What is information? A short answer could be: Anything that changes our minds and states of thinking! Information is virtual. It can be carried by speech, an image, scratches on a stone, patterns in a photon field, the connections of neurons in our brains or even by the wavefunction of an electron. Information does not depend on the actual means of transmission nor does its identity change when changing the carrier. Information is physical. It needs to be sustained by a physical substrate and rearranging that substrate to contain the wanted information requires to spend energy or work. Without a physical world, information can not be stored, processed, or transmitted. Our world is permeated with different representations of information. Actually Information about the physical laws governing our Universe can be found everywhere and in everything. Could it then be that the real fundamental elements of this world are tiny bits of information? Could we get it from bit?1 A number of researcher investigate this possibility, and several of their attempts to recover physical laws from the principles governing information are reported about in this special issue on physics of information. The articles by Gregg Jaeger,2 by Ariel Caticha,3 and by John Skilling and Kevin Knuth4 address the reconstruction of quantum mechanics from information theory. Furthermore, Kevin Knuth and James Walsh5 show how to recover space-time from particles sending each others signals in an initially geometry-free setting. A central element of most works on information is the concept of a probability. Probabilities can be defined via various routes. Most familiar might be their definitions by the frequentists school as relative frequencies of events (in the limit of infinite amounts of such). A more general definition of probabilities is the Bayesians' one, which regards probabilities as quantifiers of statements, expressing how much those should be expected to be true. Although this definition looks subjective on the first glance, it rests on a solid mathematical theorem by Richard Cox6 that any extension of binary logic to a continuum of single valued truthfulness quantifier that respects a minimum of requirements must be isomorphic to probability theory. And this definition turns out to embrace the frequentists' definition in case that infinite numbers of comparable events are actually available and can be counted. The majority of authors of this special issues seem to favor the Bayesian perspective, which is probably a result of our own, the guest editors', preference and not necessarily a reflection of the prevailing position of contemporary scientists. The different views on probabilities of frequentists and Bayesians are addressed by the article of Allen Caldwell.7 He builds a bridge between these antagonistic camps by explaining frequentists' constructions in a Bayesian language. In particular, his argumentation why the omnipresent p-value in statistical and experimental literature might often capture a relevant signal of model misfit is reassuring, given that a large fraction of our medical system is based on decisions backed up by p-values. A Bayesian might add that it would be even more reassuring, if a system rigorously build on logic and its continuous extension would be used for decisions affecting live-or-death questions. The laws governing probabilities themselves seem to reflect that of more elementary mathematical structures. Kevin Knuth shows how these emerge from lattice theory, which deals with relations in partially ordered sets like that of collections of possibilities.8 The sum rule of probability theory is a direct consequence of the underlying lattice structure. Interestingly, a number of other theories are resting also on such lattices and therefore exhibit their own versions of sum rules: measures of sets, mutual information, the Euler characteristics of topological spaces, as well as quantum mechanical interactions that are quantified by pairs of numbers, the real and imaginary parts of complex probability amplitudes (see also4). The theory of information has very practical application in physics and elsewhere. Manfred Opper discusses how the solutions of stochastic differential equations can be inferred via variational methods, which have their origin in thermodynamics.9 Torsten Enßlin introduces information field theory (IFT), the information theory for fields, as a means to infer the configuration of a physical field from incomplete and noisy data.10 The mathematical apparatus for this is that of quantum field theory (QFT), just with ordinary probabilities instead of complex probability amplitudes as in the latter. Could it be that the difference between IFT and QFT is just be the same as between classical information theory and quantum mechanics, the transition from real valued to complex valued quantifier for statements? Anyhow, any practical inference machinery, be it for dynamical systems or for fields, requires sophisticated numerical methods. Michael Betancourt reviews Markov Chain Monte Carlo Methods to calculate expectation values over high dimensional probabilities.11 Theo Steininger and coworkers present the NIFTy3 software package for numerical information field theory calculations, which can handle very high dimensional field inference problems.12 Finally, in an article focused on the subtleties of the Monty Hall problem and mostly aiming at information theoretical entertainment, Torsten Enßlin and Margret Westercamp investigate the possibility that human intuition might be more rational than contemporary psychology thinks.13 A number of possible topics for an issue on physics of information are not addressed here. We would have loved to have articles on Landauer's principle, which states that erasing information has to cost energy, on QBism, a Bayesian version of quantum mechanics, on artificial and biological neural networks, as well as any other aspect of the information-soaked biological world. What has a reader of this special issue to be equipped with in order to follow the lines of thoughts? In addition to the usual physicist's education, the minimal additional equipment are the basics of probability theory and the concept of entropy. Most of the other concepts used in the articles rest on those and are introduced there. Let A, B, and C be propositions (or possibilities). The probability P ( A | B ) of A being true assuming that B is true is a number between 0 and 1, with P ( A | B ) = 1 expressing certainty of A under B ( B ⇒ A ) and P ( A | B ) = 0 expressing certainty of not A being the case under B ( B ⇒ ¬ A ).
