发表机构
Otto von Guericke University; Max Planck Institute for Dynamics of Complex Technical Systems; Leibniz Institute for Neurobiology（奥托·冯·格里克大学; 马克斯·普朗克复杂技术系统动力学研究所; 莱布尼茨神经生物学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出包含25个生物动态博弈的库及配套Julia求解器，通过顺序最优控制计算并审计广义纳什均衡，覆盖多尺度生物相互作用，并以植物-食草动物博弈验证其应用价值。
AI 中文摘要
生物主体，从调控模块和微生物菌株到生物体和种群，作用于共同的环境，同时各自响应自身的成本、收益和限制。动态博弈通过指定谁控制哪个决策、每个参与者重视什么以及哪些限制约束单方面偏离，使这些相互作用变得明确。我们提出了一个包含25个生物动态博弈的库，涉及2至10个参与者，涵盖分子、细胞、生物体和种群尺度，以及竞争、对抗、互利和调控相互作用。实例涵盖单季节、返回初始状态的循环或情节任务；其中七个包含持续时间选择，且某些参与者的可行集是耦合的。对于每个博弈，我们提供生物学动机、一个可执行的Julia规范（包含参与者拥有的控制、目标和限制）、一个参数实例，以及一个参考的开环广义纳什均衡候选及其解释轨迹。求解器（http://this URL）通过顺序最优控制最佳响应计算候选，并在粗网格和细控制网格上审计其可行性和有利的单方面偏离。所有25个参考计算在两个网格上都满足审计容差。以植物-食草动物博弈作为运行示例，我们展示了可执行规范如何产生一个经审计的参考候选，该候选定性地再现了已发表的预测，生物学假设如何成为具有不同候选的实例变体，以及候选如何为逆博弈生成合成数据。
英文摘要
Biological agents, from regulatory modules and microbial strains to organisms and populations, act on a common environment while each responds to its own costs, benefits, and limits. A dynamic game makes such interactions explicit by assigning who controls which decision, what each participant values, and which restrictions constrain unilateral deviations. We present a library of 25 biological dynamic games with 2 to 10 players spanning molecular, cellular, organismal, and population scales and competitive, antagonistic, mutualistic, and regulatory interactions. Instances cover single seasons, cycles that return to their initial state, or episodic tasks; seven include duration choice and some players' feasible sets are coupled. For each game we provide biological motivation, an executable Julia specification with player-owned controls, objectives and restrictions, a parameter instance, and a reference open-loop generalized Nash equilibrium candidate with interpreted trajectories. The solver CorleoneGame.jl computes candidates by sequential optimal-control best responses and audits them for feasibility and profitable unilateral deviations on a coarse and a refined control grid. All 25 reference computations satisfy the audit tolerances on both grids. Using a plant--herbivore game as a running example, we show how an executable specification yields an audited reference candidate that qualitatively reproduces a published prediction, how biological hypotheses become instance variants with different candidates, and how the candidates generate synthetic data for inverse games.
