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I will begin with a brief critical analysis of some structural axioms, which form the foundation of society and thinking today. These axioms are typically “swept-under-the-rug,” hence hiding them from criticism, while also entrenching their status, creating a closed oligopoly-like scenario. This paper aims to bring hidden links out from under the rug so that an open culture of critical reflection and change can start by reflecting upon those very foundations.

It was René Descartes (1641) who separated the (non-physical) mind (as pure thought, knowledge, objective, and universal truths) from the physical body, emotions, experiences, and empiricism. In doing so, Descartes’s body of work/thought presented two important structural axioms to the world: (1) Cartesian mind–body dualism and (2) Cartesian co-ordinates. The former is of relevance here.

In keeping with the goal of not separating thought from experience or emotions, I integrate observations and experiences of my own regarding present academic cultures. Cartesian dualism presents itself in academic culture, whereby self-experiential evidence in academic work tends to be a taboo. Another instance is that qualitative analyses, which integrate human psychology or phenomenology, are reserved mainly for philosophical, psychological, or theoretical journals. Typically, the “hard science” journals are reserved for major quantitative results that are supported by empirical evidence. Unfortunately, the idea that qualitative hypotheses or studies belong in specific locations (and not others), because the “tastes” of an audience will not be satisfied, presupposes the audience’s desires and their status as static. A preclusion of this sort serves to reduce the freedom of choice of the audience and entrenches a strong division between subject matter. This hinders the evolution of knowledge (since hidden links remain hidden in this culture). Why would literature about human psychology be any different from literature about astronomy? Does thought and logic not structure both? Previously, Copernicus believed that the Sun revolved around the Earth, thus centralizing the human as being the universal subject (Deutsch, 1998). Now, it seems that the human, as the observer, represents another type of universal, being the universal object, which is apparently removed from that of which it is a part. Both are incorrect.

Immanuel Kant then opened the doors for “relativity” (Kant demonstrated logically that the concept of the “Universal” must be limited in some way). However, Kant (1890) backtracked from the radicality within his oeuvre by positing instead that the antinomies (contradictions) of pure reason are the limits of reason, not the limits of reality itself. In this way, as accused by Hegel, Kant remained attached to pre-critical metaphysics, which posits a realm of purity, like the allegory of the Platonic cave (the truth is outside of the cave, not within it).

The Kantian Copernican revolution thus created a split between epistemology and ontology. This is a split between epistemology (as philosophy, qualitative analyses, theory, logical operations, interpretive methods, technique, structure, systems, form, objectivity, thoughts about thought itself, and universalism) and ontology (subjective knowledge, practice, content of thought, experience, phenomenology, empiricism like the sciences, and quantitative analyses). I suggest that this accurately reflects the very notion of “interdisciplinary,” which only serves to entrench (1) unfounded distinctions and (2) the Kantian axioms pervading academic culture. This is a closed system since it resists any critical analysis of its very own presuppositions/assumptions (resisting change as a result). This is contrary to what the scientific method was supposed to be and reflects a stifling of imagination, creativity, and critical thought—all of which are necessary for an open system. We often tend to think of philosophy, or to be more accurate, epistemological assumptions, as being displaced from everyday life, but we do not see how those very assumptions (from thinkers like Kant and Descartes, for example) structure everyday life, all disciplines, and all sources of knowledge.

There are two reasons for the maintenance of this fib. The first is the raison d’etre of capitalism, wherein relations between individuals are treated as transactional relations between objects (Marx, 1848; Althusser, 2014). An exemplar of the aforementioned is the logical structure of Churchillian dialectics, which involves reciprocal causation (Naidoo, 2023d). Churchillian dialectics is a process where subjects function as objects that freely contract with other objects in society to create/consume objects. These object relations are thought to satisfy desire and provide fulfillment (Althusser, 2014), given that these objects seemingly validate a subject’s sense of freedom of choice and the ability to contract freely. This is known as the classic liberalist interpretation of freedom—which is freedom of choice in relation to objects. Marx (1848) called this commodity fetishism.

The commodity fetish presents freedom as being tied to objects, and thus, freedom is increased where there is a greater number of objects to choose from. However, while freedom of choice between or of objects may be increasing, meta-freedom, which is the concept of freedom of choice itself, is decaying. In other words, people have more choices related to objects but less scope to construe freedom as something else or choose among different types of freedoms. Unfortunately, this involves a degradation of qualitative freedom. Increasing degradation relating to the scope of meta-freedom is axiomatic of an unhealthy and closed society, as Theodore Adorno (Horkheimer and Adorno, 1989) and Louis Althusser each argued in their various works. For additional information on freedom and unfreedom, choice, and the paradigm of relations between objects that characterize modern society, please refer to part E of Supplementary Material.

The second, as Sapolsky (2017) demonstrated, is that the dopamine system in the human brain does not support the capitalistic understanding of “satiation through objects.” The dopamine system uses objects for the pursuit, which is the goal (thus, this is contrary to the typical capitalistic understanding, as expressed previously). This was originally a Freudian insight, where Freud described the “objectless drives” (or the Lacanian object-cause-of-desire) (Naidoo, 2023b). Importantly, increases in abundance, in terms of object access, result in less uncertainty, and thus less dopamine release upon acquisition. More predictability leads to less dopamine (and hence that good feeling). Thus, neurobiology/neurochemistry does not support the notion that satisfaction is obtained through choices among material objects. Rather, satisfaction is geared toward the pursuit of an uncertain trajectory.

Marxist dialectic materialism is different from Churchillian dialectics, in that the former posits a reciprocal constitution instead of reciprocal causation (Naidoo, 2023d). This paradigm has been confirmed by scientific evidence. Sapolsky (2017) pointed out that the prefrontal cortex, as the executive region of the brain, only matures in a human’s late twenties and is more susceptible to contextual influences, as opposed to genetic ones. Furthermore, Dawkins (1976), in The Selfish Gene, demonstrated that phenotypes develop a kind of freedom from their genetic constituents and are more susceptible to contexts as opposed to said constituents. Dialectical materialism thus highlights the importance of contexts within the paradigm of development. Contexts, as described below, enable more meta-degrees of freedom precisely because contexts modulate rapidly in open systems.

In terms of the second reason for maintaining the fib, the capitalistic system requires that subjects repress, ignore, or are prevented from grasping the hidden links between different subjects, not dissimilar to the hidden variables hypothesis, or the EPR paradox, formulated by Einstein–Podolsky–Rosen (Einstein et al., 1935). For the capitalist system to maintain itself, these links must remain hidden. If links remain hidden, people are less likely to question the appropriability of the system itself.