What is information? A short answer could be: Anything that changes our minds and states of thinking! Information is virtual. It can be carried by speech, an image, scratches on a stone, patterns in a photon field, the connections of neurons in our brains or even by the wavefunction of an electron. Information does not depend on the actual means of transmission nor does its identity change when changing the carrier. Information is physical. It needs to be sustained by a physical substrate and rearranging that substrate to contain the wanted information requires to spend energy or work. Without a physical world, information can not be stored, processed, or transmitted. Our world is permeated with different representations of information. Actually Information about the physical laws governing our Universe can be found everywhere and in everything. Could it then be that the real fundamental elements of this world are tiny bits of information? Could we get it from bit?1 A number of researcher investigate this possibility, and several of their attempts to recover physical laws from the principles governing information are reported about in this special issue on physics of information. The articles by Gregg Jaeger,2 by Ariel Caticha,3 and by John Skilling and Kevin Knuth4 address the reconstruction of quantum mechanics from information theory. Furthermore, Kevin Knuth and James Walsh5 show how to recover space-time from particles sending each others signals in an initially geometry-free setting. A central element of most works on information is the concept of a probability. Probabilities can be defined via various routes. Most familiar might be their definitions by the frequentists school as relative frequencies of events (in the limit of infinite amounts of such). A more general definition of probabilities is the Bayesians' one, which regards probabilities as quantifiers of statements, expressing how much those should be expected to be true. Although this definition looks subjective on the first glance, it rests on a solid mathematical theorem by Richard Cox6 that any extension of binary logic to a continuum of single valued truthfulness quantifier that respects a minimum of requirements must be isomorphic to probability theory. And this definition turns out to embrace the frequentists' definition in case that infinite numbers of comparable events are actually available and can be counted. The majority of authors of this special issues seem to favor the Bayesian perspective, which is probably a result of our own, the guest editors', preference and not necessarily a reflection of the prevailing position of contemporary scientists. The different views on probabilities of frequentists and Bayesians are addressed by the article of Allen Caldwell.7 He builds a bridge between these antagonistic camps by explaining frequentists' constructions in a Bayesian language. In particular, his argumentation why the omnipresent p-value in statistical and experimental literature might often capture a relevant signal of model misfit is reassuring, given that a large fraction of our medical system is based on decisions backed up by p-values. A Bayesian might add that it would be even more reassuring, if a system rigorously build on logic and its continuous extension would be used for decisions affecting live-or-death questions. The laws governing probabilities themselves seem to reflect that of more elementary mathematical structures. Kevin Knuth shows how these emerge from lattice theory, which deals with relations in partially ordered sets like that of collections of possibilities.8 The sum rule of probability theory is a direct consequence of the underlying lattice structure. Interestingly, a number of other theories are resting also on such lattices and therefore exhibit their own versions of sum rules: measures of sets, mutual information, the Euler characteristics of topological spaces, as well as quantum mechanical interactions that are quantified by pairs of numbers, the real and imaginary parts of complex probability amplitudes (see also4). The theory of information has very practical application in physics and elsewhere. Manfred Opper