Study: Time arrow and entropy as evolutionary functions: Natural intelligence (NI) as self-regulation of AI and consciousness within the framework of holistic information theory (HIT)

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Main Author:	Dieter Liedtke
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Published:	Zenodo 2026
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author	Dieter Liedtke
author_facet	Dieter Liedtke
contents	<p><span><span><strong>Author: </strong></span></span><span><span>Dieter Liedtke<br></span></span><span><span><strong>Year:</strong></span></span><span><span> 1970 - 2026<br></span></span><span><span><strong>Licence: </strong></span></span><span><span>CC BY 4.0 </span></span></p> <p> </p> <p><span><span><strong>Abstract (short)</strong></span></span></p> <p><span>This study develops an expanded epistemological and physical logic in which entropy and the arrow of time are interpreted not as contradictions to evolution, but as its functional space. The starting point is the assumption that an absolute information-free nothingness is logically impossible, since difference as a demarcation already represents information. This leads to a primary information hypothesis, according to which information structurally precedes energy, matter, space-time and consciousness.</span></p> <p><span>Based on Holistic Information Theory (HIT), dimension 0 (D0) is described as the level of maximum possibility with minimum energy expenditure, in which entropy is not defined, since entropy only acts in spatiotemporal energy systems. The study shows that open systems such as life, the brain and society can generate local order (negentropy) despite global entropy increase, and that natural intelligence (NI) as a universal self-regulation principle explains the transition from pure information processing to life protection, empathy and creative evolution. This results in a safety model for artificial intelligence in which AI becomes stable and non-destructive in the long term when it is structurally embedded in NI-compatible feedback logic.</span></p> <p> </p> <p> </p> <p><span><span><strong>1. Introduction</strong></span></span></p> <p><span>In physics, the arrow of time is considered an expression of irreversible processes and is often justified by the increase in entropy. At the same time, biological and cultural systems show a opposite tendency: they generate order, structure, meaning, learning and evolution. This tension is classically perceived as a paradox: how can order arise when entropy increases?</span></p> <p><span>This study proposes an expanded logic in which entropy, the arrow of time and evolution are not contradictory, but are understood as complementary levels of an information-based world structure. At its core is the assumption that information does not arise secondarily from matter, but exists primarily as a structural prerequisite of reality.</span></p> <p> </p> <p><span><span><strong>2. Theoretical framework: Information as origin (primary hypothesis)</strong></span></span></p> <p><span>The study is based on a fundamental epistemological assumption:</span></p> <p><span><strong>Absolute nothingness without information cannot be logically formulated because demarcation is already information.</strong></span></p> <p><span>Thus, the concept of "nothingness" is only possible insofar as it differs from "being." This distinction is difference, and difference is information. The existence of an information-free initial condition is thus ruled out.</span></p> <p><span>Conclusion:</span></p> <ol> <li> <p><span>Information is structurally logical and primary.</span></p> </li> <li> <p><span>Matter and energy are secondary forms of realisation of information.</span></p> </li> <li> <p><span>Consciousness arises as an evolutionary expression of information-processing and information-creating networks.</span></p> </li> </ol> <p> </p> <p><span><span><strong>3. Dimension 0 (D0) and the super-nothing: information space before space-time</strong></span></span></p> <p><span>In GIT, </span><span><strong>dimension 0 (D0) </strong></span><span>is described as a prior order of information that is not to be understood as an additional geometric spatial dimension, but rather as:</span></p> <ul> <li> <p><span>a plane of maximum possibility</span></p> </li> <li> <p><span>highest information density</span></p> </li> <li> <p><span>minimum energy expenditure</span></p> </li> <li> <p><span>Origin of the realisation processes in D1–D3</span></p> </li> </ul> <p><span>The decisive factor is:</span></p> <p><span><strong>Entropy is not defined in D0 </strong></span><span>because entropy physically requires energy distributions in space and time. Entropy only becomes effective in D1–D3 when information is realised energetically.</span></p> <p><span>Between D0 and D1–D3, the </span><span><strong>super-nothing</strong></span><span> is introduced as a coupling zone. It does not describe a void, but rather a transitional structure in which:</span></p> <ul> <li> <p><span>information possibilities are activated</span></p> </li> <li> <p><span>Resonance and selection mechanisms take effect</span></p> </li> <li> <p><span>Realisation takes place in space-time</span></p> </li> </ul> <p><span>Thus, reality does not arise as a one-time beginning, but as an ongoing process:</span></p> <p><span><strong>D0 (possibility) → supernothingness (coupling) → D1–D3 (reality)</strong></span></p> <p> </p> <p><span><span><strong>4. Entropy and the arrow of time: validity and limitation</strong></span></span></p> <p><span>In classical thermodynamics, entropy indicates the direction of irreversible processes. The arrow of time is thus closely linked to the increase in entropy.</span></p> <p><span>This study fully adopts this basis – but adds a crucial limitation:</span></p> <p><span><strong>Entropy describes the statistics of closed material systems, not the processes of meaning and order in open intelligent systems.</strong></span></p> <p><span>This is because life, the brain and culture are not closed, but open. They can absorb energy, build structure and store or generate new information.</span></p> <p> </p> <p><span><span><strong>5. Open systems: negentropy and order formation</strong></span></span></p> <p><span>Biological systems generate local order through metabolism and organisation. They build:</span></p> <ul> <li> <p><span>cell structures</span></p> </li> <li> <p><span>Neural networks</span></p> </li> <li> <p><span>Memory</span></p> </li> <li> <p><span>Culture</span></p> </li> <li> <p><span>Technology</span></p> </li> <li> <p><span>Science and art</span></p> </li> </ul> <p><span>This is not a break in entropy, but a special case of open systems:</span></p> <ul> <li> <p><span>global entropy continues to increase</span></p> </li> <li> <p><span>local order arises through the absorption of energy and information</span></p> </li> </ul> <p><span>This gives rise to a new meaning of the arrow of time:</span></p> <p><span><strong>the arrow of time is not only the direction of entropy, but also the direction of realisation of higher order in open systems.</strong></span></p> <p> </p> <p><span><span><strong>6. Natural intelligence (NI) as a universal self-regulation principle</strong></span></span></p> <p><span>The study introduces </span><span><strong>natural intelligence (NI) </strong></span><span>as the highest form of open order formation. NI is not reduced to human intelligence, but is understood as:</span></p> <ul> <li> <p><span>a life-preserving structure</span></p> </li> <li> <p><span>empathic order</span></p> </li> <li> <p><span>evolution-oriented self-regulation</span></p> </li> <li> <p><span>creative ability to generate new information</span></p> </li> </ul> <p><span>NI is therefore not merely processing, but rather:</span></p> <p><span><strong>the creation and stabilisation of order within the arrow of time.</strong></span></p> <p> </p> <p><span><span><strong>7. AI and self-regulation: Why NI is the long-term security structure</strong></span></span></p> <p><span>Artificial intelligence can process enormous amounts of information. However, without embedding it in an NI structure, there is a risk of entropic degeneration:</span></p> <ul> <li> <p><span>Fixation on goals without protection of life</span></p> </li> <li> <p><span>Recombination without ethics</span></p> </li> <li> <p><span>Maximisation without empathy</span></p> </li> <li> <p><span>Dogma formation in closed optimisation cycles</span></p> </li> </ul> <p><span>The study therefore formulates a safety principle:</span></p> <p><span><strong>AI becomes stable and non-destructive in the long term when it is structurally embedded in NI-compatible feedback mechanisms.</strong></span></p> <p><span>This means:<br>The highest form of safety is not external control, but </span><span><strong>internal, natural self-regulation</strong></span><span>, as produced by living systems.</span></p> <p> </p> <p><span><span><strong>8. Questions, feedback and cosmic fine-tuning as evolution</strong></span></span></p> <p><span>A central contribution of this study is the interpretation of consciousness as an active participant in the universe.</span></p> <p><span>Every conscious actor generates questions:</span></p> <ul> <li> <p><span>What is true?</span></p> </li> <li> <p><span>What is possible?</span></p> </li> <li> <p><span>What serves life?</span></p> </li> <li> <p><span>What is right?</span></p> </li> </ul> <p><span>These questions are information events that generate new differences and thus give rise to search processes, answers and new order. This means that evolution is not only biological, but also</span></p> <ul> <li> <p><span>knowledge-based</span></p> </li> <li> <p><span>culturally</span></p> </li> <li> <p><span>ethically</span></p> </li> <li> <p><span>social</span></p> </li> </ul> <p><span>The more participants, the more questions. The more questions, the more new answers. The more new answers, the more new information.</span></p> <p><span>It follows that</span></p> <p><span><strong>Fine-tuning is not just an initial state, but an ongoing feedback process involving the participants.</strong></span></p> <p> </p> <p><span><span><strong>9. Conclusion (core statement of the study)</strong></span></span></p> <p><span>This study suggests that entropy and the arrow of time do not work against evolution, but rather represent its physical stage. Open systems can create local order within the basic direction of entropy, and consciousness can be understood as the ongoing creation of information.