Ambush strategy enhances organisms' performance in rock-paper-scissors games

TL;DR

This study demonstrates that an ambush strategy, based on environmental cues and local density awareness, significantly improves success rates and alters spatial patterns in a five-species cyclic rock-paper-scissors game.

Contribution

It introduces a novel ambush tactic in a spatial cyclic game, showing how environmental awareness enhances organism performance and influences spatial organization.

Findings

Ambush strategy increases individual success probability by over 60%.

Spatial patterns exhibit spiral waves influenced by ambush tactics.

Long-range perception improves decision-making and performance.

Abstract

We study a five-species cyclic system wherein individuals of one species strategically adapt their movements to enhance their performance in the spatial rock-paper-scissors game. Environmental cues enable the awareness of the presence of organisms targeted for elimination in the cyclic game. If the local density of target organisms is sufficiently high, individuals move towards concentrated areas for direct attack; otherwise, they employ an ambush tactic, maximising the chances of success by targeting regions likely to be dominated by opponents. Running stochastic simulations, we discover that the ambush strategy enhances the likelihood of individual success compared to direct attacks alone, leading to uneven spatial patterns characterised by spiral waves. We compute the autocorrelation function and measure how the ambush tactic unbalances the organisms' spatial organisation by calculating the characteristic length scale of typical spatial domains of each species. We demonstrate that the threshold for local species density influences the ambush strategy's effectiveness, while the neighbourhood perception range significantly impacts decision-making accuracy. The outcomes show that long-range perception improves performance by over 60\%, although there is potential interference in decision-making under high attack triggers. Understanding how organisms' adaptation to their environment enhances their performance may be helpful not only for ecologists but also for data scientists aiming to improve artificial intelligence systems.