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arXiv 2405.02674q-bio.PEnlin.AOnlin.PSphysics.bio-phq-bio.QM
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伏击策略提升生物在石头-剪刀-布博弈中的表现

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

R. Barbalho, S. Rodrigues, M. Tenorio, J. Menezes

AI总结:

研究五物种循环博弈中个体通过环境线索调整移动策略(直接攻击或伏击),发现伏击策略显著提升成功率并影响空间组织,长程感知可提升性能超60%。

摘要:

我们研究了一个五物种循环系统,其中一个物种的个体策略性地调整其移动以提升其在空间石头-剪刀-布博弈中的表现。环境线索使个体能够感知到循环博弈中被定为消除目标的生物的存在。如果目标生物的局部密度足够高,个体会向集中区域移动进行直接攻击;否则,它们会采用伏击策略,通过瞄准可能被对手主导的区域来最大化成功机会。通过运行随机模拟,我们发现与仅使用直接攻击相比,伏击策略提高了个体成功的可能性,导致以螺旋波为特征的不均匀空间模式。我们计算了自相关函数,并通过计算每个物种典型空间域的特征长度尺度,测量了伏击策略如何打破生物空间组织的平衡。我们证明,局部物种密度阈值影响伏击策略的有效性,而邻域感知范围显著影响决策准确性。结果表明,长程感知将性能提升了超过60%,尽管在高攻击触发条件下决策可能存在干扰。理解生物对环境的适应如何提升其表现,可能不仅对生态学家有帮助,也对旨在改进人工智能系统的数据科学家有所裨益。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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