Open Access Article
Life as Thermodynamic Evidence of Algorithmic Structure in Natural Environments
by Hector Zenil 1,* , Carlos Gershenson 2 , James A. R. Marshall 1 and David A. Rosenblueth 2
1
Behavioral and Evolutionary Theory Lab, Department of Computer Science/Kroto Research Institute, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
2
Department of Computer Science, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México (UNAM), Av. Universidad 3000, Ciudad Universitaria. C.P. 04510, México, D.F., Mexico
*
Author to whom correspondence should be addressed.
Entropy 2012 , 14 (11), 2173-2191; https://doi.org/10.3390/e14112173
Received: 3 September 2012 / Revised: 29 October 2012 / Accepted: 30 October 2012 / Published: 5 November 2012
(This article belongs to the Special Issue Selected Papers from Symposium on Natural/Unconventional Computing and Its Philosophical Significance )
Abstract
In evolutionary biology, attention to the relationship between stochastic organisms and their stochastic environments has leaned towards the adaptability and learning capabilities of the organisms rather than toward the properties of the environment. This article is devoted to the algorithmic aspects of the environment and its interaction with living organisms. We ask whether one may use the fact of the existence of life to establish how far nature is removed from algorithmic randomness. The paper uses a novel approach to behavioral evolutionary questions, using tools drawn from information theory, algorithmic complexity and the thermodynamics of computation to support an intuitive assumption about the near optimal structure of a physical environment that would prove conducive to the evolution and survival of organisms, and sketches the potential of these tools, at present alien to biology, that could be used in the future to address different and deeper questions. We contribute to the discussion of the algorithmic structure of natural environments and provide statistical and computational arguments for the intuitive claim that living systems would not be able to survive in completely unpredictable environments, even if adaptable and equipped with storage and learning capabilities by natural selection (brain memory or DNA).View Full-Text
Keywords: behavioral ecology ; algorithmic randomness ; computational thermodynamics ; Kolmogorov–Chaitin complexity ; information theory
Figure 1
以上所述就是小编给大家介绍的《Life as Thermodynamic Evidence of Algorithmic Structure in Nature (2012)》,希望对大家有所帮助,如果大家有任何疑问请给我留言,小编会及时回复大家的。在此也非常感谢大家对 码农网 的支持!
猜你喜欢:本站部分资源来源于网络,本站转载出于传递更多信息之目的,版权归原作者或者来源机构所有,如转载稿涉及版权问题,请联系我们。