Hegel (2018), who preceded Marx, introduced the concept of “embodied cognition” (as the substance equals the subject). This has since come to the forefront within the sciences and ethico-regulatory conversations (thanks to developments in artificial intelligence) (Juarrero, 2023). However, these discussions do not go far enough; although they demonstrate the false separation between the mind, body, and emotion; for example (Damasio, 2005), they do not seek to mend the Kantian split. Lastly, the maintenance of this fib, and the entrenchment of a strict divide of the disciplines, serves to hamper the cohesion of the overall total distributed network, which is that of knowledge acquisition (described below). To assist the reader, a brief navigational map is included in part G of the Supplementary Material.

The European Enlightenment greatly impacted the foundations of all system-building, including legal, economic, social, political, financial, ethical, religious, and scientific systems. The European Enlightenment took three main forms: (1) a political thesis for better governance; (2) a philosophical thesis for a secular foundation based on rationality and science; and (3) an economic thesis on creating more wealth (Mokyr, 2012).

What allows knowledge, ideas, and thought to flourish is an open society (Popper, 1945; Thaldar, 2017). An open society is one in which the thoughts of mad men, who go against the grain, are not rejected or punished as they were in 17th- and 18th-century Britain. These rebels must be protected and afforded the space to be the mad men that they are to protect the openness of a society; it is the acts of such mad men that ensure that society reflects upon itself. As described by Žižek (2003) and Naidoo (2023a), it was Immanuel Kant who put the first crack in the concept of the Universal, the infinite, or the concept of “objective,” followed by others, such as Hegel (2010) (a complete ontology of incompleteness), Karl Marx (1848) (false consciousness), and in mathematics, Gödel (a conditional mathematics), George Cantor (the Cantor set describes a limited infinity), and Hans Peter Luhn (the computer science concept of hashing, which is analogous to the Cantor set) (Naidoo, 2023b).

The core of liberalism stemmed from the above, which is the marketplace of ideas (the marketplace is the necessary condition for the social contract). In the marketplace, ideas compete, fight, coalesce, triumph, and hibernate. The marketplace has a structure that reflects varying interests, like economic, social, and political interests (Mokyr, 2012). None of these interests solely determine outcomes within the marketplace, and each interest typically has varying degrees of importance; there is thus another marketplace within the marketplace. The ultimate marketplace, within which all others are nested and structured, is the theory of reality. The ultimate boundaries of a marketplace are thus ideological and based on theories of knowledge and modes of structuring or validating said knowledge. Each mode or theory turns on questions like, what makes knowledge possible? What is persuasive? What kinds of evidence and logic are possible? What kinds of experiments are necessary? How do we structure what the content of truth is? What does it mean to be correct? What is a true statement?

To provide answers or to construct new questions and answers, it is important to determine whether the structures of any given marketplace being visited are suitable or unsuitable. Typically, debates on this issue lead to reflections involving the Industrial Revolution and the Enlightenment (Mokyr, 2012). The English Enlightenment concerned the removal of ancient and conservative governance structures and values. However, this new modern form seems to have just translated some of the older values into a different language (Mokyr, 2012). The English Enlightenment was not in opposition to Protestantism; rather, it took the Protestant ethics and created a new, secular society around it. The new capitalist society is Protestant, often without knowing it. Herein, a liberal form of Christianity was used to justify the pursuit of one’s self-interests, which perfectly suited the industrial epoch. Hence, science, politics, law, ethics, religion, and the like are shaped by the same schema.

In the 18th century, leading up to the invention of the steam engine, science was very different from today. The common quip is that the invention of the steam engine did more for science than science did for the invention of the steam engine. This “pre-modern” science shaped important developments within empiricism, which became known as the “scientific method.”

There were many important discoveries in the sciences, such as formulae and understandings relating to heat, thermodynamics, and electricity (Rosen, 2010), which catalyzed perspective shifts as to how the world, physics, and chemistry worked. Given these discoveries and the “force model” imposed by Newton, causation and determinism were the dominant ideas of reality. This catalyzed a move away from the teleological purposiveness, inherent to Aristotelian philosophy and methodology (Naidoo, 2023d).

The scientific endeavor involved testing conclusions, ideas, and premises and falsifying non-repeated results since science, at this time, believed that invariance and physics should dominate knowledge validation and status as such (given the huge successes of the cause–effect relations of Newton’s force models). What science sought out was evidence in the search for knowledge, and in doing so, it also moved away from the influence and dominance of the Catholic Church. From this cultural shift, the concept of “replication” was born, and inventing was viewed as a social enterprise, instead of a private one. Previously, inventing was seen as the practice of a “lone genius” (Rosen, 2010). The currency for inventors during this time was mostly recognition or fame (Rosen, 2010).

The notion of perfection, ideal, objective, or universal, at least in Western scholarship, traces back to Plato (2002) and his World of Ideas and Pure Forms. Kant and others built off these ideas through the introduction of a priori pure forms which structure the mind, experience, and thought.

Technological advancements in this period were nominal, mostly consisting of simple improvements on instrumentation and navigational tools (Rosen, 2010). However, there were many novel understandings, scientific principles, formulae, and logical deductions that were understood as a priori knowledge. Society at that time, did not benefit from the unearthing of a priori concepts. In other words, no tangible value in the form of usable forms of technology was acquired from their derivation. Developing tangible or usable forms required a lot of time and other resources, much more than one individual could possibly provide. Hence, inventing became a social endeavor, wherein many could collaborate, thus sharing resources and responsibilities in the production process (enhancing usefulness, accessing, recording, storing, working, and exploring different avenues). This is what a distributed, networked system is. Hence, the scientific method sought value in the form of physical objects, and the production of the said value required bridges to be built between a priori concepts and utility; theory and empiricism; and theorists and artisans (Rosen, 2010; Mokyr, 2012). The scientific method, as the vital cog, transformed the Age of Enlightenment into the age of utility, objects, and the later Industrial Revolution. The purpose of the formation of the 1836 Select Committee on Arts and Manufactures (Sproll, 1994) was for the committee to determine the best ways in which to disseminate knowledge and principles in the fields of knowledge production and that of makers or manufacturers (Sherman and Bently, 2003). The singular value of utility through the transformation of knowledge into physical objects/products was siphoned into the jurisprudence of patent law. Inventing, in law, was now solidified as a social enterprise, which included the acquisition and validation of data and knowledge. The dominant paradigm prior to this Enlightenment was that of the Lockean private property. In this view, knowledge was understood to be within the personal domain of a person and thus private. Importantly, this underlies the typical conception of personal identity and privacy, which many are unaware of being traced back to Locke. The purpose of the Enlightenment was to bring out, and ensure transparency and access of/to information and knowledge, which was hidden away (hence the name “Enlightenment”) under the veils of preceding religious and cultural practices (Dolar, 1991). This was the first move toward an open society, based on information and knowledge being freely accessible.

The scientific method ushered in a new understanding: to enhance conversion and utility, knowledge and data had to be recorded and shared for testing. Knowledge was then understood to be conditional on trust and not absolute (Rosen, 2010). Knowledge and conclusions could be improved and replaced. No longer did logic hold center stage as the singular source of validation. Experimentation became the defining methodology in the social enterprise of knowledge (Rosen, 2010).

