Competition among animals for resources, notably food, territories, and mates, is ubiquitous at all scales of life. This competition is often resolved through contests among individuals, which are commonly understood according to their outcomes and in particular, how these outcomes depend on decision-making by the contestants. Because they are restricted to end-point predictions, these approaches cannot predict real-time or real-space dynamics of animal contest behavior. This limitation can be overcome by studying systems that feature typical contest behavior while being simple enough to track and model. Here, we propose to use such systems to construct a theoretical framework that describes real-time movements and behaviors of animal contestants. We study the spatiotemporal dynamics of contests in an orb-weaving spider, in which all the common elements of animal contests play out. The confined arena of the web, on which interactions are dominated by vibratory cues in a two-dimensional space, simplifies the analysis of interagent interactions. We ask whether these seemingly complex decision-makers can be modeled as interacting active particles responding only to effective forces of attraction and repulsion due to their interactions. By analyzing the emergent dynamics of “contestant particles,” we provide mechanistic explanations for real-time dynamical aspects of animal contests, thereby explaining competitive advantages of larger competitors and demonstrating that complex decision-making need not be invoked in animal contests to achieve adaptive outcomes. Our results demonstrate that physics-based classification and modeling, in terms of effective rules of interaction, provide a powerful framework for understanding animal contest behaviors.

动物之间对资源的竞争，尤其是食物、领地和配偶，在生命的各个层面无处不在。这种竞争通常通过个人之间的竞争来解决，人们通常根据他们的结果来理解，特别是这些结果如何取决于参赛者的决策。因为它们仅限于终点预测，所以这些方法无法预测动物竞赛行为的实时或真实空间动态。这个限制可以通过研究具有典型比赛行为的系统来克服，同时又足够简单以进行跟踪和建模。在这里，我们建议使用这样的系统来构建一个描述动物参赛者实时运动和行为的理论框架。我们研究了一个圆蛛蜘蛛比赛的时空动态，动物竞赛的所有共同元素都在其中发挥作用。网络的受限区域，交互由二维空间中的振动线索主导，简化了代理间交互的分析。我们询问这些看似复杂的决策者是否可以被建模为相互作用的活性粒子，由于它们的相互作用，它们只对有效的吸引力和排斥力作出反应。通过分析“参赛者粒子”的涌现动力学，我们为动物比赛的实时动力学方面提供了机械解释，从而解释了大型竞争对手的竞争优势，并证明了在动物比赛中不需要调用复杂的决策来实现适应性结果. 我们的结果表明，基于物理的分类和建模，