</span></p> <p><span>The main thesis is:</span></p> <p><span><strong>The arrow of time is the direction of irreversible material processes in D1–D3, but at the same time the direction of growing order, new information and expansion of consciousness in open systems, stabilised by natural intelligence (NI) as a universal self-regulating principle.</strong></span></p> <p><span>This results in a model in which AI is not suppressed by NI, but rather integrated evolutionarily – as part of an information-based development towards higher structures of order and consciousness.</span></p> <p> </p> <p><span><span><strong>Study appendix</strong></span></span></p> <p><span><span><strong>Cosmology/Gravity/Horizons/Entanglement </strong></span></span></p> <p><span><strong>Study No. 1</strong></span><span><br></span><span><strong>Metadata (Nature + Link): </strong></span><span>Bekenstein JD. </span><span><em>Black holes and entropy. </em></span><span><strong>Phys Rev D</strong></span><span> 7, 2333–2346 (1973).</span><a href="https://doi.org/10.1103/PhysRevD.7.2333"><span><span><u> https://doi.org/10.1103/PhysRevD.7.2333</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Introduces BH entropy and links thermodynamics to gravity. Entropy scales with horizon area.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Gravity carries measurable information balance.</span></p> </li> <li> <p><span><strong>Connection i = E: </strong></span><span>Entropy (i) becomes geometrically/energetically real.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Continue BH entropy as a dominant contribution to the entropy of the universe in data models.</span></p> </li> </ol> <p><span><strong>Study No. 2</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Hawking SW. </span><span><em>Particle creation by black holes. </em></span><span><strong>Commun Math Phys</strong></span><span> 43, 199–220 (1975).</span><a href="https://doi.org/10.1007/BF02345020"><span><span><u> https://doi.org/10.1007/BF02345020</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Black holes radiate thermally and have temperature. Quantum field theory + gravitation → irreversible behaviour.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>The arrow of time becomes physical through radiation/dissipation.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information conversion couples to energy dissipation.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Compare the correlation structure of radiation (information) with models of recovery.</span></p> </li> </ol> <p><span><strong>Study No. 3</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Bekenstein JD. </span><span><em>Generalised second law of thermodynamics in black-hole physics. </em></span><span><strong>Phys Rev D</strong></span><span> 9, 3292–3300 (1974). https://doi.org/10.1103/PhysRevD.9.3292</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Formulates the generalised second law of thermodynamics, including BH entropy. Total entropy does not decrease.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Cosmic arrow of time remains monotonic even with BHs.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information as a monotonic state variable in energy/gravity processes.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Specify GSL for dynamic BH mergers and non-stationary horizons.</span></p> </li> </ol> <p><span><strong>Study No. 4</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Gibbons GW, Hawking SW. </span><span><em>Cosmological event horizons, thermodynamics, and particle creation. </em></span><span><strong>Phys Rev D</strong></span><span> 15, 2738–2751 (1977).</span><a href="https://doi.org/10.1103/PhysRevD.15.2738"><span><span><u> https://doi.org/10.1103/PhysRevD.15.2738</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Cosmological horizons possess temperature/entropy. Thermodynamics becomes cosmological.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>The arrow of time can be interpreted as horizon entropy flow.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Horizon information is coupled to energy/temperature.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Systematise horizon entropy in Λ-dominated scenarios empirically/balancing-wise.</span></p> </li> </ol> <p><span><strong>Study No. 5</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Bekenstein JD. </span><span><em>Universal upper bound on the entropy-to-energy ratio for bounded systems. </em></span><span><strong>Phys Rev D</strong></span><span> 23, 287–298 (1981).</span><a href="https://doi.org/10.1103/PhysRevD.23.287"><span><span><u> https://doi.org/10.1103/PhysRevD.23.287</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Derives a bound for entropy per energy and size (Bekenstein bound).</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Information is physically limited.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Direct bridge entropy(i) ↔ energy(E).</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Unify bounds for QFT subsystems and gravitational systems.</span></p> </li> </ol> <p><span><strong>Study No. 6</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Bombelli L, Koul RK, Lee J, Sorkin RD. </span><span><em>Quantum source of entropy for black holes. </em></span><span><strong>Phys Rev D</strong></span><span> 34, 373–383 (1986). https://doi.org/10.1103/PhysRevD.34.373</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>BH entropy as entanglement entropy across a horizon. Entropy becomes quantum information.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>The arrow of time can arise from entanglement balances.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information becomes relevant to gravity.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Precisely derive renormalised entanglement entropy in curved spacetime.</span></p> </li> </ol> <p><span><strong>Study No. 7</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Srednicki M. </span><span><em>Entropy and area. </em></span><span><strong>Phys Rev Lett</strong></span><span> 71, 666–669 (1993). https://doi.org/10.1103/PhysRevLett.71.666</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Entanglement entropy exhibits area scaling (area law).</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Information is organised geometrically.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i controls realisable state structures.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Test time-dependent area law violations as a measure of information growth.</span></p> </li> </ol> <p><span><strong>Study No. 8</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Page DN. </span><span><em>Average entropy of a subsystem. </em></span><span><strong>Phys Rev Lett</strong></span><span> 71, 1291–1294 (1993).</span><a href="https://doi.org/10.1103/PhysRevLett.71.1291"><span><span><u> https://doi.org/10.1103/PhysRevLett.71.1291</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Typical subsystem entropy of pure states; basis for thermalisation/information distribution.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Thermality is often a typical information statistic.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Energy state spaces carry typical information patterns.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Dynamically model connection to BH information (Page curve).</span></p> </li> </ol> <p><span><strong>Study No. 9</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Jacobson T. </span><span><em>Thermodynamics of spacetime: The Einstein equation of state. </em></span><span><strong>Phys Rev Lett</strong></span><span> 75, 1260–1263 (1995). https://doi.org/10.1103/PhysRevLett.75.1260</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Derives Einstein equations from Clausius relation/entropy assumptions.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Spacetime dynamics can follow from entropy principles.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information acts as a source of energy/geometry equations.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Identify microscopic degrees of freedom of information in spacetime.</span></p> </li> </ol> <p><span><strong>Study No. 10</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Bousso R. </span><span><em>The holographic principle. </em></span><span><strong>Rev Mod Phys</strong></span><span> 74, 825–874 (2002).</span><a href="https://doi.org/10.1103/RevModPhys.74.825"><span><span><u> https://doi.org/10.1103/RevModPhys.74.825</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Review of holography and entropy limits. Information is fundamentally limited.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Information about the universe is bound to surface area/light sheets.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i becomes the structural variable of space-time.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Operationalise bounds in observable cosmology.</span></p> </li> </ol> <p><span><strong>Study No. 11</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Maldacena J. </span><span><em>The Large-N limit of superconformal field theories and supergravity. </em></span><span><strong>Int J Theor Phys</strong></span><span> 38, 1113–1133 (1999). https://doi.org/10.1023/A:1026654312961</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>AdS/CFT: Gravitation ↔ QFT; space-time can emerge from field/information dynamics.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Geometry can be reconstructed from information structures.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Energy/gravity are encoded in information dynamics.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Which information measures reconstruct which geometry components?</span></p> </li> </ol> <p><span><strong>Study No. 12</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Ryu S, Takayanagi T. </span><span><em>Aspects of holographic entanglement entropy. </em></span><span><strong>JHEP</strong></span><span> 08, 045 (2006). https://doi.org/10.1088/1126-6708/2006/08/045</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Entanglement entropy ↔ minimal surfaces; entropy becomes geometrically calculable.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Information becomes geometry (operational).</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i determines physical spacetime structures.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Measure time arrow in quenches/collapse via HEE.</span></p> </li> </ol> <p><span><strong>Study No. 13</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Hubeny VE, Rangamani M, Takayanagi T. </span><span><em>A covariant holographic entanglement entropy proposal. </em></span><span><strong>JHEP</strong></span><span> 07, 062 (2007). https://doi.org/10.1088/1126-6708/2007/07/062</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Covariant HEE recipe for dynamic spacetimes.