Unfortunately, the “practical” or utility justification of innovation pervades many academic explorations, and the necessity and difficulties associated with obtaining funding only serve to entrench four fibs: (1) scientific empirical experiments are necessary to validate knowledge; (2) there is a strong separation between “thinking” and “doing.” As demonstrated by thinkers such as Kant, Hegel, Freud, and Marx, knowledge can be validated through thought alone (second-order inferences). Sapolsky (2017) also echoes the argument that one does not need to perform empirical experiments to simply confirm something that is observationally clear. Fib (3) is that there is a strong separation or difference between science and religion. The final fib (4) is that there is such a thing as progress. Taleb (2007) demonstrates that progress is not a linear concept (nor averageable, much like knowledge), nor is there any consensus as to what constitutes progress. Given that neither progress nor knowledge is linear, both are impossible to evaluate in extremist systems.

The breakthrough of communications theory, later dubbed “information theory,” occurred when Claude Shannon and Weaver (1964) published their book The Mathematical Theory of Communication. Both felt it was a mathematical theory of communication, as opposed to a theory of information. The mathematical formulations therein describe the necessary conditions for communication using concepts like symbols, signals, and carriers. Therein, Weaver described information as the measure of one’s degree of freedom of choice when selecting a message (Shannon and Weaver, 1964).

Shannon and Weaver (1964) were concerned with transmitting information, and not meaning. Thus, Shannon created a formula that could transmit messages in the presence of noise. This formula for encoding messages with maximum efficacy was the same as the one created by Ludwig Boltzmann half a century earlier (Hidalgo, 2015). Both formulas treated information as physical. For Shannon, informational entropy is the minimum volume of data necessary to specify any type of message (Shannon and Weaver, 1964).

In this paradigm, information is meaningless (and meaning is not information); it is the receiver/interpreter/perceiver who weaves-in/transmits meaning into information. It is not valuable either. Meaning and information are often confused because of this automatic (unconscious) transposition (Taleb, 2007). This creates the illusion of a hidden depth within the information. This automatic transposition occurs because of a need to reduce information and conserve energy. Thus, meaning is not the message, and meaning lies within the receiver, contexts, and prior knowledge (Hidalgo, 2015). Meaning is a tool used to communicate the physical order of things.

Meaning (dubbed semantics, from here, as semantics are associations of meaning) exists in various spaces and times, within everyday life, and history. Depending on the spatial, temporal, or spatiotemporal co-ordinates, the semantic content of each co-ordinate will vary. Different co-ordinates or contexts entail different semantics, and vice versa. Hence, semantics and co-ordinates are co-constitutive (Naidoo, 2023d); semantics constitute spatiotemporal coordinates as much as spatiotemporal constitutes semantics. Semantics are also not just the product of, or constituted by, human brains, but rather exist as varying types of constraints, which can be context-dependent or context-independent (Naidoo, 2023d). Nonetheless, human beings do bias semantics within spatiotemporal co-ordinates, within their favor (as individuals or as a species), to maintain their viability and structure relations within a networked, distributed system. The semantic content of a co-ordinate is constructed through processes of trial and error and jury rigging, testing, validation, correction, updating, and rejection. These processes require assessment criteria, which bias some semantic content over others, known as selection criteria. Selection criteria are also the product of context-dependent or context-independent constraints. For example, human society would not designate that a volcano is a semantically suitable location for a school, but in human history, it was a suitable location (the heat and noxious fumes are context-independent constraints) for sacrificing virgins to appease the gods. Hence, the semantics of contexts shape human selections.

What is required to maintain the semantic content of a spatiotemporal location is continuous, repeated observations and assessments, known as measurements. It was Sigmund Freud (Naidoo, 2023b) who first suggested that repetition is not something that is defined by humans but something that defines humans (Lacan, 1979). Hence, the validity or continuous appropriability of semantic content is determined through repeated observational or experiential assessment (measurements) consisting of taking in, keeping up-to-date, and updating factual information in the form of events. Importantly, events are not removed from observation; observation and events are reciprocally constitutive.

Understanding of David Chalmers’ (1996; 2010) “hard problem of consciousness” requires knowledge of what preceded the hard problem. In 1799, Friedrich Heinrich Jacobi (di Giovanni and Livieri, 2018) wrote a letter to Johann Gottlieb Fichte in which he expressed his unhappiness about the loss of subjectivity, which arose because of Spinozism and the rationalistic physical sciences. Jacobi wanted to “save” humanity from the perils of nihilism, tasking Fichte, as the “true disciple” of Kant, with this duty (di Giovanni and Livieri, 2018). In The Spinoza Letters (Spinoza, 1995), Jacobi quite clearly foresaw that humanity would be saved by a return to inner experience and feeling (affect) (di Giovanni and Livieri, 2018). However, thoughts like Fichte’s only served to entrench nihilism, which Hegel argued, since there was a reliance on “spurious infinities,” such as the linear flow of time or advancement. Neither time nor progression is linear (Deutsch, 1998).

An important development of the human mind (and of identity) was John Locke’s (1860) An Essay Concerning Human Understanding, wherein Locke proposed an account of the passive mind as a tabula rasa. Locke’s mind simply served to reflect what was perceived. This passive mind was challenged by others, like Freud, who proposed, in his topological economic theory of mind, that the mind was active. In Freud’s (1915) breakthrough work, The Interpretation of Dreams, he proposed that the separation of the conscious and the unconscious was a defensive threshold, implemented by the psyche, to protect against high energy levels of states and excitation. The purpose of this defense was to maintain a dynamic equilibrium/homeostatic stability. The mind is active. Consciousness and unconsciousness were separated because excessive energy is damaging; the unconscious, which contains the highly energy-invested (bestsum) forms of thought and ideas, is limited by consciousness. In other words, consciousness exists as nothing but a limit to energy. These views have been confirmed by modern science (refer to part D of Supplementary Material).