discusses how the solutions of stochastic differential equations can be inferred via variational methods, which have their origin in thermodynamics.9 Torsten Enßlin introduces information field theory (IFT), the information theory for fields, as a means to infer the configuration of a physical field from incomplete and noisy data.10 The mathematical apparatus for this is that of quantum field theory (QFT), just with ordinary probabilities instead of complex probability amplitudes as in the latter. Could it be that the difference between IFT and QFT is just be the same as between classical information theory and quantum mechanics, the transition from real valued to complex valued quantifier for statements? Anyhow, any practical inference machinery, be it for dynamical systems or for fields, requires sophisticated numerical methods. Michael Betancourt reviews Markov Chain Monte Carlo Methods to calculate expectation values over high dimensional probabilities.11 Theo Steininger and coworkers present the NIFTy3 software package for numerical information field theory calculations, which can handle very high dimensional field inference problems.12 Finally, in an article focused on the subtleties of the Monty Hall problem and mostly aiming at information theoretical entertainment, Torsten Enßlin and Margret Westercamp investigate the possibility that human intuition might be more rational than contemporary psychology thinks.13 A number of possible topics for an issue on physics of information are not addressed here. We would have loved to have articles on Landauer's principle, which states that erasing information has to cost energy, on QBism, a Bayesian version of quantum mechanics, on artificial and biological neural networks, as well as any other aspect of the information-soaked biological world. What has a reader of this special issue to be equipped with in order to follow the lines of thoughts? In addition to the usual physicist's education, the minimal additional equipment are the basics of probability theory and the concept of entropy. Most of the other concepts used in the articles rest on those and are introduced there. Let A, B, and C be propositions (or possibilities). The probability P ( A | B ) of A being true assuming that B is true is a number between 0 and 1, with P ( A | B ) = 1 expressing certainty of A under B ( B ⇒ A ) and P ( A | B ) = 0 expressing certainty of not A being the case under B ( B ⇒ ¬ A ).
Abstract. At this point in time, two major areas of physics, statistical mechanics and quantum mechanics, rest on the foundations of probability and entropy. The last century saw several significant fundamental advances in our understanding of the process of inference, which make it clear that these are inferential theories. That is, rather than being a description of the behavior of the universe, these theories describe how observers can make optimal predictions about the universe. In such a picture, information plays a critical role. What is more is that little clues, such as the fact that black holes have entropy, continue to suggest that information is fundamental to physics in general. In the last decade, our fundamental understanding of probability theory has led to a Bayesian revolution. In addition, we have come to recognize that the foundations go far deeper and that Cox’s approach of generalizing a Boolean algebra to a probability calculus is the first specific example of the more fundamental idea of assigning valuations to partially-ordered sets. By considering this as a natural way to introduce quantification to the more fundamental notion of ordering, one obtains an entirely new way of deriving physical laws. I will introduce this new way of thinking by demonstrating how one can quantify partially-ordered sets and, in the process, derive physical laws. The implication is that physical law does not reflect the order in the universe, instead it is derived from the order imposed by our description of the universe. Information physics, which is based on understanding the ways in which we both quantify and process information about the world around us, is a fundamentally new approach to science.