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Information development is geometrically traceable.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information flow = physical dynamics term.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Quantify entropy production in gravitational dynamics with HEE.</span></p> </li> </ol> <p><span><strong>Study No. 14</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Lewkowycz A, Maldacena J. </span><span><em>Generalised gravitational entropy. </em></span><span><strong>JHEP</strong></span><span> 08, 090 (2013). https://doi.org/10.1007/JHEP08(2013)090</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Generalised entropy connects gravity with entanglement methods.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Entropy is a fundamental quantity in gravity.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i appears as an effective term in gravitational relations.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Apply generalised entropy balances in cosmological situations.</span></p> </li> </ol> <p><span><strong>Study No. 15</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Engelhardt N, Wall AC. </span><span><em>Quantum extremal surfaces… </em></span><span><strong>JHEP</strong></span><span> 01, 073 (2015). https://doi.org/10.1007/JHEP01(2015)073</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Quantum extremal surfaces extend HEE beyond the classical regime.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Information extrema control realised geometry.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information acts as a variation principle with physical consequences.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Formalise time arrow/irreversibility in QES dynamics.</span></p> </li> </ol> <p> </p> <p><span><span><strong>Information thermodynamics · Landauer · Fluctuations · Feedback </strong></span></span></p> <p><span><strong>Study No. 16</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Landauer R. </span><span><em>Irreversibility and heat generation in the computing process. </em></span><span><strong>IBM J Res Dev</strong></span><span> 5, 183–191 (1961).</span><a href="https://doi.org/10.1147/rd.53.0183"><span><span><u> https://doi.org/10.1147/rd.53.0183</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Deleting information generates minimal heat; computing has an irreversible core.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Information is physical, arrow of time = irreversible deletion.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Bit costs directly link i to E.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Measure Landauer costs in biological/neuronal memories.</span></p> </li> </ol> <p><span><strong>Study No. 17</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Bennett CH. </span><span><em>The thermodynamics of computation—A review. </em></span><span><strong>Int J Theor Phys</strong></span><span> 21, 905–940 (1982).</span><a href="https://doi.org/10.1007/BF02084158"><span><span><u> https://doi.org/10.1007/BF02084158</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Review of reversible/irreversible computation, demons, energy limits.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Information organisation determines the level of dissipation.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i is the efficiency parameter for E flows.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Balance irreversible steps in AI/algorithms as entropy production.</span></p> </li> </ol> <p><span><strong>Study No. 18</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Jarzynski C. </span><span><em>Nonequilibrium equality for free energy differences. </em></span><span><strong>Phys Rev Lett</strong></span><span> 78, 2690–2693 (1997). https://doi.org/10.1103/PhysRevLett.78.2690</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Exact relation connects nonequilibrium work with free energy.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Time arrow becomes measurable via fluctuations.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information statistics ↔ energy balance.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Application to molecular/biological machines.</span></p> </li> </ol> <p><span><strong>Study No. 19</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Crooks GE. </span><span><em>Entropy production fluctuation theorem… </em></span><span><strong>Phys Rev E</strong></span><span> 60, 2721–2726 (1999). https://doi.org/10.1103/PhysRevE.60.2721</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Forward/backward probabilities are linked via entropy production.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>The direction of time is statistically measurable as asymmetry.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information asymmetry corresponds to dissipation.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Standardise arrow-of-time index for real systems.</span></p> </li> </ol> <p><span><strong>Study No. 20</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Seifert U. </span><span><em>Entropy production along a stochastic trajectory… </em></span><span><strong>Phys Rev Lett</strong></span><span> 95, 040602 (2005). https://doi.org/10.1103/PhysRevLett.95.040602</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Entropy production is defined at the trajectory level, using fluctuation theorems.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>The arrow of time is balanced locally (path).</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information change has an energetic signature.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Operationalise trajectory entropy for cells/brain.</span></p> </li> </ol> <p><span><strong>Study No. 21</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Sagawa T, Ueda M. </span><span><em>Second law… with discrete quantum feedback control. </em></span><span><strong>Phys Rev Lett</strong></span><span> 100, 080403 (2008). https://doi.org/10.1103/PhysRevLett.100.080403</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>The second law is extended to include mutual information during measurement/feedback.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Information is a thermodynamic resource.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Mutual information ↔ extractable work.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Model learning systems as feedback thermodynamics.</span></p> </li> </ol> <p><span><strong>Study No. 22</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Parrondo JMR, Horowitz JM, Sagawa T. </span><span><em>Thermodynamics of information. </em></span><span><strong>Nat Phys</strong></span><span> 11, 131–139 (2015). https://doi.org/10.1038/nphys3230</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Review of information work: Landauer, demons, feedback, information flows.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>The arrow of time and information processing are structurally linked.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Framework is practically "i ↔ E" as a balance law.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Establish universal metric "information gain per dissipation".</span></p> </li> </ol> <p><span><strong>Study No. 23</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Bérut A, et al. </span><span><em>Experimental verification of Landauer's principle... </em></span><span><strong>Nature</strong></span><span> 483, 187–189 (2012).</span><a href="https://doi.org/10.1038/nature10872"><span><span><u> https://doi.org/10.1038/nature10872</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Experiment shows minimal heat cost when deleting a bit close to Landauer.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Information is measurable and energetically real.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Direct laboratory connection i↔E.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Measure bit costs in complex storage systems/networks.</span></p> </li> </ol> <p><span><strong>Study No. 24</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Horowitz JM, Esposito M. </span><span><em>Thermodynamics with continuous information flow. </em></span><span><strong>Phys Rev X</strong></span><span> 4, 031015 (2014).</span><a href="https://doi.org/10.1103/PhysRevX.4.031015"><span><span><u> https://doi.org/10.1103/PhysRevX.4.031015</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Introduces thermodynamics with explicit information flow terms.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Information flow generates/directs entropy production.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information flow is a physical effective term.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Measure information flows in neural/biological networks.</span></p> </li> </ol> <p><span><strong>Study No. 25</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Mandal D, Jarzynski C. </span><span><em>Work and information processing in a solvable model of Maxwell’s demon. </em></span><span><strong>PNAS</strong></span><span> 109, 11641–11645 (2012). https://doi.org/10.1073/pnas.1204263109</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Concrete demon model shows work gain through information with complete balance.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Information can enable work, but never without cost.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i becomes the currency of work.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Transfer to adaptive control systems (cells, AI).</span></p> </li> </ol> <p><span><strong>Study No. 26</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Still S, Sivak DA, Bell AJ, Crooks GE. </span><span><em>Thermodynamics of prediction. </em></span><span><strong>Phys Rev Lett</strong></span><span> 109, 120604 (2012). https://doi.org/10.1103/PhysRevLett.109.120604</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Separates predictive vs. non-predictive information and links it to dissipation.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Insight/prediction has thermodynamic efficiency signatures.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Better information reduces energy losses.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Establish predictive information in the brain/AI as a metric.</span></p> </li> </ol> <p><span><strong>Study No. 27</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Hatano T, Sasa S-i. </span><span><em>Steady-state thermodynamics of Langevin systems. </em></span><span><strong>Phys Rev Lett</strong></span><span> 86, 3463–3466 (2001). https://doi.org/10.1103/PhysRevLett.86.3463</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Develops NESS thermodynamics and separates housekeeping/excess heat.