Chalmers (1996; 2010) also queried why there exists the subjective “I.” Naidoo (2023a; 2023c), relying on Lacanian thought, and Benveniste (1966) noted that the “I” is a stabilizing referent. The purpose of the “I” is to indicate the spatial location/identity of the speaker. At a deeper level, and importantly, the subjective “I” represents a resistance to symbolic representation, meaning a failure to obtain a positive identity through positive knowledge of what the “I” is. The resistance to symbolic incorporation (a deadlock or knot) is important for maintaining an open system and society, as argued by Althusser (2014) in his critique of the overdetermining effect of state apparatus. In other words, the “I” serves as an irreducible, indivisible link enabling constitutive couplings, interrelations, and entanglements that serve to create an autocatalytic feedback loop between a system and its previous context [which now becomes a “niche” (Naidoo, 2023d)]. This constitutive coupling enables for the structuring of language, identity, and knowledge. Indivisible knots are used to construct distributed, networked systems and maintain their status as “open,” which is a dynamical (non)equilibrium state of stability (homeostasis). The content of the “I” is “unknowledge,” meaning that there is no positive knowledge as to what it is, but rather knowledge as to what it is not. Consensus on the “I” is thus reached through determinate negation, instead of affirmation. Consensus is thus constituted through a lack of positive affirmation. I call this constitutive consensus dis-consensus, which in social matters takes the form of “we agree to disagree.” Importantly, this kind of reasoning is analogous to the Kantian infinite judgment, the Gödel incompleteness theorem, or the Hegelian logic of Aufhebung (or chirality, present in biology and chemistry) (Naidoo, 2023b). To examine the appearance of the “I,” we see that it is constituted by two parallel horizontal lines and a single perpendicular line joining the two, as in Figure 1 below.

In other words, the parallel lines are kept apart but also kept together by a vertical line, which separates and joins them. Returning to Hegel (2010), this is precisely the logic of Aufhebung, or chirality, which describes the holding apart, while simultaneously holding together something. They are held together by what I (based on Lacan) call a pure difference (Naidoo, 2023b). That pure difference is an indivisible link, as described. When turned on its side, it resembles Figure 2, transitioning from the subjective “I” (also the “I” of information) to the “H” of homeostasis.

The logic of Aufhebung (Naidoo, 2023b) is coupled with that of an autocatalytic-feedback-loop, akin to the Belousov–Zhabotinsky chemical reaction, wherein the presence of a fourth step (the observer in the sciences) encloses an open-dynamism intrinsic to a system (Juarrero, 2023). Such a logic provides the necessary enabling conditions for a system to incorporate contextual information into its very constitution, increasing its informational content (Yoshida, 2010). The Hegelian dialectic is topological (relating to circles)—being similar mathematically to Poincare’s cobordism, which he explained in Papers on Topology: Analysis Situs and Its Five Supplements (Poincaré, 2010). The four categories of cobordism are almost mirrored by Hegel’s four-fold infinities (Naidoo, 2023b). Cobordism describes how two circles can be morphed into two other circles, like a pair of pants (Dimitrov, 2015). In Figure 3, the Hegelian fourfold infinities are represented. In Figure 4, the Hegelian ontology, which produces an “internal pair of pants” within the original, is presented. Figure 3 represents what is known as autopoiesis, self-causing logic, or Aufhebung.

Both Aufhebung and autocatalytic feedback loops are necessary conditions for open systems, open societies, and the creation of a dynamic equilibrium (a stable state of non-equilibrium, or homeostasis). It is necessary to note that knowledge is not static, but dynamic and evolving, in such systems. On a neurobiological level, the subjective state, as pointed out by Sapolsky (2017) and Naidoo (2023b), operates as a simulated, stochastic risk management strategy aimed at maintaining the viability of the system itself.

Information is a measure of the degree of surprise obtained by an agent who observes or experiences an event (thus the link to probability). This degree of surprise of an event is described by self-information. Information (I) is thus a function called self-information. Self-information is the informational content inherent to any event or occurrence. Information entropy extends this idea to discrete random variables (X). The entropy of (X) is the average of self-information over all possible outcomes of (X). Conversely, the entropy of a random variable describes the average degree of surprise obtained by the outcome of (X). There is an increased information gain after a surprise, as opposed to expected or predicted events. Information gain is tied to reductions in entropy. Entropy, to conclude, is about the degree of surprise obtained from outcomes based on prediction (part B of Supplementary Material).

Any ontology needs to begin with a definition; so, what is information? Currently, there is no qualitative consensus on an account of information; only a compendium of various and vague axioms exists (Floridi, 2009). We know that information is quantifiable, additive, storable, and transmittable. It is also a golden thread that runs through all disciplines (Deacon, 2007). It is certainly incorporeal and intangible because one cannot physically handle or manipulate information. In the 21st century, the importance of information increased, resulting in heavy commodification (Badiou, 2006; Hidalgo, 2015). Information was thought to be physical because of its physical embodiment. It was not construed as a “thing,” but instead a physical arrangement, taking the form of a thing. Information was that which differed from its surroundings, based on its identity, which is its appearance, or the physical order of its arrangement (Hidalgo, 2015). As time passed, the nature of information morphed into being understood as digital, immaterial, and non-physical.

A founding father of cybernetics, Norbert Wiener (1961), provided a negative (exclusionary) definition of information in cybernetics. Wiener (1961) said that

The last part of this quote is insightful; information is not matter or energy. This is a negative definition since Wiener does not propose to know what information is, but he suggests what it is not. Although information is neither matter nor energy, it needs matter for embodiment and energy for its communication.

Entropy is a limit on efficient communication of the outcome of (X). In other words, it describes how much compression can take place while maintaining the efficiency of the communication. In communication theory, there is a correspondence between the base of the logarithm and the symbol quantity, which is used in a hypothetical scenario involving two agents who communicate surprising, random events (Brownlee, 2019). Any change in the base of the logarithm results in a corresponding change in the entropy equation (Bernstein, 2020). This means that informational entropy describes the minimum number of symbols (lower bound) needed to communicate an outcome of (X). The base of the logarithm of the self-information function is also the lower bound on the number of symbols required to communicate, as above.

Information is expensive (energy-wise) (Taleb, 2007) to obtain (the rule-finding, updating, learning, and executive region of the human brain, being the prefrontal cortex, is highly metabolic) (Sapolsky, 2017). It is similarly expensive to store, order, manipulate, and retrieve information. To combat this, systems need energy-efficient ways to handle and store information. The solution to the energy-efficiency issue is to order information, thus making information less random, which requires an association or attachment to narrations, words, or symbols (Taleb, 2007). As Shannon and Weaver (1964) demonstrated, for efficient communication, strings of symbols and signals can be used. Shannon (1940) also demonstrated that some symbols will be used more frequently than others (the source of the code will have a higher frequency with regard to certain symbols or knowledge). Based on this, it was possible to assign shorter codes to more frequently occurring symbols (including symbol pairings), reducing the total length of the required code. Natural language processing (in artificial intelligence) uses the same principle, namely, the frequency of a letter depends on what precedes the said letter. This is Shannon’s empirical entropic frequency distribution formula (Hartnett, 2022). This is a predictive functionality. Importantly, Hawkins and Dawkins (2021) demonstrated that the brain functions according to a single cortical algorithm—which is prediction. Humans build their own subjective (and inter-subjective) world-maps according to this system, comprising spatiotemporal semantic relations.

