Book chapter published: Facing complexity: Predition vs. adaptation

Gershenson, C. (2013). Facing complexity: Predition vs. adaptation. In A. Massip and A. Bastardas (eds), Complexity Perspectives on Language, Communication and Society.

One of the presuppositions of science since the times of Galileo, Newton, Laplace, and Descartes has been the predictability of the world. This idea has strongly influenced scientific and technological models. However, in recent decades, chaos and complexity have shown that not every phenomenon is predictable, even if it is deterministic. If a problem space is predictable, in theory we can find a solution via optimization. Nevertheless, if a problem space is not predictable, or it changes too fast, very probably optimization will offer obsolete solutions. This occurs often when the immediate solution affects the problem itself. An alternative is found in adaptation. An adaptive system will be able to find by itself new solutions for unforeseen situations.


Video: Las implicaciones de las interacciones para la ciencia y la filosofĂ­a

From today's seminar [in Spanish]


Based on:


Paper published: Life as Thermodynamic Evidence of Algorithmic Structure in Natural Environments

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).

Zenil, Hector; Gershenson, Carlos; Marshall, James A.R.; Rosenblueth, David A. 2012. "Life as Thermodynamic Evidence of Algorithmic Structure in Natural Environments." Entropy 14, no. 11: 2173-2191.