Network topology and trap properties significantly affect game outcomes.

Biases and initial distributions influence the likelihood of reaching traps.

Abstract

The concept of intransitiveness for games, which is the condition for which there is no first-player winning strategy can arise surprisingly, as happens in the Penney game, an extension of the heads or tails. Since a game can be converted into a random walk on a graph, i.e., a Markov process, we extend the intransitiveness concept to such systems. The end of the game generally consists in the appearance of a pre-defined pattern. In the language of random walk this corresponds to an absorbing trap, since once that the game has reached this condition the game comes to an end. Therefore, the intransitiveness of the game can be mapped into a problem of competition among traps. We analyse in details random walkers on several kind of networks (rings, scale-free, hierarchical and city-inspired) with several variations: traps can be partially absorbing, the walker can be biased and the initial distribution can be arbitrary. We found that the transitivity concept can be quite useful for characterizing the combined properties of a graph and that of the walkers.