In other words, compression is fundamental for energy efficiency. Compression is achieved through the creation of tunnels, called narratives, wherein the dimensionality of information is reduced (Taleb, 2007). A side effect of compression or reduction is that vast chunks of information are typically ignored. Along with reduction, compression requires an active function of repeated, continuous assessment and observation, wherein information is abstracted (reduced in dimensionality). The abstracted information is reduced through the combination of both ignoring chunks and the creation of narrations (association links or chains of sequential chunks of observed events). These selective narrations, as sequential observations, are known as patterns, which connect events, thus making prediction possible.

Patterns are abstractions or representations of reduced information. Abstraction (as opposed to concretization) is fundamental to mathematics and physics. Abstraction describes the derivation of non-physical patterns. Concretization refers to the creation of physical objects. Abstraction is similar to Plato’s (2002) concept of ideal. The transformation of the Platonic ideal into the language of sciences by philosophy is important to understand. It is this concept that structures others, like Newton’s universal clock. The Platonic and Aristotelian triadic structure consists of three components: (1) the physical/material world; (2) the mental world; and (3) the world of structures. Abstraction is thus the mental process of removing properties from an (X), followed by attaching a name/identity to those properties. These abstractions belong in the world of mentality, which can be individualized or collectivized.

Patterns enable efficient informational manipulation and storage. It is impossible to use and store all available information, given the several orders of magnitude of energy required. Hence, only useful bits or patterns are used to make generalized knowledge possible. Examples/descriptions of patterns include summaries, compressions, narrations, episodes, sequences, slices, and foliations of information. From patterns, rules or laws can be derived, which are generalized forms of knowledge (Woodward, 2000). Generalized knowledge is that which is judged to be invariant (Woodward, 2000) for a given set of contexts, thus holding the status of governing or ruling constraints (Juarero, 2023), which allows for greater predictability, less uncertainty, and the performance of experiments (which test and maintain the validity of the said rule or law).

Rules and laws, as governing constraints, are compact, and because of their reliability value (Woodward, 2000), they enable functions to occur with less energy expenditure, while also enabling coherence, comprehensibility, and understanding. Media, in the forms of books, magazines, plays, stories, videos, movies, paintings, poetry, and all sciences are based on this principle of compressed bits of information (Taleb, 2007). Patterns also take the form of ideas or concepts.

The name of this compressed information, such as patterns, ideas, concepts, episodes, movies, or any of the aforementioned, is data. Data describe identifiable, embodied, encoded, or nested patterns within various physical mediums, structures, or forms (like vehicles). In a non-physical medium, such as a mind, patterns or data exist in the form of ideas, concepts, or thoughts, each of which is largely represented by analog encoding through synchronized neuronal spiking and synaptic connections in the brain (Hawkins and Dawkins, 2021). These sequences form through continuous, repetitive observations. As Hawkins and Dawkins (2021) note, world maps are built up of reference frames (mental structures), and thinking is a virtual movement through reference frames.

Observation, I suggest, is a dual function. The passive form of observation is known as measurement, quantizing, foliation, and hermeneutics. These are repeated processes of automatic assessments and updates. These passive observations consist of subjective, yet autonomic behaviors, aimed at updating and maintaining a system’s world map (Hawkins and Dawkins, 2021). In doing so, these processes maintain the system’s dynamic equilibrium. Observations are thus subjectively sliced into qualitative and quantitative “pieces” of space, time, or a combination of both. Passive observation can occur through perception and experience (if it is possible to even distinguish between them). Observation is thus precisely a method of biasing information in favor of the observer.

Embodied knowledge, as passive observation, is difficult to acquire, communicate, store, and copy. The knowledge and knowhow contained in the human body (and mind) is “heavier”; knowledge and knowhow contained in objects is relativity easier to move, as objects can be carried and communicated through mediums, such as books and the internet (Hidalgo, 2015). Embodied knowledge is comparatively slower to acquire. This includes technical proficiency like scientific, programming, and legal skills/techniques. It is known as expertise. It includes the knowledge and abilities of other team members and knowledge of contextual circumstances. Embodied knowledge is biased in terms of sociality; it is accumulated and translated through social learning and experience. Beginners learn from more experienced persons; thus, it is not an individual endeavor. There is a social and experiential learning curve, which makes its accumulation time-consuming and limits the speed at which individuals can develop it. Embodied knowledge also biases geographic locations, which have greater quantities of some quality. To solve the issue of embodied knowledge distribution, society breaks up knowledge and knowhow among different individuals in a distributed, networked structure.

These individuals then utilize their specific forms of knowledge and knowhow as a social network of individuals performing as a team (Hidalgo, 2015). Through networks, the collective body of this knowledge and knowhow can be increased, which is greater than what an individual can produce. Importantly, these networks must be able to distribute knowledge evenly and ensure there is cohesion and a combination of individual parts to produce the result. It is harder to maintain a cohesion rather than to ensure everyone performs their roles. The whole is thus greater than the sum of its parts. For this, there must be timeous and performative cohesion, shared responsibility, social practice, updates, corrections of mistakes, assessments, proper communication, and trust.

The Freudian “id” described the automatic, unconscious aspect of Freud’s topological mind. In modern terms, this would describe the lizard brain, which performs autonomic functions like maintaining homeostasis in the body (Sapolsky, 2017). As I have described, patterns or ideas describe subjective (or relative) perspectives intrinsic to the constitution of any pattern or data. In other words, ideas are inherently linked to the concept of identity. The processes of observation and abstraction described above are automatic functions of the brain and body (Hawkins and Dawkins, 2021). Both observation and abstraction aim to maintain autonomic stability or homeostasis (Sapolsky, 2017). These are autonomic/automatic forms of applied reason; hence, the separation between observation and reason is misplaced. Reason and observation are reciprocally constitutive; memories, for example, influence reason, and reason influences and alters memories (Taleb, 2007). Reason is thus not something which one does; reason is something which one is. In this light, obtaining patterns is then not a matter of a strong form of labor, expenditure, autonomy, or creativity. As argued below, it is the modulation of contexts, which are in a reciprocally constitutive relation with subjects, which enables patterns to emerge. These are known as discoveries.

The active form of observation, which I define as assessment (or meta-assessment, to be more accurate), entails active selection and direct participation. Meta-assessment would constitute a strong form of labor, expenditure, autonomy, and creativity in the form of second-order inferences, as described below. These are considerations of meta-suitability or meta-reasoning, which are examples of reasoning about the various modalities (and viabilities) of reason itself. In other words, this process involves biasing certain forms of reason (and, by extension, the products of reason) over others.

Reasoning is the name given to processes used in abstracting and compressing (and ignoring) information to form sequential narrations. Reasoning is thus a technique or the various employed methods of constructing sequences, forming patterns, and collecting data. The various types or techniques of reasoning are called inferences. Inferences (like induction, deduction, and abduction) are used to validate, falsify, cast in doubt, maintain, or update sequences or patterns. Hence, reasoning involves constructing various types of coherences (different techniques can obtain different patterns or informational content from the same information). Some techniques are more contextually suitable than others.