The ability to transfer information securely is essential in the modern world we live in. While the field of cryptography has long researched ways to do this, events over the last couple of years have brought the issues of secure information transfer squarely into the public eye. As a consequence, security and cryptography have quickly become issues of social and political importance, sparking debates about the values of privacy and the role of government. An essential ingredient of modern cryptography is the generation of randomness: cryptographic techniques are built on the premise that one has access to random bits. It is well known, however, that computers cannot produce algorithmically random sequences – that is, sequences with maximal algorithmic information content – but are doomed to produce ‘pseudorandomness’. This lack of randomness can be, and has been, exploited to compromise security, see [4]. The active field of quantum information theory has proposed approaches to provide supposedly ‘unbreakable’ security by exploiting various quantum phenomena. This security unfortunately relies on assumptions about the nature of quantum measurements and their ability to generate random bits. Anton Zeilinger summarises this by postulating that the simplest quantum systems, qubit, can hold only one bit of classical information [6]. This foundational principle is in line with a wider paradigm shift to view quantum information as an extension of classical information, but it is nonetheless unsatisfying to simply postulate this principle, given its importance in determining the practical advantages of quantum information theory. In order to understand better how quantum mechanics can help generate meaningful information, we instead look to relate the outcomes of quantum measurements to formal properties of the system based on more fundamental assumptions. Indeed, we have shown that a) some of the postulated properties of quantum information follow from the formal structure of the theory and b) a purely formal notion of information within a quantum world can generate, via measurement, meaningful and useful information in the macroscopic world (for example, in cryptography). The indeterminism of quantum measurements can be formalised via the notion of value indefiniteness. To explain this concept, let us consider an arbitrary quantum system and ask whether the outcome of a measurement of any observable quantity A (such as the energy of the system, its angular momentum (spin), etc.) is determined prior to the measurement. If this is the case, then we say the observable is value definite with value v(A); otherwise, the observable is value indefinite and v(A) is undefined. Mathematically, one can reduce all observable quantities to so-called projection observables, which can only take the values 0 or 1. Thus, the question of whether the outcome of several measurements can be simultaneously determined in advance can be rephrased in terms of the information ‘carried’ by a particular system in a definite quantum state. Classically, one expects that all quantities are determined in advance, and hence all observables are value definite. Quantum mechanically, however, the belief is that this is not the case, and the information content of quantum systems is limited. Formulating carefully the notion of value indefiniteness allows us to formalise the notion of (quantum) indeterminism; however, this doesn’t help clarify whether quantum systems are indeed value indefinite or not. Staying in this formal framework, the Kochen-Specker theorem [5] provides a first positive result, showing that at least some observables must be value indefinite if one makes the assumption known as non-contextuality, which states that any definite values that exist must be independent of other compatible measurements that may or may not be performed on the system. Under the same assumption, this theorem can be strengthened to show that only one single observable can have the definite value 1 (see [2]). Furthermore, only observables that can be measured simultaneously with this single one can have the definite value 0; the rest must all be value indefinite. Since the preparation of a system involves precisely ensuring that, usually via measurement, the system is in a definite state with respect to some desired observable, this result shows that no other incompatible observable can be value definite. That is, preparing a system in a definite state by making the ‘preparation’ observable value definite specifies completely the information content of the quantum system. This is an example of syntactical quantum information acquiring meaning at the level of the quantum itself. Can the syntactical information at the level of the quantum generate meaning outside the quantum, that is, at the macroscopic level? The results cited above hold in the Hilbert-space framework of quantum mechanics and are formulated only in terms of a syntactical notion of information. Their real importance becomes evident when one interprets them in the context of quantum measurements. Specifically, if we prepare a quantum system in a known state, they allow us to ‘locate’ observables which we can measure, but which are value indefinite; that is, observables whose measurement results are not specified by any pre-existing property of the quantum system. Furthermore, with respect to a mathematical model of unpredictability which we developed in [3], the results of these measurements can be shown to be absolutely unpredictable. In this way we get a mathematical explanation and justification of the largely accepted intuition that quantum mechanics is inherently unpredictable, and that this unpredictability arises from the phenomenon of indefiniteness within the quantum world. Furthermore, if one considers a hypothetical infinite sequence generated by the repeated measurement of a quantum value indefinite observable, one can prove that these sequences must be strongly incomputable, technically ‘bi-immune’ [1]. Such sequences cannot be generated by any Turing machine or classical computer, showing that value indefiniteness leads to a clear classical/quantum split in a purely algorithmic context. Importantly, bi-immunity is a property of observed, macroscopic quantities, quite separate from the quantum framework in which the value indefiniteness is formalised. This form of macroscopic meaning created from the lack of syntactic information is precisely the scenario that quantum random number generators try to create, and which is essential for the development and certification of quantum cryptographic systems. The fact that the macroscopic information created goes beyond anything classically obtainable serves as a valuable practical resource, outside of and removed from the quantum formalism. To conclude, quantum information creates meaning within the quantum and via measure- ment, the lack of information within the quantum, creates meaning and valuable information at the macroscopic level. Acknowledgments The second author thanks Prof. S. Marcus for useful conversations on quantum information theory. This work was supported in part by Marie Curie FP7-PEOPLE-2010-IRSES Grant RANPHYS. References and Notes Abbott, A.A.; Calude, C.S.; Svozil, K. Strong Kochen-Specker theorem and incomputability of quantum randomness. Physical Review A 2012, 86, 062109. Abbott, A.A.; Calude, C.S.; Svozil, K. Value-indefinite observables are almost everywhere. Physical Review A 2013, 89, 032109. Abbott, A.A.; Calude, C.S.; Svozil, K. On the unpredictability of individual quantum measurement outcomes. CDMTCS Research Report 2014, 458. Calude, C.S. Quantum randomness & cryptology. CyberTalk 2013, 3, 42–43. Kochen, S.; Specker, E. The problem of hidden variables in quantum mechanics. Journal of Mathematics and Mechanics 1967, 17, 59–87. Zeilinger, A. A foundational principle for quantum mechanics. Foundations of Physics 1999, 29(4),631–643.
