“If scientists believe themselves capable of foresight, and demand serious consideration for their ongoing contributions to society, it is not enough for them to simply continue scientific production. A scientist who wishes to possess any meaningful foresight must know how the sausage is made: what are they feeding us, and why? Biology has operated as a reductive machine throughout the 21st century, the time has come for it to begin acting like a body”. (39)

“These major assumptions in conceptual reasoning illuminate the core intention of bottom-up ontologies. These are ontological approaches which functionalize human beings and create hierarchies of human mechanics by virtue of functionalization. This bottom-up ontology bears a striking resemblance and even intellectual history with First-Order Cybernetics. The delineation of First and Second-Order Cybernetics begins with Margaret Mead’s address at the first meeting for the American Society of Cybernetics in 1969. During this address she detailed a novel approach to cybernetics which highlighted a massive theoretical oversight: First-Order Cybernetics could not relate subjects to objects. As an approach it was severely limited because it was incapable of considering observers as actors, unlike the nascent Second-Order Cybernetics. As Heinz von Foerster noted:

First Order Cybernetics is the Cybernetics of observed systems. 
Second Order Cybernetics is the Cybernetics of observing systems.

The First-Order primarily characterized observations as atomized feedback systems. Cyberneticians in the late 60s noted that they failed to account for the observer relation, the systems of input and output that relate cognitive description with the objects scientific observers describe. As a fundamental disagreement, Second-Order cyberneticians like Humberto Maturana and Francisco Varela excluded living systems from the reduction of relations to input and output:

“Autopoietic machines do not have inputs or outputs. They can be perturbated by independent events and undergo internal structural changes which compensate these perturbations. If the perturbations are repeated, the machine may undergo repeated series of internal changes which may or may not be identical. Whichever series of internal changes takes place, however, they are always subordinated to the maintenance of the machine organization, [a] condition which is definitory of the autopoietic machines. Thus any relation between these changes and the course of perturbations to which we may point to, pertains to the domain in which the machine is observed, but not to its organization. Thus, although an autopoietic machine can be treated as an allopoietic machine, this treatment does not reveal its organization as an autopoietic machine.” — Humberto Maturana and Francisco Varela

As a description of the disagreement between biomolecular reductionism and the systemic principles of autopoiesis, the conflict between First and Second-Order Cybernetics serves as allegory for those ontological differences. The First-Order approach to a biological question is to observe the inputs and outputs of an organism and to consider this as an adequate description of that organism’s function or differentiation. It applies its logic recursively, imagining every biological aspect as nested sets of smaller cybernetic cycles operating in isolation. This demands a fragmented, hyper-specialized approach to biology wherein each of these components is considered in its own specialized terms. There is a clear connection between this approach and the current biopharmacological paradigm. It is an inherently functionalized description. To conceive of the gravity of Maturana and Varela’s alternative hypothesis, we must imagine the biology of Second-Order Cybernetics. In what ways would its premises and conclusions differ? As a negative exercise, which aspects of contemporary biology and its relation to the social world would no longer be meaningful?” (89)

[...]

“In Tiqqun’s characterization, cybernetics is an engine of homogenizing political atomization. It is the breakdown of all elements of vertical, structured, and intentional organization into horizontal, atomized, and constantly updating systems intended to maintain information exchange through the erasure of systemic qualities. An illustrative example of this practice was the reinvention of manufacturing during the Third Industrial Revolution, as in shoe production.

Whereas most shoe manufacturers handled all aspects of a shoe’s construction, marketing, and sales, Nike reinvented footwear by horizontalizing its production. Nike’s production contracts were auctioned to manufacturers on a shoe-by-shoe basis, and even marketing was bid on by external companies. The central bureaucracy of Nike was reduced significantly, and a more granular form of production was born. Aided by information technology, a centralized, coordinating structure was no longer necessary for industrial coordination; even the traditional physical proximity of factories was obviated. With well-defined information channels, each aspect of an assembly line can be isolated and separated from the centralized structure and shipped out globally, functionally prescribed through a process of description.