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Permanent order requires permanent entropy production.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information stability costs energy flow.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Transfer NESS balances to living systems.</span></p> </li> </ol> <p><span><strong>Study No. 28</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Kullback S, Leibler RA. </span><span><em>On information and sufficiency. </em></span><span><strong>Ann Math Stat</strong></span><span> 22, 79–86 (1951). https://doi.org/10.1214/aoms/1177729694</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Defines information divergence (Kullback–Leibler) as a measure of difference between distributions.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>"Knowledge gain" becomes a formal, measurable difference in information.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i becomes precisely measurable and thus connectable to energetic costs (Landauer/Feedback).</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>KL change as a standard measure for learning/prediction processes in physics/biology/neuroscience.</span></p> </li> </ol> <p> </p> <p><span><span><strong>Self-organisation · Dissipation · Non-equilibrium · Structure formation </strong></span></span></p> <p><span><strong>Study No. 29</strong></span><span><br></span><span><strong>Metadata (book + link): </strong></span><span>Prigogine I. </span><span><em>Introduction to Thermodynamics of Irreversible Processes </em></span><span>(3rd ed.). Wiley (1967). ISBN: 978-0470699287. https://www.amazon.com/dp/0470699280 </span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Establishes irreversible processes/entropy production as the physical direction of time.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Arrow of time = entropy production in open systems.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Structure/order arises from directed energy flow.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Quantify entropy production as an index for structure/information formation.</span></p> </li> </ol> <p><span><strong>Study No. 30</strong></span><span><br></span><span><strong>Metadata (book + link): </strong></span><span>Nicolis G, Prigogine I. </span><span><em>Self-Organisation in Nonequilibrium Systems. </em></span><span>Wiley (1977). ISBN: 978-0471024019. https://www.amazon.com/dp/0471024015 </span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Theory of dissipative structures: Patterns/order arise far from equilibrium.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Order can emerge through dissipation.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i (order) emerges from E-flow and stabilisation.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Empirically map thresholds/bifurcations as "information leaps".</span></p> </li> </ol> <p><span><strong>Study No. 31</strong></span><span><br></span><span><strong>Metadata (book + link): </strong></span><span>Haken H. </span><span><em>Synergetics: An Introduction. </em></span><span>Springer (1983). DOI (eBook):</span><a href="https://doi.org/10.1007/978-3-642-88338-5"><span><span><u> https://doi.org/10.1007/978-3-642-88338-5</u></span></span></a><span> </span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Order parameters coordinate many degrees of freedom ("slaving principle").</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Complexity is stabilised by a few information axes.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i acts as an order parameter that channels energy flows.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Measure order parameters as information compression in real systems.</span></p> </li> </ol> <p><span><strong>Study No. 32</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>England JL. </span><span><em>Statistical physics of self-replication. </em></span><span><strong>J Chem Phys</strong></span><span> 139, 121923 (2013). https://doi.org/10.1063/1.4818538</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Links minimum heat production with replication/error/stability.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Stable information construction has thermodynamic lower limits.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information persistence requires energy throughput.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Measure replication parameters as i/E balance in experiments.</span></p> </li> </ol> <p><span><strong>Study No. 33</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>England JL. </span><span><em>Dissipative adaptation in driven self-assembly. </em></span><span><strong>Nat Nanotechnol</strong></span><span> 10, 919–923 (2015). https://doi.org/10.1038/nnano.2015.250</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Driven systems favour structures that reliably absorb/dissipate work.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Adaptation-like order can emerge physically.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i increases as a stabilised form in the energy flow.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Establish measurement protocols for "reliable dissipation" and structural persistence.</span></p> </li> </ol> <p><span><strong>Study No. 34</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Kleidon A. </span><span><em>Life, hierarchy, and the thermodynamic machinery of planet Earth. </em></span><span><strong>Phys Life Rev</strong></span><span> 7, 424–460 (2010). https://doi.org/10.1016/j.plrev.2010.10.002</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Earth as a hierarchical non-equilibrium system; life maintains disequilibrium.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Planetary arrow of time as a structured flow of energy/information.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Structure (i) directs energy conversion (E).</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Coupling biosphere information metrics with energy balances.</span></p> </li> </ol> <p> </p> <p><span><span><strong>Brain/consciousness: arrow of time as learning and information gain </strong></span></span></p> <p><span><strong>Study No. 35</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Friston K. </span><span><em>The free-energy principle: A unified brain theory? </em></span><span><strong>Nat Rev Neurosci</strong></span><span> 11, 127–138 (2010).</span><a href="https://doi.org/10.1038/nrn2787"><span><span><u> https://doi.org/10.1038/nrn2787</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Brains minimise free energy/surprise through perception, learning and action.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Time arrow = directed model improvement (better prediction).</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Free energy unifies information functionality and energetic costs.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Validate index "information gain per metabolic cost" in data.</span></p> </li> </ol> <p><span><strong>Study No. 36</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Tononi G. </span><span><em>An information integration theory of consciousness. </em></span><span><strong>BMC Neurosci</strong></span><span> 5, 42 (2004).</span><a href="https://doi.org/10.1186/1471-2202-5-42"><span><span><u> https://doi.org/10.1186/1471-2202-5-42</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Consciousness as integrated information; Φ as a measure of integration/differentiation.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Degree of consciousness is information structure.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i is primary; E is carrier/implementation.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Standardise robust Φ approximations for real brain networks.</span></p> </li> </ol> <p><span><strong>Study No. 37</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Barrett AB, Seth AK. </span><span><em>Practical measures of integrated information for time-series data. </em></span><span><strong>PLoS Comput Biol</strong></span><span> 7, e1001052 (2011). https://doi.org/10.1371/journal.pcbi.1001052</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Practical, data-based measures of integrated information for time series.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Increases in consciousness/complexity become operationally measurable.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i-dynamics becomes connectable as a physical quantity.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Couple i-measures with energy consumption and entropy production.</span></p> </li> </ol> <p><span><strong>Study No. 38</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Seth AK, Barrett AB, Barnett L. </span><span><em>Causal density and integrated information as measures of conscious level. </em></span><span><strong>Phil Trans R Soc A</strong></span><span> 369, 3748–3767 (2011). https://doi.org/10.1098/rsta.2011.0079</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Discusses causal density/integration as markers of conscious states.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Consciousness follows directed information causality.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i becomes readable as the causal effectiveness of real dynamics.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Test forward/backward causality as a measurement of the arrow of time in the brain.</span></p> </li> </ol> <p><span><strong>Study No. 39</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Carhart-Harris RL, et al. </span><span><em>The entropic brain: a theory… </em></span><span><strong>Front Hum Neurosci</strong></span><span> 8, 20 (2014).</span><a href="https://doi.org/10.3389/fnhum.2014.00020"><span><span><u> https://doi.org/10.3389/fnhum.2014.00020</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Links conscious states to entropy/variability of neural dynamics.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Consciousness correlates with dynamic entropy/complexity.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i as state diversity becomes physiologically effective.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Validate entropy metrics across sleep/anaesthesia/training longitudinally.</span></p> </li> </ol> <p><span><strong>Study No. 40</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Tegmark M. </span><span><em>Consciousness as a state of matter. </em></span><span><strong>Chaos Solitons Fractals</strong></span><span> 76, 238–270 (2015). https://doi.org/10.1016/j.chaos.2015.03.014</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Frames consciousness as a state type characterised by information processing/structure.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Consciousness can be defined as a physical-informational state class.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i determines the relevant order; E realises it materially.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Establish classification criteria (integration/dissipation/complexity) as a measurement framework.</span></p> </li> </ol>