Nihil est in intellectu quod non prius fuerit in sensu. The Physics of Information This book describes the limits for the communication of information with waves. How many ideas can we communicate by writing on a sheet of paper? How well can we hear a concert? How many details can we distinguish in an image? How much data can we get from our internet connection? These are all questions related to the transport of information by waves. Our sensing ability to capture the differences between distinct waveforms dictates the limits of the amount of information that is delivered by a propagating wave. The problem of quantifying this precisely requires a mathematical description and a physical understanding of both the propagation and the communication processes. We focus on the propagation of electromagnetic waves as described by Maxwell's theory of electromagnetism, and on communication as described by Shannon's theory of information. Although our treatment is mostly based on classical field theory, we also consider limiting regimes where the classical theory must give way to discrete quantum formulations. The old question of whether information is physics or mathematics resounds here. Information is certainly described mathematically, but we argue that it also has a definite physical structure. The central theme of this book is that Shannon's information-theoretic limits are natural. They are revealed by observing physical quantities at certain asymptotic scales where finite dimensionality emerges and observational uncertainties are averaged out. These limits are also rigorous, and obey the mathematical rules that govern the model of reality on which the physical theories are based. Shannon's Laws Originally introduced by Claude Elwood Shannon in 1948, and continuing up to its latest developments in multi-user communication networks, information theory describes in the language of mathematics the limits for the communication of information. These limits are operational, of real engineering significance, and independent of the semantic aspects associated with the communication process.
Entropy as Inference and Entropy as a Physical Field: A Comprehensive Comparison of Ariel Caticha’s Entropic Dynamics and Obidi’s Theory of Entropicity (ToE) Introduction Putting Ariel Caticha’s work on entropic dynamics (ED) side by side with Obidi’s Theory of Entropicity (ToE) is like comparing two very different bets on what “entropy” is allowed to be in physics. Both take entropy seriously, both see it as more than a bookkeeping device, and both use information‑theoretic ideas. But they diverge at the deepest level: Caticha treats entropy as an epistemic principle guiding inference about an underlying reality, while Obidi treats entropy as the ontological field that constitutes reality itself. That difference—epistemic versus ontological—is not cosmetic. It changes what counts as a law of nature, what time is, what causality is, and what it even means for something to exist. Caticha: Entropy as a principle of inference and dynamics Ariel Caticha’s program, broadly called entropic dynamics, starts from the idea that physical laws can be derived from principles of inference, especially the method of maximum entropy. The core move is: given incomplete information about a system, the best we can do is update probability distributions in a way that is consistent with known constraints and maximizes entropy subject to those constraints. From this perspective, dynamics—how systems evolve in time—can be seen as a sequence of entropic updates. Caticha shows that, under suitable assumptions, one can recover forms of classical and quantum dynamics from entropic inference. Time, in this view, is an ordering parameter for updates; it is not fundamental but emerges as a way of indexing successive states of knowledge. The underlying ontology is relatively modest: there is some “stuff” (positions, configurations, etc.), and we have incomplete information about it. Entropy quantifies that ignorance, and the laws of motion are rules for updating probabilities. So in Caticha’s world, entropy is: a measure of uncertainty, a tool for inference, a generator of effective dynamics. It is powerful, but it is not the substance of reality. It is about how we reason about reality. Obidi: Entropy as the Fundamental Field of Reality Obidi’s Theory of Entropicity (ToE) takes a much more audacious step. It does not merely use entropy to derive dynamics; it declares entropy to be the primitive physical field from which everything else emerges. Entropy is not a measure of ignorance; it is the structure that makes knowledge, states, and even ignorance possible. In ToE, the universe does not begin with spacetime, particles, or even probability distributions. It begins with an entropic field whose configurations encode distinguishability, structure, and the capacity for events to occur. Space and time are emergent bookkeeping