This process of decentralization has defined the 21st century and has deeply affected governance and politics–often by the ceding of public ventures to private contractors. Much the same as cybernetic biology and its recursive logic of smaller and smaller units, cybernetic social and political structures break into smaller and smaller atomized processes.” (98)

“The crux of reductionism’s limitations lies here. Without a previously-conceived model of systems and their generalized capacities, it is not possible to account for or predict the significance of data-level knowledge gaps. While there are many statistical tools designed for approximating and defining the degree to which an observation ‘explains’ or accounts for a result, in an investigation with substantial gaps in observational depth this can be a highly misleading or outright deceptive metric.

Imagine an experiment focusing on a cell’s response to a particular drug in vitro as intricately as is currently feasible , involving the sequencing of methylation sites, protein and transcript isoforms, expression levels, protein splicing and modification, chromatin accessibility, and any other relevant sequencing array. Regardless of whether synthesizing these numerous datasets is computationally possible, let alone practically feasible, this experiment will be so heavily constrained in specifics as to be non-abstractable, with many of these data points being highly interrelated and with interactions unrelated to the fundamental investigative question. The meaningful properties of the system would not necessarily be illuminated through the addition of this knowledge, and in fact, the complexity would be obfuscating, which is why most scientists apply critical models towards the importance of some aspects over others. Almost all biological investigations hone in on the aspects perceived to be most critical, often a particular stratum of biomolecular processes. It is important to recall the historical pitfalls of biological reduction to a particular stratum, primarily the excess belief in DNA-coded phenotypes throughout the 20th century.

The reduction of biological investigation into core aspects based on the predictions of a scientist is a primary component of model-making and has defined biology throughout its history. The concern at hand is in which orientations one should generate models. If it is possible to abstract complicated processes into questions relating to only a handful of the parts involved, then a biologist has many choices in making those selections. Biologists may view this as a suggestion that an investigation can choose any handful of data or observational qualities from the ‘bucket’ of an organism’s features (epigenetic processes, microbiome relations, genetic variations, etc.), but this is a much larger proposition. Whether a biologist views themselves as orientated in a paradigm, they choose central viewpoints to position their investigations, many of which have no foundation beyond function. It is possible to ask biological questions almost completely unrelated to the individual elements contained in the data bucket.” (116)

[...]

“To understand this approach, we can turn to the 1959 paper on visual cognition undertaken by Jerome Lettvin and a team including a young Humberto Maturana, What the Frog’s Eyes Tells the Frog’s Brain:

“The assumption has always been that the eye mainly senses light, whose local distribution is transmitted to the brain in a kind of copy by a mosaic of impulses. Suppose we held otherwise, that the nervous apparatus in the eye is itself devoted to detecting certain patterns of light and their changes, corresponding to particular relations in the visible world. If this should be the case, the laws found by using small spots of light on the retina may be true and yet, in a sense, be misleading. Consider, for example, a bright spot appearing in a receptive field. Its actual and sensible properties include not only intensity, but the shape of its edge, its size, curvature, contrast, etc. 
            We decided then how we ought to work. First, we should find a way of recording from single myeliniated and untmyelinated fibers in the intact optic nerve. Second, we should present the frog with as wide a range of visible stimuli as we could, not only spots of light but things he would be disposed to eat, other things from which he would flee, sundry geometrical figures, stationary and moving about, etc. From the variety of stimuli we should then try to discover what common features were abstracted by whatever groups of fibers we could find in the optic nerve. Third, we should seek the anatomical basis for the grouping.”

The compelling aspect of this paper–though it is by no means the only possible systemic approach is that it begins with a predictive framework for how perception as a general category may be understood. It integrates fundamental assertions of how a perceptive agent might discern its surroundings using light in a general formation, outlining the systemic appeal of patterns over pure light detection. Then from this systemic plane the investigators can make particular predictions in terms of a frog’s visual physiology. This is a systemic property that informs a physiological manifestation.
            A hasty evaluation of this paper may lend the impression that is no different from any simplistic ‘scientific method’ and its hypothesis-observation-modification cycle, but this ignores crux ontological aspects which distinguish this work. This paper importantly does not constrain itself to a hypothesis in the conventional sense. It does not suggest anything about a frog’s vision or the anatomy of an eye, and it makes no suggestion of an observable confirmation. It instead suggests a higher-level capacity of organisms capable of visual perception, which is the ability to deduce patterns instead of graded brightness. From there it returns to the biological model to interrogate the implications of this suggestion on particulars. It is an attempt to confirm a systemic property through an appearance in the observable biology, not to define a systemic property as the simple appearance of its base biological components.” (121)