Evolutionary Process Epistemology and Cognitive Landscape

The theory of evolution operates assuming that organisms under environmental pressure become extinct or adapt to new conditions. This is quite clear but a rigorous explanation is lacking. Evolutionists state that only the strongest survive, and that mutations create the condition for survival of the best-adapted, or survival of novel genotypes that appear by chance. Assuming a population is heterogeneous in terms of genome and is under a predictable environmental pressure, we can expect the maintenance of such heterogeneity, although we ignore the precise mechanisms. Often in science we state a principle and then we invoke a complicated procedure (statistical or numerical) to demonstrate our assumption. In this way science becomes cryptic and self-explanatory. I believe that life is composed of processes that are basically extremely simple. Complexity appears when we move from individuals to aggregations like populations, communities, meta-communities, systems. The Ptolemaic system, not the Copernican, was complicated. We can't understand a fact if this fact is not distinct from a background.

The cognitive landscape is a novel paradigm, and the principles that result open a new perspective on landscape science.

If a cognitive landscape is the space in which the life web is connected with all the possible relations and interactions of a collection of elements, the organism should have a holistic vision of such a landscape. An organism is not an observer in such a domain, but an active component without the capacity to describe the surroundings as they occur to an observer (man). At this point, it seems difficult to find a good metric able to capture quantitative data from this vision. A possibility is to utilize individual-based "sensory perception" and to move from an operational domain to a descriptive domain. Most metrics used in landscape ecology are related to descriptive domains and not to operational domains.

Later we will discuss this point which is fundamental to a more robust science of landscape in greater detail.

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