The applications of these various techniques serve to slice subjective perceptions or experiences (Hegel, 2010; 2018). This process converts each piece (through construction) into spatiotemporal sequential patterns, as functionally usable or functionally relevant data. Reasoning enables the construction of associative semantic relationships (a semantic network or semantic web) between observational and/or experiential information with a spatiotemporal location within a subject’s world map. In other words, reasoning creates an interrelation of dependencies. Using reason, subjects can construct their own affordances (affordances are advantages or adaptations, which enable and create agency) based on the value and suitability of said data, in a given context. The data’s functional usage has value since it enables the subject to persist (delay the thermodynamic equilibrium of the second law of thermodynamics), maintain, or enhance its viability values and update its world maps (Naidoo, 2023d). Second-order inferences are processes of repeated meta-assessment. They are analogous to the Freudian death-drive (Naidoo (2023b). In psychoanalytic terms, this oscillation is known as hysteria (Žižek, 2014).

I conceive second-order inferences as those that simultaneously target current and previously obtained data (including memories). These inferences also target the reasoning techniques used to obtain said data, including any data obtained about the catalog of reasoning techniques, thus determining the appropriability of the data and the applied techniques (in terms of maintaining a dynamic equilibrium by ascertaining the viability value of data relating to reason and data relating to obtained data).

Second-order inferences are thus observations about observations or thoughts about thoughts or reason about reason (hence, meta-reasoning). These inferences are typically ignored in favor of observed data (Hossenfelder, 2016), especially in the sciences. These kinds of inferences are often labeled as “mere philosophy” in my experience and ignored. However, they are most important since they ensure that a system remains open and viable, or, in Popper’s (1945) understanding, a closed system of totalitarianism does not ensure. These inferences are aimed at questioning the natural or accepted order of things and serve to undermine settled positions by demonstrating their inherent contradictions, a la Hegel. In other words, transposed into Schrödinger’s (1944) terms, second-order inferences enable a system to persist, avoid their (systems) own entropy increases, and thus avoid thermodynamic equilibrium associated with heat death. Second-order inferences are thus those that maintain a stable-non-equilibrium state, known as a dynamical equilibrium, using negentropy (Schrödinger, 1944). In psychoanalytic theory, a closed system is one that has psychotic foreclosure, wherein “things” are accepted without question. The goal of psychoanalysis is to move a subject from a state of psychotic foreclosure to a state of hysteria.

Three important questions to answer are as follows: (1) when does data become knowledge? (2) what are the conditions for the acquisition of the status of knowledge (and acquisition of knowledge as such)? and (3) what is learning?

The first two questions are strongly linked and can be dealt with concurrently. Data becomes, or is converted into knowledge only upon gaining a certain grade of trust. Trust grading is based on various considerations, such as the invariance (Woodward, 2000) and the value of the data, both of which are related to the aim of maintaining a system’s dynamic equilibrium. Thus, knowledge is data trusted to maintain or enhance a system’s dynamic equilibrium. To establish trust, there must be repeated observations and assessments (mainly second-order inferences). The observations and assessments must also target modes of data acquisition/creation, like sampling frequency, error rates, subjective and contextual conditions associated with sampling, the timeframe of the sampling, the sample size, integration with other knowledge, and many others. Trust, like knowledge, is thus context-sensitive or relative and needs to be continuously maintained. Both trust and knowledge fluctuate, degrade, or modulate slower in comparison to contextual information and data acquisition. In this way, data and knowledge (as context-dependent constraints) influence and reciprocally constitute one another. Contexts, as a concept, I understand as being the overarching context-independent constraint. Trust and knowledge exist in a dynamic equilibrium, both serving to maintain an open-dynamic-equilibrium system state. Repeated assessment is necessary, not just repeated observation.

In terms of the truth–knowledge dichotomy, knowledge is not absolute (universal or objective) but relatively-absolute. Relatively absolute, instead of absolutely relative, highlights the distinction between a postmodern insight and insights into postmodernism. The former would posit that knowledge is completely contextual, which would delight Locke (given his passive mind) and a contextualist like Jacques Derrida. The latter, on the other hand, would suggest that the former replaces one false universal/idol (absolute objectivity or universality) with another false universal/idol (absolute relativity). Thus, the latter attacks the concept of absoluteness/universality itself. Hence, absolutely-relative can be explained with reference to art: in societal terms, art cannot be absolute subjectivity or just anything anyone says it is. Art is a singular thing; that singular thing is where current consensus lies, particularly in art, and it is situated in an art gallery. In Kantian–Lacanian terms, art, or knowledge, is that which is sublime or imbued with fundamental fantasy (Naidoo, 2023b). The sublime, or the fundamental fantasy, is trust. Absolutely relative, as the postmodern, contextualist account of knowledge, typically ignores the underbelly consensus of unknowledge or that which is deemed not trusted. For example, if I write a paper about the origins of life, which is then disproven by someone else, it may seem like the state of knowledge has not been improved. However, this is not true because invalidation itself serves as a reduction. The state of knowledge knows what does not describe the origins of life, which is my invalidated paper.

As pointed out by Popper (1962), the aim of science is not truth but rather knowledge. Knowledge is conditional; knowledge is only acquired as such through confirmation, repeated assessment, and integration (Woodward, 2000). Science thus aims to build generalized knowledge. Truth is impossible to obtain, argued Popper, because there are infinite paths in history from which knowledge could have originated, making the endeavor of obtaining truth fruitless. Science thus does not prove; science only confirms through corroboration or refutes. This means that while knowledge may be relative to space, time (epochs), or spatial–temporal locations, it nonetheless is relatively absolute since knowledge is only knowledge as such if it contains selective trust (which is an expression of societal autonomy). Hegel (2010), anticipating Popper, presented this insight in a different way through his explication of the necessity of contingency. Facts are thus forms of knowledge, which have a higher trust value and appear most often in a social context; but facts are not truth.

Knowledge and trust are thus based on consensus. Consensus is ultimately about building-in, indivisible, irreducible knots into a distributed, networked system (a multi-agent system) (OpenCSF, n.d.). This allows a system to function and maintain a dynamic equilibrium. The issue is that consensus is often interpreted in a closed manner, meaning that it requires the constitution of consensus to be an agreement among all participants or agents. However, as voting within politics demonstrates, the presence of a winner does not mean that there is consensus.