devices that track how this entropic field differentiates itself. Causality is not geometric but entropic: events become causally related when the entropic field has evolved enough to separate and stabilize them. Measurement is not a mysterious collapse but an entropic stabilization of previously indistinguishable alternatives. Matter is "frozen" entropy; mass is stabilized internal entropic content; [absolute/inherent] identity is what survives when entropy can no longer erase distinctions [in a given system or element]. Where Caticha uses entropy to derive dynamics on an underlying configuration space, Obidi uses entropy to generate the configuration space itself, along with its geometry, its dynamics, and its ontology. Comparison of Ariel Caticha’s Entropic Dynamics and Obidi’s Theory of Entropicity: Conceptual, Philosophical, and Structural Differences A comparison between Ariel Caticha’s entropic dynamics and Obidi’s Theory of Entropicity (ToE) reveals two fundamentally different visions of what entropy is and what role it plays in physics. Caticha’s work is rooted in the epistemic tradition: entropy is a measure of uncertainty, and physical laws emerge from principles of inference. Obidi’s work is rooted in an ontological re‑founding of physics: entropy is the fundamental physical field, and the universe emerges from its evolution. Caticha’s entropic dynamics begins with the assumption that we have incomplete information about the positions or configurations of a system. Using the method of maximum entropy, he derives rules for updating probability distributions in a way that is consistent with known constraints. Under certain assumptions, these updates reproduce forms of classical and quantum dynamics. Time, in this framework, is an ordering parameter for successive entropic inferences. The underlying ontology remains modest: there is a configuration space, there are unknown variables, and entropy quantifies our uncertainty about them. Caticha’s program is therefore a powerful epistemic reconstruction of dynamics, but it does not claim that entropy is a physical field or that it generates spacetime, causality, or matter. Obidi’s Theory of Entropicity takes a radically different approach. Entropy is not a measure of ignorance but the primitive ontological field. It is the substrate from which distinguishability, time, causality, measurement, matter, and geometry emerge. The entropic and coherent sectors are not epistemic constructs but structural phases of the entropic field. The entropic sector is the domain of irreversible becoming, where the entropic field differentiates itself and writes the arrow of time. The coherent sector is the domain of stabilized being, where structures persist and interact. This two‑sector architecture is not present in Caticha’s work and has no analogue in entropic dynamics. Furthermore, Caticha does not develop a philosophical interpretation of his framework. His papers are technical, focused on inference, probability updating, and the derivation of dynamical equations. He does not address the ontological status of entropy, the metaphysics of becoming, the nature of measurement, or the philosophical implications of entropic evolution. Obidi, by contrast, explicitly engages with the philosophical foundations of physics, drawing connections to ontology, metaphysics, epistemology, and the philosophy of time. His work situates the Theory of Entropicity (ToE) within a broader intellectual tradition, including comparisons with Paul Tillich, Schopenhauer, and the classical distinction between appearance and reality. The originality of Obidi’s contribution lies in the systematic way he reorders the explanatory hierarchy of physics. Instead of deriving dynamics from inference, he derives inference, dynamics, geometry, and matter from the entropic field alone. Instead of treating probability as epistemic, he derives it as a conserved physical quantity. Instead of treating measurement as a postulate, he explains it as entropic stabilization. Instead of treating spacetime as fundamental, he derives it from information geometry via the Obidi Action. Instead of treating coherence as a mathematical artifact, he interprets and grounds it as a structural phase of the entropic field. The broader implications of this difference are profound. Caticha’s program suggests that physics is ultimately about how rational agents should update their beliefs. Obidi’s program suggests that physics is about how the universe entropically becomes what it is [and that ultimately it is not about beliefs taken as isolated givens or provisos independent of the Entropic Field (EF)]. Caticha reconstructs known laws; Obidi reconstructs the foundations from which laws arise. Caticha’s entropy is epistemic; Obidi’s entropy is ontological. Caticha’s dynamics is inferential; Obidi’s dynamics is generative. Caticha’s framework is silent on metaphysics; Obidi’s framework