Schmickl Thomas, Karsai Istvan
Artificial Life Lab of the Institute of Biology, Karl-Franzens-University Graz, Universitätsplatz 2, A-8010, Graz, Austria.
Department of Biological Sciences, East Tennessee State University, Box 70703, Johnson City, TN, 37614, USA.
Heliyon. 2023 Sep 13;9(9):e20064. doi: 10.1016/j.heliyon.2023.e20064. eCollection 2023 Sep.
We investigate how simple physical interactions can generate remarkable diversity in the life history of social agents using data of social wasps, yielding complex scalable task partitioning. We built and analyzed a computational model to investigate how diverse task allocation patterns found in nature can emerge from the same behavioral blueprint. Self-organizing mechanisms of interwoven behavioral feedback loops, task-dependent time delays and simple material flows between interacting individuals yield an emergent homeostatic self-regulation while keeping the global colony performance scalable. Task allocation mechanisms based on implicitly honest signaling via material flows are not only very robust but are also highly evolvable due to their simplicity and reliability. We find that task partitioning has evolved to be scalable and adaptable to life history traits, such as expected colony size or temporal bottlenecks in the available workforce or materials. By tuning solely the total number of agents and a social connectivity-related parameter in the model, our simulations yield the whole range of emergent patterns in task allocation and task fidelity akin to observed field data. Our model suggests that the material exchange ("common stomach mechanism") found in many paper wasps provides a common functional "core" across these genera, which not only provides self-regulation of the colony, but also provides a scalable mechanism allowing natural selection to yield complex social integration in larger colonies over the course of their evolutionary trajectory.
我们利用社会性黄蜂的数据，研究简单的物理相互作用如何在社会主体的生活史中产生显著的多样性，从而产生复杂的可扩展任务划分。我们构建并分析了一个计算模型，以研究自然界中发现的各种任务分配模式如何从相同的行为蓝图中出现。相互交织的行为反馈回路、任务相关的时间延迟以及相互作用个体之间简单的物质流的自组织机制，在保持全球蜂群性能可扩展的同时，产生了一种涌现的稳态自我调节。基于通过物质流进行隐含诚实信号传递的任务分配机制不仅非常稳健，而且由于其简单性和可靠性，具有高度的可进化性。我们发现，任务划分已经进化为可扩展的，并能适应生活史特征，如预期的蜂群大小或可用劳动力或材料中的时间瓶颈。通过仅调整模型中的主体总数和一个与社会连通性相关的参数，我们的模拟产生了一系列类似于观察到的实地数据的任务分配和任务保真度的涌现模式。我们的模型表明，在许多胡蜂中发现的物质交换（“共同胃机制”）在这些属中提供了一个共同的功能“核心”，它不仅提供了蜂群的自我调节，而且还提供了一种可扩展的机制，使自然选择能够在其进化轨迹中，在更大的蜂群中产生复杂的社会整合。
