School of Management, Technical University of Munich, Munich, Germany.
Faculty of Computer Science, Otto von Guericke University of Magdeburg, Magdeburg, Germany.
PLoS One. 2020 Feb 7;15(2):e0228879. doi: 10.1371/journal.pone.0228879. eCollection 2020.
Evolving in groups can either enhance or reduce an individual's task performance. Still, we know little about the factors underlying group performance, which may be reduced to three major dimensions: (a) the individual's ability to perform a task, (b) the dependency on environmental conditions, and (c) the perception of, and the reaction to, other group members. In our research, we investigated how these dimensions interrelate in simulated evolution experiments using adaptive agents equipped with Markov brains ("animats"). We evolved the animats to perform a spatial-navigation task under various evolutionary setups. The last generation of each evolution simulation was tested across modified conditions to evaluate and compare the animats' reliability when faced with change. Moreover, the complexity of the evolved Markov brains was assessed based on measures of information integration. We found that, under the right conditions, specialized animats could be as reliable as animats already evolved for the modified tasks, and that reliability across varying group sizes correlated with evolved fitness in most tested evolutionary setups. Our results moreover suggest that balancing the number of individuals in a group may lead to higher reliability but also lower individual performance. Besides, high brain complexity was associated with balanced group sizes and, thus, high reliability under limited sensory capacity. However, additional sensors allowed for even higher reliability across modified environments without a need for complex, integrated Markov brains. Despite complex dependencies between the individual, the group, and the environment, our computational approach provides a way to study reliability in group behavior under controlled conditions. In all, our study revealed that balancing the group size and individual cognitive abilities prevents over-specialization and can help to evolve better reliability under unknown environmental situations.
群体中的进化可以提高或降低个体的任务表现。然而,我们对影响群体表现的因素知之甚少,这些因素可以归结为三个主要维度:(a)个体完成任务的能力,(b)对环境条件的依赖,以及(c)对其他群体成员的感知和反应。在我们的研究中,我们使用配备了马尔可夫大脑(“智能体”)的适应性智能体在模拟进化实验中研究了这些维度是如何相互关联的。我们让智能体在各种进化设置下执行空间导航任务。每个进化模拟的最后一代都在经过修改的条件下进行测试,以评估和比较智能体在面对变化时的可靠性。此外,还根据信息整合的度量来评估进化的马尔可夫大脑的复杂性。我们发现,在适当的条件下,专业化的智能体可以像已经针对修改后的任务进化的智能体一样可靠,而且在大多数测试的进化设置中,不同群体大小的可靠性与进化适应度相关。我们的研究结果还表明,平衡群体中的个体数量可能会导致更高的可靠性,但也会降低个体的表现。此外,高脑复杂度与平衡的群体大小相关,因此在有限的感官能力下具有较高的可靠性。然而,额外的传感器可以在不需要复杂的、集成的马尔可夫大脑的情况下,在经过修改的环境中实现更高的可靠性。尽管个体、群体和环境之间存在复杂的依赖关系,但我们的计算方法提供了一种在受控条件下研究群体行为可靠性的方法。总之,我们的研究表明,平衡群体规模和个体认知能力可以防止过度专业化,并有助于在未知环境情况下更好地进化可靠性。