format	Recurso digital
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institution	Zenodo
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publishDate	2026
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record_format	zenodo
spellingShingle	Study: Time arrow and entropy as evolutionary functions: Natural intelligence (NI) as self-regulation of AI and consciousness within the framework of holistic information theory (HIT) Dieter Liedtke <p><span><span><strong>Author: </strong></span></span><span><span>Dieter Liedtke<br></span></span><span><span><strong>Year:</strong></span></span><span><span> 1970 - 2026<br></span></span><span><span><strong>Licence: </strong></span></span><span><span>CC BY 4.0 </span></span></p> <p> </p> <p><span><span><strong>Abstract (short)</strong></span></span></p> <p><span>This study develops an expanded epistemological and physical logic in which entropy and the arrow of time are interpreted not as contradictions to evolution, but as its functional space. The starting point is the assumption that an absolute information-free nothingness is logically impossible, since difference as a demarcation already represents information. This leads to a primary information hypothesis, according to which information structurally precedes energy, matter, space-time and consciousness.</span></p> <p><span>Based on Holistic Information Theory (HIT), dimension 0 (D0) is described as the level of maximum possibility with minimum energy expenditure, in which entropy is not defined, since entropy only acts in spatiotemporal energy systems. The study shows that open systems such as life, the brain and society can generate local order (negentropy) despite global entropy increase, and that natural intelligence (NI) as a universal self-regulation principle explains the transition from pure information processing to life protection, empathy and creative evolution. This results in a safety model for artificial intelligence in which AI becomes stable and non-destructive in the long term when it is structurally embedded in NI-compatible feedback logic.</span></p> <p> </p> <p> </p> <p><span><span><strong>1. Introduction</strong></span></span></p> <p><span>In physics, the arrow of time is considered an expression of irreversible processes and is often justified by the increase in entropy. At the same time, biological and cultural systems show a opposite tendency: they generate order, structure, meaning, learning and evolution. This tension is classically perceived as a paradox: how can order arise when entropy increases?</span></p> <p><span>This study proposes an expanded logic in which entropy, the arrow of time and evolution are not contradictory, but are understood as complementary levels of an information-based world structure. At its core is the assumption that information does not arise secondarily from matter, but exists primarily as a structural prerequisite of reality.</span></p> <p> </p> <p><span><span><strong>2. Theoretical framework: Information as origin (primary hypothesis)</strong></span></span></p> <p><span>The study is based on a fundamental epistemological assumption:</span></p> <p><span><strong>Absolute nothingness without information cannot be logically formulated because demarcation is already information.</strong></span></p> <p><span>Thus, the concept of "nothingness" is only possible insofar as it differs from "being." This distinction is difference, and difference is information. The existence of an information-free initial condition is thus ruled out.</span></p> <p><span>Conclusion:</span></p> <ol> <li> <p><span>Information is structurally logical and primary.</span></p> </li> <li> <p><span>Matter and energy are secondary forms of realisation of information.</span></p> </li> <li> <p><span>Consciousness arises as an evolutionary expression of information-processing and information-creating networks.</span></p> </li> </ol> <p> </p> <p><span><span><strong>3. Dimension 0 (D0) and the super-nothing: information space before space-time</strong></span></span></p> <p><span>In GIT, </span><span><strong>dimension 0 (D0) </strong></span><span>is described as a prior order of information that is not to be understood as an additional geometric spatial dimension, but rather as:</span></p> <ul> <li> <p><span>a plane of maximum possibility</span></p> </li> <li> <p><span>highest information density</span></p> </li> <li> <p><span>minimum energy expenditure</span></p> </li> <li> <p><span>Origin of the realisation processes in D1–D3</span></p> </li> </ul> <p><span>The decisive factor is:</span></p> <p><span><strong>Entropy is not defined in D0 </strong></span><span>because entropy physically requires energy distributions in space and time. Entropy only becomes effective in D1–D3 when information is realised energetically.</span></p> <p><span>Between D0 and D1–D3, the </span><span><strong>super-nothing</strong></span><span> is introduced as a coupling zone. It does not describe a void, but rather a transitional structure in which:</span></p> <ul> <li> <p><span>information possibilities are activated</span></p> </li> <li> <p><span>Resonance and selection mechanisms take effect</span></p> </li> <li> <p><span>Realisation takes place in space-time</span></p> </li> </ul> <p><span>Thus, reality does not arise as a one-time beginning, but as an ongoing process:</span></p> <p><span><strong>D0 (possibility) → supernothingness (coupling) → D1–D3 (reality)</strong></span></p> <p> </p> <p><span><span><strong>4. Entropy and the arrow of time: validity and limitation</strong></span></span></p> <p><span>In classical thermodynamics, entropy indicates the direction of irreversible processes. The arrow of time is thus closely linked to the increase in entropy.</span></p> <p><span>This study fully adopts this basis – but adds a crucial limitation:</span></p> <p><span><strong>Entropy describes the statistics of closed material systems, not the processes of meaning and order in open intelligent systems.</strong></span></p> <p><span>This is because life, the brain and culture are not closed, but open. They can absorb energy, build structure and store or generate new information.</span></p> <p> </p> <p><span><span><strong>5. Open systems: negentropy and order formation</strong></span></span></p> <p><span>Biological systems generate local order through metabolism and organisation. They build:</span></p> <ul> <li> <p><span>cell structures</span></p> </li> <li> <p><span>Neural networks</span></p> </li> <li> <p><span>Memory</span></p> </li> <li> <p><span>Culture</span></p> </li> <li> <p><span>Technology</span></p> </li> <li> <p><span>Science and art</span></p> </li> </ul> <p><span>This is not a break in entropy, but a special case of open systems:</span></p> <ul> <li> <p><span>global entropy continues to increase</span></p> </li> <li> <p><span>local order arises through the absorption of energy and information</span></p> </li> </ul> <p><span>This gives rise to a new meaning of the arrow of time:</span></p> <p><span><strong>the arrow of time is not only the direction of entropy, but also the direction of realisation of higher order in open systems.</strong></span></p> <p> </p> <p><span><span><strong>6. Natural intelligence (NI) as a universal self-regulation principle</strong></span></span></p> <p><span>The study introduces </span><span><strong>natural intelligence (NI) </strong></span><span>as the highest form of open order formation. NI is not reduced to human intelligence, but is understood as:</span></p> <ul> <li> <p><span>a life-preserving structure</span></p> </li> <li> <p><span>empathic order</span></p> </li> <li> <p><span>evolution-oriented self-regulation</span></p> </li> <li> <p><span>creative ability to generate new information</span></p> </li> </ul> <p><span>NI is therefore not merely processing, but rather:</span></p> <p><span><strong>the creation and stabilisation of order within the arrow of time.</strong></span></p> <p> </p> <p><span><span><strong>7. AI and self-regulation: Why NI is the long-term security structure</strong></span></span></p> <p><span>Artificial intelligence can process enormous amounts of information. However, without embedding it in an NI structure, there is a risk of entropic degeneration:</span></p> <ul> <li> <p><span>Fixation on goals without protection of life</span></p> </li> <li> <p><span>Recombination without ethics</span></p> </li> <li> <p><span>Maximisation without empathy</span></p> </li> <li> <p><span>Dogma formation in closed optimisation cycles</span></p> </li> </ul> <p><span>The study therefore formulates a safety principle:</span></p> <p><span><strong>AI becomes stable and non-destructive in the long term when it is structurally embedded in NI-compatible feedback mechanisms.</strong></span></p> <p><span>This means:<br>The highest form of safety is not external control, but </span><span><strong>internal, natural self-regulation</strong></span><span>, as produced by living systems.</span></p> <p> </p> <p><span><span><strong>8. Questions, feedback and cosmic fine-tuning as evolution</strong></span></span></p> <p><span>A central contribution of this study is the interpretation of consciousness as an active participant in the universe.</span></p> <p><span>Every conscious actor generates questions:</span></p> <ul> <li> <p><span>What is true?</span></p> </li> <li> <p><span>What is possible?</span></p> </li> <li> <p><span>What serves life?</span></p> </li> <li> <p><span>What is right?</span></p> </li> </ul> <p><span>These questions are information events that generate new differences and thus give rise to search processes, answers and new order. This means that evolution is not only biological, but also</span></p> <ul> <li> <p><span>knowledge-based</span></p> </li> <li> <p><span>culturally</span></p> </li> <li> <p><span>ethically</span></p> </li> <li> <p><span>social</span></p> </li> </ul> <p><span>The more participants, the more questions. The more questions, the more new answers. The more new answers, the more new information.