Total agreement is unnecessary when the function of consensus, which is irreducibility, is unearthed. In this light, consensus can also be a dis-consensus, meaning a consensus based on a failure to reach consensus. This kind of conflict takes the form of “we agree to disagree.” This is an irreducible link, which entangles polemic positions in such a way that it keeps the overall system in an open, dynamic equilibrium (hysteria). Žižek (2008) calls this “oppositional determination,” which, in computer science, is known as “a split brain” in distributed, networked systems (the split brain can be traced back to Kant, who demonstrated the split between understanding and reason. This split was confirmed by Sapolsky (2017) as analogous to the neurobiological workings of the amygdala and the prefrontal cortex. Hegel also described the idea of the split as “unhappy consciousness”). I call this a pure difference, which is how Lacan described the way sexual difference is articulated in society (Naidoo, 2023b). It is not that there are differences between polar positions or contested points of view; instead, a meta-difference is introduced, wherein the difference itself is conceived differently. If difference itself is construed differently, there can be no consensus, and a dis-consensus results. Dis-consensus ensures that there is a radical enclosure of openness within a distributed networked system, enabling the persistence of its status as dynamic, as opposed to static. This is also known as an open society in political terms. In this society, opposing sides remain linked while in a state of continuous observation and assessment because of their very (intentional, unbeknownst to them) oppositional determinations. It enables a system of this sort to continuously and dynamically seek out new gradients of energy or information, which are relevant for viability maintenance. Hence, it is not that identities are used for violence; rather, intentional, conceptual violence is performed to create identities. The creation of an indivisible knot as such, which structures a split brain, requires the use of second-order inferences.

On question 3, learning is the process of repeated observation, assessment, validation, correction, storage (memorization), updating, degrading, and relating useful or important semantic, spatiotemporal information within a system’s world map. It is thus an active process intrinsically related to the constitution (construction) or destruction of data, knowledge, and trust. This is the dual role of imagination. Imagination is absent in a closed system.

Ralf Launderer (1961) suggested that information, as a mathematical object, plays a crucial role in physics. His intention was to find the minimum energy required for computation using standard thermodynamics. He used the Launderer reset, which comprises a starting state (say 0) and a binary switch, the latter of which consists of “1” and “0.” Each binary state is a possible logical state for this binary switch. This operation is often referred to as “information erasure” since it reduces the amount of information that can be associated with the binary switch. Before operation, there are two possible states; after operation, there is one. According to thermodynamics, a reduction in the number of possible states for a physical device requires a minimum energy expenditure, which is computable thanks to Boltzmann’s equation. Launderer (1961) then proceeded to deduce the logically irreversible concept, arguing that it implies physical irreversibility. His ultimate deduction was thus that information must be physical. However, this spawned much research into logical reversibility, famously by Charles Henry Bennett (1973) and others. Bennet demonstrated that Launderer erred (refer to part B of Supplementary Material).

The claim that information is physical has been refuted, and the current consensus is that it is not physical. An experiment at the NiPS laboratory demonstrated that the logically irreversible gate can be reversed logically with a small amount of energy expenditure (López-Suárez et al., 2016), concluding that there was no fundamental limit and that reversible logic is not required to operate computers with zero energy expenditure. This means that there is no limit as to how much we can lower energy consumption during computation. In turn, this means that information cannot be physical; it only has a physical representation (Burgin and Mikkilineni, 2022).

Information and physical carriers/representations are different. Different physical carriers can carry the same kinds of information (different brands of pens still carry the information, that it is a pen, used for writing, despite different appearances). Studies like those done by Vopson and Lepadu (2022) demonstrate valuable insights into the teleological movement of information but also have (in conjunction with later studies) incorrectly described the nature of information by confusing physical carriers with information. The properties of informational representations and informational carriers are different (Burgin and Mikkilineni, 2022). Information is never directly interacted with; rather, methods of dealing with informational representations and carriers of information are used, like computation, for example. Humans are prone to conflating metaphorical symbols with literals due to the recent evolution and organization of the brain, with the prefrontal cortex being an honorary member of the emotional limbic system (Sapolsky, 2017).

The current consensus is that information is massless (Burgin and Mikkilineni, 2022). The physical representation of information has mass, which means it would comply with physical laws. This is important to keep in mind.

Information forms part of the world of structures. In the physical world, entities like genes and neurons process, communicate, and convert information into data (and then knowledge). They communicate information first through a representative analog form, such as biological and neurological structures. Second, communication of such form is achieved with the use of chemical or electrical signals (Burgin and Mikkilineni, 2022). For example, “bits” in the digital world are information, which can have many physically representative forms (like symbols, electrical voltage, or pulses). Information is “carried” by these physical representations in the same way that temperature is “carried” by thermometers (Burgin and Mikkilineni, 2022). The General Theory of Information (GTI) describes and distinguishes the properties of information from those of representations and carriers of information.

Material structures in the physical world carry information, which represents the state and the dynamics of the analog structures mentioned (Burgin and Mikkilineni, 2022). In the physical world, physical or material things are governed by the transformation laws of matter and energy; energy can create or change material structures. All physical (including chemical) structures, which are created or changed by the transformation of matter and energy, are governed by and obey transformation laws. Hence, this is how physics distinguishes what is physical from what is not.

All physical structures contain information, which characterizes their structures, functions of their components (including the interactions of the components with their surroundings), and their behaviors upon the occurrence of fluctuations. Factually, there is a relationship between the characteristics of physical objects, which allows for the conversion of mass into the energy of physical objects described by these characteristics. Einstein’s mass–energy equivalence equation, E = mc², interlinks the energy and mass of physical objects. However, this formula does not mean that substance (matter) is equal to energy; rather, it describes the maximal amount of energy in a physical object with a given mass.

Thus, the states of physical structures and the regularities of their evolution are described by the laws of physics, which are mental structures created by humans. “Living” organisms developed physical structures, which exploit matter and energy transformations, to acquire unique identities and the ability to sense and process information, which is carried by material/physical structures. They can do this by converting it into data or knowledge, which are mental structures.

All living organisms have varying degrees of perceptive, processing, magnification, and information-to-data-to-knowledge conversion abilities. Humans can typically represent and manage mental structures using ideal structures or categories like named sets or fundamental triads (Burgin, 2010). Triads provide the schema, including the necessary operations for creating organized forms of data and knowledge, like entities, relationships, and evolutions, based on events and behaviors (Burgin et al., 2020; Mikkilineni, 2022a; Mikkilineni, 2022b). These are world maps.

Events are caused by (1) fluctuations in the interactions among the components of structures and (2) fluctuations among components and their niches (Naidoo, 2023d). Function, structure, and fluctuations play important roles in a system’s microscopic and macroscopic behaviors (Prigogine, 1978). Mental models, created by information processing, are observer-dependent, as they are conditional on subjective foliations, previous knowledge of the observer, and various other idiosyncratic variables.

According to this principle, information plays the same role in the world of structures as energy plays in the physical, material world. Despite this link, information is not part of the physical world. It can only be materialized in a physical form (Burgin and Krzanowski, 2022; Burgin and Mikkilineni, 2022).

