The Natural Medium as Carrier of Meanings and Their Decoding by Living Beings:  Biosemiotics in Action

The synthetic, integrative significance of biosemiotics as a modern interdisciplinary research program is under discussion in the article. Aimed at studying the cognitive and life activity of living beings, which are capable of recognizing signals and extracting the meanings, biosemiotics serves as a conceptual node that combines some important notions of theoretical biology, evolutionary epistemology, cognitive science, phenomenology, neuroscience and neurophilosophy as well as the theory of complex adaptive systems and network science. Worlds of perception and actions of living beings are built in the process of co-evolution, in structural coupling and in enactive interaction with the surrounding natural environment (Umwelt). Thereby the biosemiotic theories developed by the founders of biosemiotics (J. von Uexküll, Th. Sebeok, G. Prodi, H. Pattie) are conceptually closed to the system-structural evolutionary approach developed in synergetics by H. Haken and S.P. Kurdyumov, the conception of autopoiesis (H. Maturana and F. Varela), second-order cybernetics (H. von Foerster), the conception of enactivism in cognitive science (F. Varela, E. Thompson, A. Noë). The key to comprehending the processes of extracting and generating meanings is that every living organism lives in the subjectively built world (Umwelt), so that its Umwelt and its internal psychic organization become parts of a single autopoietic system. According to the well-known expression of G. Bateson, information is a not indifferent difference or a difference that makes a difference. Differences become information when a cognitive agent as an interpreter, acting as part of an autopoietic system, sees signs in these differences that make meanings.

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The Impossibility of Automating Ambiguity

Artificial Life ◽

10.1162/artl_a_00336 ◽

2021 ◽

pp. 1-18

Author(s):

Abeba Birhane

Keyword(s):

Cognitive Science ◽

Complex Adaptive Systems ◽

Social Life ◽

Adaptive Systems ◽

Social Systems ◽

Complexity Science ◽

Human Behaviour ◽

Complex Adaptive ◽

Embodied Cognitive Science ◽

The One

Abstract On the one hand, complexity science and enactive and embodied cognitive science approaches emphasize that people, as complex adaptive systems, are ambiguous, indeterminable, and inherently unpredictable. On the other, Machine Learning (ML) systems that claim to predict human behaviour are becoming ubiquitous in all spheres of social life. I contend that ubiquitous Artificial Intelligence (AI) and ML systems are close descendants of the Cartesian and Newtonian worldview in so far as they are tools that fundamentally sort, categorize, and classify the world, and forecast the future. Through the practice of clustering, sorting, and predicting human behaviour and action, these systems impose order, equilibrium, and stability to the active, fluid, messy, and unpredictable nature of human behaviour and the social world at large. Grounded in complexity science and enactive and embodied cognitive science approaches, this article emphasizes why people, embedded in social systems, are indeterminable and unpredictable. When ML systems “pick up” patterns and clusters, this often amounts to identifying historically and socially held norms, conventions, and stereotypes. Machine prediction of social behaviour, I argue, is not only erroneous but also presents real harm to those at the margins of society.

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“Niche Selection” and the Evolution of Complex Behavior in a Changing Environment—A Simulation

Artificial Life ◽

10.1162/106454699568782 ◽

1999 ◽

Vol 5 (3) ◽

pp. 271-289 ◽

Cited By ~ 5

Author(s):

Richard Walker

Keyword(s):

Environmental Change ◽

Complex Adaptive Systems ◽

Adaptive Systems ◽

Theoretical Biology ◽

Control Model ◽

Selection Model ◽

Evolution Of Complexity ◽

Complex Adaptive ◽

Simulation Parameters ◽

Biological Organisms

One of the key problems in theoretical biology is the identification of the mechanisms underlying the evolution of complexity. This paper suggests that some difficulties in current models could be avoided by taking account of “niche selection” as proposed by Waddington [21] and subsequent authors [2]. Computer simulations, in which an evolving population of artificial organisms “selects” the niche(s) that maximize their fitness, are compared with a Control Model in which “Niche Selection” is absent. In the simulations the Niche Selection Model consistently produced a greater number of “fit” organisms than the Control Model; although the Niche Selection Model tended, in general, to produce organisms occupying simple niches, it was nonetheless more effective than the Control Model in producing well-adapted organisms inhabiting complex niches. It is shown that the production of these organisms is critically dependent on the rate of environmental change: Slow change leads to fit but undifferentiated populations, dominated by organisms occupying simple niches; differentiated populations, including well-adapted organisms living in complex niches, require rates of environmental change lying just beyond a mathematically well-defined critical value. In simulation “Niche Selection,” unlike conventional “Natural Selection,” provides a permanent selective bias in favor of simplicity. This tendency is counterbalanced by statistical forces favoring shifts from rare “simple niches” to commoner niches of greater complexity. Fit organisms inhabiting complex niches only emerge in conditions where the rate of environmental change is high enough to avoid the concentration of the population in very simple niches, but slow enough to permit step-by-step adaptation to niches of gradually increasing complexity. This result appears to be robust to changes in simulation parameters and assumptions, and leads to interesting conjectures about the real world behavior of biological organisms (and other complex adaptive systems). It is suggested that some of these conjectures might be relatively easy to test.

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