使能量收入最大化,消耗最小化的动物和人类行为

IF 1.9 4区 生物学 Q2 BIOLOGY
Alexander V. Sirotkin
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引用次数: 0

摘要

任何生物的生存能力和竞争力取决于它的能力:(1)获得最大的能量/资源;(2)最大限度地节省获得的能量并减少其消耗。实现上述目标的行为特征包括不同形式的领土和社会行为,资源的过度消耗,对能量需求活动的时间抑制,以及一些个体保护行为,以最大限度地减少体力和智力劳动的能量消耗。除了这些措施,在现代人类的能源消耗的增加:能源消耗率可以实现不仅通过合作,在提高生物过程的效率,而且在仪器的应用。技术进步的目的是将人们从高耗能的劳动中解放出来,通过投资自己最少的生物能源来获得尽可能多的能源来满足自己的生物需求。目前的跨学科方法使我们能够更好地理解能量代谢效率最大化的行为策略及其对个人和整个社会的好与坏后果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Animal and human behavior to maximize energy income and to minimize its expenditure
The viability and competitiveness of any living organism depend on its ability (1) to get the maximum of energy/resources and (2) to save maximum of obtained energy and to reduce its expenditure. Behavioral traits to achieve these goals listed above include different forms of territorial and social behavior, overconsumption of resources, temporal suppression of energy-demanding activity, as well as some kinds of individual protective behavior towards minimization of energy expenditure for manual and intellectual labor. In addition to these measures, in modern humans the increase in energy consumption: energy expenditure rate can be achieved not only by cooperation in increase of the efficiency of biological processes, but also in the application of instruments. The technical progress aims to unleash the people from energy-demanding labor, to get as much energy for covering their biological needs by investment of minimum of their own bioenergy. The present interdisciplinary approach enables a better understanding of the behavioral strategies to maximize efficiency of energy metabolism and their good and bad consequences for individuals and the society as a whole.
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来源期刊
Biosystems
Biosystems 生物-生物学
CiteScore
3.70
自引率
18.80%
发文量
129
审稿时长
34 days
期刊介绍: BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.
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