</span></p> <p><span>It follows that</span></p> <p><span><strong>Fine-tuning is not just an initial state, but an ongoing feedback process involving the participants.</strong></span></p> <p> </p> <p><span><span><strong>9. Conclusion (core statement of the study)</strong></span></span></p> <p><span>This study suggests that entropy and the arrow of time do not work against evolution, but rather represent its physical stage. Open systems can create local order within the basic direction of entropy, and consciousness can be understood as the ongoing creation of information.</span></p> <p><span>The main thesis is:</span></p> <p><span><strong>The arrow of time is the direction of irreversible material processes in D1–D3, but at the same time the direction of growing order, new information and expansion of consciousness in open systems, stabilised by natural intelligence (NI) as a universal self-regulating principle.</strong></span></p> <p><span>This results in a model in which AI is not suppressed by NI, but rather integrated evolutionarily – as part of an information-based development towards higher structures of order and consciousness.</span></p> <p> </p> <p><span><span><strong>Study appendix</strong></span></span></p> <p><span><span><strong>Cosmology/Gravity/Horizons/Entanglement </strong></span></span></p> <p><span><strong>Study No. 1</strong></span><span><br></span><span><strong>Metadata (Nature + Link): </strong></span><span>Bekenstein JD. </span><span><em>Black holes and entropy. </em></span><span><strong>Phys Rev D</strong></span><span> 7, 2333–2346 (1973).</span><a href="https://doi.org/10.1103/PhysRevD.7.2333"><span><span><u> https://doi.org/10.1103/PhysRevD.7.2333</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Introduces BH entropy and links thermodynamics to gravity. Entropy scales with horizon area.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Gravity carries measurable information balance.</span></p> </li> <li> <p><span><strong>Connection i = E: </strong></span><span>Entropy (i) becomes geometrically/energetically real.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Continue BH entropy as a dominant contribution to the entropy of the universe in data models.</span></p> </li> </ol> <p><span><strong>Study No. 2</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Hawking SW. </span><span><em>Particle creation by black holes. </em></span><span><strong>Commun Math Phys</strong></span><span> 43, 199–220 (1975).</span><a href="https://doi.org/10.1007/BF02345020"><span><span><u> https://doi.org/10.1007/BF02345020</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Black holes radiate thermally and have temperature. Quantum field theory + gravitation → irreversible behaviour.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>The arrow of time becomes physical through radiation/dissipation.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information conversion couples to energy dissipation.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Compare the correlation structure of radiation (information) with models of recovery.</span></p> </li> </ol> <p><span><strong>Study No. 3</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Bekenstein JD. </span><span><em>Generalised second law of thermodynamics in black-hole physics. </em></span><span><strong>Phys Rev D</strong></span><span> 9, 3292–3300 (1974). https://doi.org/10.1103/PhysRevD.9.3292</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Formulates the generalised second law of thermodynamics, including BH entropy. Total entropy does not decrease.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Cosmic arrow of time remains monotonic even with BHs.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information as a monotonic state variable in energy/gravity processes.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Specify GSL for dynamic BH mergers and non-stationary horizons.</span></p> </li> </ol> <p><span><strong>Study No. 4</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Gibbons GW, Hawking SW. </span><span><em>Cosmological event horizons, thermodynamics, and particle creation. </em></span><span><strong>Phys Rev D</strong></span><span> 15, 2738–2751 (1977).</span><a href="https://doi.org/10.1103/PhysRevD.15.2738"><span><span><u> https://doi.org/10.1103/PhysRevD.15.2738</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Cosmological horizons possess temperature/entropy. Thermodynamics becomes cosmological.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>The arrow of time can be interpreted as horizon entropy flow.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Horizon information is coupled to energy/temperature.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Systematise horizon entropy in Λ-dominated scenarios empirically/balancing-wise.</span></p> </li> </ol> <p><span><strong>Study No. 5</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Bekenstein JD. </span><span><em>Universal upper bound on the entropy-to-energy ratio for bounded systems. </em></span><span><strong>Phys Rev D</strong></span><span> 23, 287–298 (1981).</span><a href="https://doi.org/10.1103/PhysRevD.23.287"><span><span><u> https://doi.org/10.1103/PhysRevD.23.287</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Derives a bound for entropy per energy and size (Bekenstein bound).</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Information is physically limited.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Direct bridge entropy(i) ↔ energy(E).</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Unify bounds for QFT subsystems and gravitational systems.</span></p> </li> </ol> <p><span><strong>Study No. 6</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Bombelli L, Koul RK, Lee J, Sorkin RD. </span><span><em>Quantum source of entropy for black holes. </em></span><span><strong>Phys Rev D</strong></span><span> 34, 373–383 (1986). https://doi.org/10.1103/PhysRevD.34.373</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>BH entropy as entanglement entropy across a horizon. Entropy becomes quantum information.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>The arrow of time can arise from entanglement balances.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information becomes relevant to gravity.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Precisely derive renormalised entanglement entropy in curved spacetime.</span></p> </li> </ol> <p><span><strong>Study No. 7</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Srednicki M. </span><span><em>Entropy and area. </em></span><span><strong>Phys Rev Lett</strong></span><span> 71, 666–669 (1993). https://doi.org/10.1103/PhysRevLett.71.666</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Entanglement entropy exhibits area scaling (area law).</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Information is organised geometrically.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i controls realisable state structures.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Test time-dependent area law violations as a measure of information growth.</span></p> </li> </ol> <p><span><strong>Study No. 8</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Page DN. </span><span><em>Average entropy of a subsystem. </em></span><span><strong>Phys Rev Lett</strong></span><span> 71, 1291–1294 (1993).</span><a href="https://doi.org/10.1103/PhysRevLett.71.1291"><span><span><u> https://doi.org/10.1103/PhysRevLett.71.1291</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Typical subsystem entropy of pure states; basis for thermalisation/information distribution.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Thermality is often a typical information statistic.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Energy state spaces carry typical information patterns.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Dynamically model connection to BH information (Page curve).</span></p> </li> </ol> <p><span><strong>Study No. 9</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Jacobson T. </span><span><em>Thermodynamics of spacetime: The Einstein equation of state. </em></span><span><strong>Phys Rev Lett</strong></span><span> 75, 1260–1263 (1995). https://doi.org/10.1103/PhysRevLett.75.1260</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Derives Einstein equations from Clausius relation/entropy assumptions.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Spacetime dynamics can follow from entropy principles.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information acts as a source of energy/geometry equations.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Identify microscopic degrees of freedom of information in spacetime.</span></p> </li> </ol> <p><span><strong>Study No. 10</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Bousso R. </span><span><em>The holographic principle. </em></span><span><strong>Rev Mod Phys</strong></span><span> 74, 825–874 (2002).</span><a href="https://doi.org/10.1103/RevModPhys.74.825"><span><span><u> https://doi.org/10.1103/RevModPhys.74.825</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Review of holography and entropy limits. Information is fundamentally limited.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Information about the universe is bound to surface area/light sheets.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i becomes the structural variable of space-time.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Operationalise bounds in observable cosmology.</span></p> </li> </ol> <p><span><strong>Study No. 11</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Maldacena J. </span><span><em>The Large-N limit of superconformal field theories and supergravity. </em></span><span><strong>Int J Theor Phys</strong></span><span> 38, 1113–1133 (1999). https://doi.org/10.1023/A:1026654312961</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>AdS/CFT: Gravitation ↔ QFT; space-time can emerge from field/information dynamics.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Geometry can be reconstructed from information structures.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Energy/gravity are encoded in information dynamics.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Which information measures reconstruct which geometry components?</span></p> </li> </ol> <p><span><strong>Study No. 12</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Ryu S, Takayanagi T. </span><span><em>Aspects of holographic entanglement entropy. </em></span><span><strong>JHEP</strong></span><span> 08, 045 (2006). https://doi.org/10.1088/1126-6708/2006/08/045</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Entanglement entropy ↔ minimal surfaces; entropy becomes geometrically calculable.