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path can be also seen with information theory. Data acquisition, processing, transmission, and reception are biological facts, not only in humans but in other animals. Without the trouble to define what information is now, it is clear that different human societies have come up with different ways of sending informative data from one point to another. From spoken language to books, from smoke signals to pigeons, data can be transferred in space and time, always facing the possibility of error. With the radical changes brought by the industrial revolution, quicker data transmission across longer distances was desired [5]. The first telegraphs appeared in the early 1800s; the first transatlantic telegraph dates to 1866. By the end of the 19th century, the first communication networks were deployed in the United States. Interestingly enough, the development of communications networks depended on amplifiers and the use of feedback control, which have been studied in control theory.

      Even with a great technological development of communication networks, mainly carried out within the Bell Labs, transmission errors had always been considered inevitable. Most solutions were focusing on how to decrease the chances of such events, also in a sort of highly complex trial‐and‐error fashion. In 1948, in one edition of The Bell System Technical Journal, C. E. Shannon published one amazing piece of work stating in a fully mathematical manner the fundamental limits of communication systems utilizing a newly proposed definition of information based on entropy; some more details will come later in Chapter 4. What is important here is to say that, in contrast to all previous contributions, Shannon created a mathematical model that states the fundamental limits of all existing and possible communication systems by determining the capacity of communication channels. One of his key results was the counter‐intuitive statement that an error‐free transmission is possible if, and only if, the communication rate is below the channel capacity. Once again, one can see Shannon's paper as the birth of a scientific theory of this new well‐determined object referred to as information.

In the same year that Shannon published A Mathematical Theory of Communication, another well‐recognized researcher – Norbert Wiener – published a book entitled Cybernetics: Or Control and Communication in the Animal and the Machine [6]. This book introduces the term cybernetics in reference to self‐regulating mechanisms. In his erudite writing, Wiener philosophically discussed several recent developments of control theory, as well as preliminary thoughts on information theory. He presented astonishing scientific‐grounded arguments to draw parallels between human‐constructed self‐regulating machines, on the one side, and animals, humans, social, and biological processes, on the other side. Here, I would like to quote the book From Newspeak to Cyberspeak [7]:

      Cybernetics is an unusual historical phenomenon. It is not a traditional scientific discipline, a specific engineering technique, or a philosophical doctrine, although it combines many elements of science, engineering, and philosophy. As presented in Norbert Wiener's classic 1948 book Cybernetics, or Control and Communication in the Animal and the Machine, cybernetics comprises an assortment of analogies between humans and self‐regulating machines: human behavior is compared to the operation of a servomechanism; human communication is likened to the transmission of signals over telephone lines; the human brain is compared to computer hardware and the human mind to software; order is identified with life, certainty, and information; disorder is linked to death, uncertainty, and entropy. Cyberneticians view control as a form of communication, and communication as a form of control: both are characterized by purposeful action‐based on information exchange via feedback loops. Cybernetics unifies diverse mathematical models, explanatory frameworks, and appealing metaphors from various disciplines by means of a common language that I call cyberspeak. This language combines concepts from physiology (homeostasis and reflex), psychology (behavior and goal), control engineering (control and feedback), thermodynamics (entropy and order), and communication engineering (information, signal, and noise) and generalizes each of them to be equally applicable to living organisms, to self‐regulating machines, and to human society.

      In the West, cybernetic ideas have elicited a wide range of responses. Some view cybernetics as an embodiment of military patterns of command and control; others see it as an expression of liberal yearning for freedom of communication and grassroots participatory democracy. Some trace the origins of cybernetic ideas to wartime military projects in fire control and cryptology; others point to prewar traditions in control and communication engineering. Some portray cyberneticians' universalistic aspirations as a grant‐generating ploy; others hail the cultural shift resulting from cybernetics' erasure of boundaries between organism and machine, between animate and inanimate, between mind and body, and between nature and culture.

We can clearly see a difference between the generality of information and control theories with respect to their own well‐defined objects, and the claimed universality of cybernetics that would cover virtually all aspects of reality. In this sense, the first two can be claimed to be scientific theories in the strong sense. The last, despite its elegance, seems less a science but more a theoretical (philosophical) displacement or distortion of established scientific theories by expanding their reach towards other objects. This is actually a very controversial argument that depends on the philosophical position taken throughout this book, whose details will be presented next.

1.4 Philosophical Background

      Science is a special type of formal discourse that claims to hold objective true knowledge of well‐determined objects. Different sciences have different objects, requiring different methods to state the truth value of different statements. A given science is presented as a theory (i.e. a systematic, consistent discourse) that articulates different concepts through a chain of determinations (e.g. causal or structural relations) that are independent of any agent (subject) involved in the production of scientific knowledge. This, however, does not preclude the importance of scientists: they are the necessary agents of the scientific practice. Scientific practice can then be thought as the way to produce new knowledge about a given object, where scientists work on theoretical raw material (e.g. commonsense knowledge, know‐how knowledge, empirical facts, established scientific knowledge) following historically established norms and methods in a specific scientific field to produce new scientific knowledge. In other words, scientific practice is the historically defined production process of objective true knowledge. Note that these norms, despite not being fixed, have a relatively stable structure since the object itself constrains which are the valid methods eligible to produce the knowledge effect.

      Moreover, scientific knowledge poses general statements about its object. Such a generality comes with abstraction, moving from particular (narrow) abstractions of real‐world, concrete objects to abstract, symbolic ones. Particular variations of a class of concrete objects can be used as the raw material by scientists to build a general theory that is capable of covering all, known and unknown, concrete variations of that class of objects. This general theory is built upon abstract objects that provide knowledge of concrete objects. However, this differentiation is of key importance since a one‐to‐one map between the concrete and abstract realities may not exist. Abstract (symbolic) objects as part of scientific theories produce a knowledge effect on concrete objects, understood as realizations of the theory, not as a reduction or special case. At any rate, despite the apparent preponderance of abstractions, the concrete reality is what determines in the last instance the validity of the theory (even in the “concrete” symbolic reality of pure mathematics, concreteness is defined by the foundational axioms and valid operations).

To illustrate this position, let us think about dogs. Although the concept of dog cannot bark, dogs do bark. Clearly, in the symbolic reality in which the concept of dog exists, it has the ability of barking. The concept, though, cannot transcend this domain so we cannot hear in the real world the barking sound of the abstracted dog. Conversely, we all hear real dogs barking, and therefore, any abstraction of dogs that assumes that they cannot bark shall not be considered scientific at all. This seems trivial when presented with this naive example, but we will see throughout this book the implications of unsound abstractions in different, more elusive domains. This is even

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