For any portion of information (I), there is always a representation (R) for this (I) in a system. This representation is often material, and because of this, information seems physical (Burgin and Mikkilineni, 2022). The physical representation, rather, is the materialization or manifestation of this information and is not the same kind of thing as the information itself. This material form enables the possibility of social exchange, given that it allows other subjects to read, process, obtain, and transfer information. DNA is an example of an inanimate transformation and transmittance of information from one physical representation to another. It is the physical/material representation of information that complies with physical laws, and not the information itself. Mental processes themselves are also not physical; they are tied to something physical, like the brain, but are themselves not (Davis et al., 2012). In other words, semantics are not physical.

In terms of this principle, information in any system can precipitate the potential for, or cause, transformations within the system itself (like changing its structural or logical elements) (Burgin and Krzanowski, 2022).

According to this principle, for any part of information (I), there is always a representation (R) of this part of (I) for a system (S). (R) is a material representation of said information, and it is only (R) only that obeys physical laws.

In terms of this principle, for any information (I), there is always also a carrier (C) in a system (S). As a rule, (C) is typically material; hence, (I) is present in the material world. (C) is an instance of materialization of the information, which I call the second level materialization sub-principle (or SLM for short). Consider this example: a piece of paper, as a carrier, requires the materialization of symbolic information (enaction or inscription, in the form of writing letters forming a language) via an instrument, such as a pen. In this example, information is materialized and hence present in the material world when embodied within a carrier, but the materialization in the form of the inked-in written words is only a physical representation of information. The symbols, being the letters of the language, are also carriers. This is supported by Shannon and Weaver, who separated the message from semantics.

Thus, any (C) of (I) is a physical something within which (I) is embodied. A physical (R) is also a physical (C) if it allows for the direct extraction of the said information. The key difference between (C) and (R) is that any physical representation is a physical carrier, but not every physical carrier is a physical representation (Burgin and Mikkilineni, 2022).

To illustrate, consider the following: an envelope is a physical carrier of information (the envelope contains a paper letter with writing on it). The paper letter is also a physical carrier of the same information as the envelope since the information embodied within both the envelope, and the paper letter is the text written on the paper. Given that direct extraction is only possible through viewing the text (reading it), it is not possible to extract this text from the envelope without opening it and reading the letter. One also cannot directly extract the text from the paper letter itself but only from the visible writing embodied on the paper letter. For example, if the letter is written in a visible foreign language, being in possession of the letter does not mean that one can extract the information embodied within it. It is the visible symbols themselves, from which direct abstraction is possible and proceeds (not the paper letter). Hence, the envelope and paper letter within it are only (C). Neither, however, is (R) of the information contained within the letter, since the extraction process cannot be performed on either the paper letter or the envelope. Hence, the difference between FLM (representations) and SLM (carriers) is that FLM comprises SLM, but SLM does not necessarily comprise FLM. A (C) of (I), which is not an (R) (like the envelope or paper letter), is called an enveloping carrier of (I).

The mental worlds of living biological organisms are structured by scaffolding (Naidoo, 2023b). Information obtained from the environment through the senses enables mental representation, which is then converted into mental structures in the form of triads. There are two types of mental structures: (1) those derived from external observations and (2) those created by human minds serving to represent ideal structures. Mathematics is used to represent ideal structures and operators; it is also used to model systems from the material world, their states, and their evolution (Burgin and Mikkilineni, 2022). The mental world/reality contains different mental structures that are involved in transforming information and data into knowledge. These processes are physical processors, namely, genes and neurons.

Information is non-physical (Timpson, 2004; Timpson, 2008; Timpson, 2013), but it is tied to physical and mental structures and processes. Informational (R) and (C) are embodied in other physical and mental structures (Burgin and Mikkilineni, 2022). If the physical (R) is altered, the information changes too. Erasing a representation (like erasing writing)results in (R) losing its status as such since it would no longer embody information. The status as a (C) likewise can be constituted or un-constituted as such.

Symbolic (R) of information is involved in logical or abstract computation (like linguistics), whereas physical computation works with physical (R) and (C) of information (Burgin and Mikkilineni, 2022). GTI locates information not in the world of abstract objects (information exists in things outside of mentality) but rather within the world of ideal structures. Information appears in mental and physical worlds through materialization and mentalization. Abstract objects are mental representations of information from the world of ideal structures. They are structures themselves, but they do not belong in the world of ideal structures; they are rather external structures within the general theory of structures.

With regard to living organisms, information can be conceptually (not physically) separated into ontological information and mental information. The former is that which precipitates formations and transformations of structures within the physical world/physical systems (Burgin and Krzanowski, 2022). Ontological information functions within the physical world; hence, it is used in treating natural phenomena. Mental information (also known as epistemic information), on the other hand, is that which facilitates formations and transformations of structures within the mental world/systems (Burgin and Krzanowski, 2022).

According to GTI, physical energy is a type of generalized information situated within the physical world and is that which precipitates the changing or preserving of physical systems (Burgin and Krzanowski, 2022). There is a key difference between ontological information and energy as generalized information; the former (as genuine information that can precipitate alterations or preservations of physical systems) acts only on physical systems, which have physical representations, and are embedded in a physical carrier. The latter, energy, directly acts on physical systems. Ontological information can also have physical energy as its representation (Burgin and Krzanowski, 2022). This position has also been supported by the demonstration of Maxwell’s demon in laboratories (refer to part B of Supplementary Material) (Hossenfelder, 2016). Information can be converted into work. This means that it is possible to replace the transfer of energy from a sender to a receiver with a transfer of information, and this information transfer can occur with much less energy than what the receiver gains from the information (Hossenfelder, 2014).

GTI also distinguishes between mental/epistemic information and mental/psychic energy. Mental/psychic information is generalized information in the mental world that can precipitate the change or preservation of mental systems. Mental information, as epistemic information, is genuine information that can precipitate changes or the preservation of mental systems because of the way it behaves. The difference between both is that mental/psychic information directly acts on mental systems, while mental information, as epistemic information, acts only on systems with a mental representation and embedded within a mental carrier. For example, knowledge is embedded within the mentalities/minds of people. Mental information can have mental energy as its representation (Burgin and Krzanowski, 2022).

Following Hayek (1945) once more, we can obtain answers regarding the issue of data migrations. It is possible instead to send algorithms to personal/private data storage instead of the other way around. Data produced by the usage of a migrated algorithm would be attributable to the sending party. This potentially limits the risks associated with legal and ethical cross-border data transfers. In terms of data integrity, a Hayekian approach would suggest leaving the decisions to the man on the ground. A solution is simply allowing data analytics companies (any company that performs analytics on datasets. These can be genomic companies, financial companies, legal companies, and so on) to construct their own tiered quality/integrity approach, wherein they charge different amounts depending on a guarantee the company makes related to the quality of the data. Alternatively, data analytics companies can apply the same tiered approach to in-house analytic tools, which can be applied to their in-house datasets. Higher amounts can be charged for higher-quality data or analytic tools. This way, both parties can decide for themselves and rely on contractual provisions and guarantees to regulate their relations.