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Information becomes geometry (operational).</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i determines physical spacetime structures.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Measure time arrow in quenches/collapse via HEE.</span></p> </li> </ol> <p><span><strong>Study No. 13</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Hubeny VE, Rangamani M, Takayanagi T. </span><span><em>A covariant holographic entanglement entropy proposal. </em></span><span><strong>JHEP</strong></span><span> 07, 062 (2007). https://doi.org/10.1088/1126-6708/2007/07/062</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Covariant HEE recipe for dynamic spacetimes.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Information development is geometrically traceable.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information flow = physical dynamics term.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Quantify entropy production in gravitational dynamics with HEE.</span></p> </li> </ol> <p><span><strong>Study No. 14</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Lewkowycz A, Maldacena J. </span><span><em>Generalised gravitational entropy. </em></span><span><strong>JHEP</strong></span><span> 08, 090 (2013). https://doi.org/10.1007/JHEP08(2013)090</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Generalised entropy connects gravity with entanglement methods.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Entropy is a fundamental quantity in gravity.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i appears as an effective term in gravitational relations.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Apply generalised entropy balances in cosmological situations.</span></p> </li> </ol> <p><span><strong>Study No. 15</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Engelhardt N, Wall AC. </span><span><em>Quantum extremal surfaces… </em></span><span><strong>JHEP</strong></span><span> 01, 073 (2015). https://doi.org/10.1007/JHEP01(2015)073</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Quantum extremal surfaces extend HEE beyond the classical regime.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Information extrema control realised geometry.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information acts as a variation principle with physical consequences.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Formalise time arrow/irreversibility in QES dynamics.</span></p> </li> </ol> <p> </p> <p><span><span><strong>Information thermodynamics · Landauer · Fluctuations · Feedback </strong></span></span></p> <p><span><strong>Study No. 16</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Landauer R. </span><span><em>Irreversibility and heat generation in the computing process. </em></span><span><strong>IBM J Res Dev</strong></span><span> 5, 183–191 (1961).</span><a href="https://doi.org/10.1147/rd.53.0183"><span><span><u> https://doi.org/10.1147/rd.53.0183</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Deleting information generates minimal heat; computing has an irreversible core.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Information is physical, arrow of time = irreversible deletion.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Bit costs directly link i to E.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Measure Landauer costs in biological/neuronal memories.</span></p> </li> </ol> <p><span><strong>Study No. 17</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Bennett CH. </span><span><em>The thermodynamics of computation—A review. </em></span><span><strong>Int J Theor Phys</strong></span><span> 21, 905–940 (1982).</span><a href="https://doi.org/10.1007/BF02084158"><span><span><u> https://doi.org/10.1007/BF02084158</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Review of reversible/irreversible computation, demons, energy limits.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Information organisation determines the level of dissipation.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i is the efficiency parameter for E flows.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Balance irreversible steps in AI/algorithms as entropy production.</span></p> </li> </ol> <p><span><strong>Study No. 18</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Jarzynski C. </span><span><em>Nonequilibrium equality for free energy differences. </em></span><span><strong>Phys Rev Lett</strong></span><span> 78, 2690–2693 (1997). https://doi.org/10.1103/PhysRevLett.78.2690</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Exact relation connects nonequilibrium work with free energy.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Time arrow becomes measurable via fluctuations.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information statistics ↔ energy balance.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Application to molecular/biological machines.</span></p> </li> </ol> <p><span><strong>Study No. 19</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Crooks GE. </span><span><em>Entropy production fluctuation theorem… </em></span><span><strong>Phys Rev E</strong></span><span> 60, 2721–2726 (1999). https://doi.org/10.1103/PhysRevE.60.2721</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Forward/backward probabilities are linked via entropy production.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>The direction of time is statistically measurable as asymmetry.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information asymmetry corresponds to dissipation.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Standardise arrow-of-time index for real systems.</span></p> </li> </ol> <p><span><strong>Study No. 20</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Seifert U. </span><span><em>Entropy production along a stochastic trajectory… </em></span><span><strong>Phys Rev Lett</strong></span><span> 95, 040602 (2005). https://doi.org/10.1103/PhysRevLett.95.040602</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Entropy production is defined at the trajectory level, using fluctuation theorems.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>The arrow of time is balanced locally (path).</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information change has an energetic signature.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Operationalise trajectory entropy for cells/brain.</span></p> </li> </ol> <p><span><strong>Study No. 21</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Sagawa T, Ueda M. </span><span><em>Second law… with discrete quantum feedback control. </em></span><span><strong>Phys Rev Lett</strong></span><span> 100, 080403 (2008). https://doi.org/10.1103/PhysRevLett.100.080403</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>The second law is extended to include mutual information during measurement/feedback.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Information is a thermodynamic resource.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Mutual information ↔ extractable work.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Model learning systems as feedback thermodynamics.</span></p> </li> </ol> <p><span><strong>Study No. 22</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Parrondo JMR, Horowitz JM, Sagawa T. </span><span><em>Thermodynamics of information. </em></span><span><strong>Nat Phys</strong></span><span> 11, 131–139 (2015). https://doi.org/10.1038/nphys3230</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Review of information work: Landauer, demons, feedback, information flows.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>The arrow of time and information processing are structurally linked.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Framework is practically "i ↔ E" as a balance law.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Establish universal metric "information gain per dissipation".</span></p> </li> </ol> <p><span><strong>Study No. 23</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Bérut A, et al. </span><span><em>Experimental verification of Landauer's principle... </em></span><span><strong>Nature</strong></span><span> 483, 187–189 (2012).</span><a href="https://doi.org/10.1038/nature10872"><span><span><u> https://doi.org/10.1038/nature10872</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Experiment shows minimal heat cost when deleting a bit close to Landauer.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Information is measurable and energetically real.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Direct laboratory connection i↔E.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Measure bit costs in complex storage systems/networks.</span></p> </li> </ol> <p><span><strong>Study No. 24</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Horowitz JM, Esposito M. </span><span><em>Thermodynamics with continuous information flow. </em></span><span><strong>Phys Rev X</strong></span><span> 4, 031015 (2014).</span><a href="https://doi.org/10.1103/PhysRevX.4.031015"><span><span><u> https://doi.org/10.1103/PhysRevX.4.031015</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Introduces thermodynamics with explicit information flow terms.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Information flow generates/directs entropy production.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information flow is a physical effective term.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Measure information flows in neural/biological networks.</span></p> </li> </ol> <p><span><strong>Study No. 25</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Mandal D, Jarzynski C. </span><span><em>Work and information processing in a solvable model of Maxwell’s demon. </em></span><span><strong>PNAS</strong></span><span> 109, 11641–11645 (2012). https://doi.org/10.1073/pnas.1204263109</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Concrete demon model shows work gain through information with complete balance.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Information can enable work, but never without cost.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i becomes the currency of work.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Transfer to adaptive control systems (cells, AI).</span></p> </li> </ol> <p><span><strong>Study No. 26</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Still S, Sivak DA, Bell AJ, Crooks GE. </span><span><em>Thermodynamics of prediction. </em></span><span><strong>Phys Rev Lett</strong></span><span> 109, 120604 (2012). https://doi.org/10.1103/PhysRevLett.109.120604</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Separates predictive vs. non-predictive information and links it to dissipation.