is a metaphysics inseparable from physics. References (Caticha): Caticha, A. Entropic Dynamics, in Bayesian Inference and Maximum Entropy Methods in Science and Engineering, AIP Conference Proceedings, 2011. Caticha, A. Entropic Inference and the Foundations of Physics, 2012. Caticha, A. From Entropic Dynamics to Quantum Theory, 2014. References (Obidi): Obidi, J. O. Theory of Entropicity (ToE) Canonical Archives, 2024–2026. Obidi, J. O. Obidi’s Philosophical Analysis of Being and Becoming, 2026. Obidi, J. O. The Obidi Action, Entropic Geometry, and the Emergence of Spacetime, 2025. Obidi, J. O. The Obidi Probability Law and the Two‑Sector Hilbert Space Decomposition, 2025. Entropic and Coherent Sectors: Caticha versus Obidi The language of “entropic” and “coherent” sectors is especially revealing. In Caticha’s framework, the entropic sector is essentially the inferential machinery: probability distributions, entropy functionals, constraints, and the maximum entropy principle. Coherence, when it appears, is largely about consistency of inference and the emergence of quantum‑like structures (e.g., complex amplitudes, Hilbert space) from entropic updating. The “coherent” behavior is a feature of how probability amplitudes evolve under entropic rules, not a separate ontological sector of reality. In Obidi’s ToE, the entropic and coherent sectors are structurally deeper. The entropic sector is the domain where the entropic field is actively differentiating, where distinguishability is being created, where time’s arrow is being written. The coherent sector is the regime where structures—once formed by entropic evolution—exhibit stability, persistence, and internal consistency. Coherence here is not just a property of wavefunctions; it is a structural phase of the entropic field itself. You can think of it this way: in ToE, the entropic sector is the “becoming” of reality, while the coherent sector is the “being” that results from that becoming. The two are not separate theories but two regimes of the same entropic substrate. The entropic sector governs how new distinctions, structures, and events come into existence; the coherent secto
ENGLISH ABSTRACT: What are the information processing capabilities of physical systems? As recently as the first half of the 20th century this question did not even have a definite meaning. What is information, and how would one process it? It took the development of theories of computing (in the 1930s) and information (late in the 1940s) for us to formulate mathematically what it means to compute or communicate. Yet these theories were abstract, based on axiomatic mathematics: what did physical systems have to do with these axioms? Rolf Landauer had the essential insight - "Information is physical" - that information is always encoded in the state of a physical system, whose dynamics on a microscopic level are well-described by quantum physics. This means that we cannot discuss information without discussing how it is represented, and how nature dictates it should behave. Wigner considered the situation from another perspective when he wrote about "the unreasonable effectiveness of mathematics in the natural sciences". Why are the computational techniques of mathematics so astonishingly useful in describing the physical world [1]? One might begin to suspect foul play in the universe's operating principles. Interesting insights into the physics of information accumulated through the 1970s and 1980s - most sensationally in the proposal for a "quantum computer". If we were to mark a particular year in which an explosion of interest took place in information physics, that year would have to be 1994, when Shor showed that a problem of practical interest (factorisation of integers) could be solved easily on a quantum computer. But the applications of information in physics - and vice versa - have been far more widespread than this popular discovery. These applications range from improved experimental technology, more sophisticated measurement techniques, methods for characterising the quantum/classical boundary, tools for quantum chaos, and deeper insight into quantum theory and nature. In this thesis I present a short review of ideas in quantum information theory. The first chapter contains introductory material, sketching the central ideas of probability and information theory. Quantum mechanics is presented at the level of advanced undergraduate knowledge, together with some useful tools for quantum mechanics of open systems. In the second chapter I outline how classical information is represented in quantum systems and what this means for agents trying to extract information from these systems. The final chapter presents a new resource: quantum information. This resource has some bewildering applications which have been discovered in the last ten years, and continually presents us with unexpected insights into quantum theory and the universe.