Departamento de Electromagnetismo y Física de la Materia and Instituto Carlos I de Física Teórica y Computacional, Universidad de Granada, 18071 Granada, Spain;
Dipartimento di Fisica "G. Galilei", Consorzio Nazionale Interuniversitario per le Scienze Fisiche della Materia and Istituto Nazionale di Fisica Nucleare, Università di Padova, 35131 Padua, Italy; and.
Proc Natl Acad Sci U S A. 2014 Jul 15;111(28):10095-100. doi: 10.1073/pnas.1319166111. Epub 2014 Jun 30.
Empirical evidence suggesting that living systems might operate in the vicinity of critical points, at the borderline between order and disorder, has proliferated in recent years, with examples ranging from spontaneous brain activity to flock dynamics. However, a well-founded theory for understanding how and why interacting living systems could dynamically tune themselves to be poised in the vicinity of a critical point is lacking. Here we use tools from statistical mechanics and information theory to show that complex adaptive or evolutionary systems can be much more efficient in coping with diverse heterogeneous environmental conditions when operating at criticality. Analytical as well as computational evolutionary and adaptive models vividly illustrate that a community of such systems dynamically self-tunes close to a critical state as the complexity of the environment increases while they remain noncritical for simple and predictable environments. A more robust convergence to criticality emerges in coevolutionary and coadaptive setups in which individuals aim to represent other agents in the community with fidelity, thereby creating a collective critical ensemble and providing the best possible tradeoff between accuracy and flexibility. Our approach provides a parsimonious and general mechanism for the emergence of critical-like behavior in living systems needing to cope with complex environments or trying to efficiently coordinate themselves as an ensemble.
近年来，越来越多的实证证据表明，生命系统可能在临界点附近运作，处于秩序和无序的边界，这些证据的例子包括自发的大脑活动和群体动态。然而，对于理解为什么相互作用的生命系统能够动态地调整自己，以便在临界点附近保持平衡，目前还缺乏一个有充分依据的理论。在这里，我们使用统计力学和信息论的工具来表明，当处于临界点时，复杂的自适应或进化系统在应对多样化的异质环境条件时，可以更加高效。分析和计算的进化和自适应模型生动地说明了，当环境的复杂性增加时，这样的系统群体可以更加动态地自我调整到接近临界状态，而对于简单和可预测的环境，它们仍然是非临界的。在共同进化和共同适应的设置中，出现了更加稳健的向临界点的收敛，在这种设置中，个体的目标是准确地代表社区中的其他代理，从而创建一个集体的临界集合，并在准确性和灵活性之间提供最佳的权衡。我们的方法为需要应对复杂环境的生命系统或试图作为一个整体有效地协调自己的生命系统中出现类似临界的行为提供了一个简洁而通用的机制。