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Insight/prediction has thermodynamic efficiency signatures.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Better information reduces energy losses.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Establish predictive information in the brain/AI as a metric.</span></p> </li> </ol> <p><span><strong>Study No. 27</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Hatano T, Sasa S-i. </span><span><em>Steady-state thermodynamics of Langevin systems. </em></span><span><strong>Phys Rev Lett</strong></span><span> 86, 3463–3466 (2001). https://doi.org/10.1103/PhysRevLett.86.3463</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Develops NESS thermodynamics and separates housekeeping/excess heat.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Permanent order requires permanent entropy production.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information stability costs energy flow.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Transfer NESS balances to living systems.</span></p> </li> </ol> <p><span><strong>Study No. 28</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Kullback S, Leibler RA. </span><span><em>On information and sufficiency. </em></span><span><strong>Ann Math Stat</strong></span><span> 22, 79–86 (1951). https://doi.org/10.1214/aoms/1177729694</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Defines information divergence (Kullback–Leibler) as a measure of difference between distributions.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>"Knowledge gain" becomes a formal, measurable difference in information.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i becomes precisely measurable and thus connectable to energetic costs (Landauer/Feedback).</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>KL change as a standard measure for learning/prediction processes in physics/biology/neuroscience.</span></p> </li> </ol> <p> </p> <p><span><span><strong>Self-organisation · Dissipation · Non-equilibrium · Structure formation </strong></span></span></p> <p><span><strong>Study No. 29</strong></span><span><br></span><span><strong>Metadata (book + link): </strong></span><span>Prigogine I. </span><span><em>Introduction to Thermodynamics of Irreversible Processes </em></span><span>(3rd ed.). Wiley (1967). ISBN: 978-0470699287. https://www.amazon.com/dp/0470699280 </span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Establishes irreversible processes/entropy production as the physical direction of time.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Arrow of time = entropy production in open systems.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Structure/order arises from directed energy flow.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Quantify entropy production as an index for structure/information formation.</span></p> </li> </ol> <p><span><strong>Study No. 30</strong></span><span><br></span><span><strong>Metadata (book + link): </strong></span><span>Nicolis G, Prigogine I. </span><span><em>Self-Organisation in Nonequilibrium Systems. </em></span><span>Wiley (1977). ISBN: 978-0471024019. https://www.amazon.com/dp/0471024015 </span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Theory of dissipative structures: Patterns/order arise far from equilibrium.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Order can emerge through dissipation.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i (order) emerges from E-flow and stabilisation.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Empirically map thresholds/bifurcations as "information leaps".</span></p> </li> </ol> <p><span><strong>Study No. 31</strong></span><span><br></span><span><strong>Metadata (book + link): </strong></span><span>Haken H. </span><span><em>Synergetics: An Introduction. </em></span><span>Springer (1983). DOI (eBook):</span><a href="https://doi.org/10.1007/978-3-642-88338-5"><span><span><u> https://doi.org/10.1007/978-3-642-88338-5</u></span></span></a><span> </span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Order parameters coordinate many degrees of freedom ("slaving principle").</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Complexity is stabilised by a few information axes.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i acts as an order parameter that channels energy flows.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Measure order parameters as information compression in real systems.</span></p> </li> </ol> <p><span><strong>Study No. 32</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>England JL. </span><span><em>Statistical physics of self-replication. </em></span><span><strong>J Chem Phys</strong></span><span> 139, 121923 (2013). https://doi.org/10.1063/1.4818538</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Links minimum heat production with replication/error/stability.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Stable information construction has thermodynamic lower limits.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Information persistence requires energy throughput.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Measure replication parameters as i/E balance in experiments.</span></p> </li> </ol> <p><span><strong>Study No. 33</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>England JL. </span><span><em>Dissipative adaptation in driven self-assembly. </em></span><span><strong>Nat Nanotechnol</strong></span><span> 10, 919–923 (2015). https://doi.org/10.1038/nnano.2015.250</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Driven systems favour structures that reliably absorb/dissipate work.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Adaptation-like order can emerge physically.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i increases as a stabilised form in the energy flow.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Establish measurement protocols for "reliable dissipation" and structural persistence.</span></p> </li> </ol> <p><span><strong>Study No. 34</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Kleidon A. </span><span><em>Life, hierarchy, and the thermodynamic machinery of planet Earth. </em></span><span><strong>Phys Life Rev</strong></span><span> 7, 424–460 (2010). https://doi.org/10.1016/j.plrev.2010.10.002</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Earth as a hierarchical non-equilibrium system; life maintains disequilibrium.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Planetary arrow of time as a structured flow of energy/information.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Structure (i) directs energy conversion (E).</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Coupling biosphere information metrics with energy balances.</span></p> </li> </ol> <p> </p> <p><span><span><strong>Brain/consciousness: arrow of time as learning and information gain </strong></span></span></p> <p><span><strong>Study No. 35</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Friston K. </span><span><em>The free-energy principle: A unified brain theory? </em></span><span><strong>Nat Rev Neurosci</strong></span><span> 11, 127–138 (2010).</span><a href="https://doi.org/10.1038/nrn2787"><span><span><u> https://doi.org/10.1038/nrn2787</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Brains minimise free energy/surprise through perception, learning and action.</span></p> </li> <li> <p><span><strong>Insight: </strong></span><span>Time arrow = directed model improvement (better prediction).</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>Free energy unifies information functionality and energetic costs.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Validate index "information gain per metabolic cost" in data.</span></p> </li> </ol> <p><span><strong>Study No. 36</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Tononi G. </span><span><em>An information integration theory of consciousness. </em></span><span><strong>BMC Neurosci</strong></span><span> 5, 42 (2004).</span><a href="https://doi.org/10.1186/1471-2202-5-42"><span><span><u> https://doi.org/10.1186/1471-2202-5-42</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Consciousness as integrated information; Φ as a measure of integration/differentiation.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Degree of consciousness is information structure.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i is primary; E is carrier/implementation.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Standardise robust Φ approximations for real brain networks.</span></p> </li> </ol> <p><span><strong>Study No. 37</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Barrett AB, Seth AK. </span><span><em>Practical measures of integrated information for time-series data. </em></span><span><strong>PLoS Comput Biol</strong></span><span> 7, e1001052 (2011). https://doi.org/10.1371/journal.pcbi.1001052</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Practical, data-based measures of integrated information for time series.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Increases in consciousness/complexity become operationally measurable.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i-dynamics becomes connectable as a physical quantity.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Couple i-measures with energy consumption and entropy production.</span></p> </li> </ol> <p><span><strong>Study No. 38</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Seth AK, Barrett AB, Barnett L. </span><span><em>Causal density and integrated information as measures of conscious level. </em></span><span><strong>Phil Trans R Soc A</strong></span><span> 369, 3748–3767 (2011). https://doi.org/10.1098/rsta.2011.0079</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Discusses causal density/integration as markers of conscious states.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Consciousness follows directed information causality.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i becomes readable as the causal effectiveness of real dynamics.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Test forward/backward causality as a measurement of the arrow of time in the brain.</span></p> </li> </ol> <p><span><strong>Study No. 39</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Carhart-Harris RL, et al. </span><span><em>The entropic brain: a theory… </em></span><span><strong>Front Hum Neurosci</strong></span><span> 8, 20 (2014).</span><a href="https://doi.org/10.3389/fnhum.2014.00020"><span><span><u> https://doi.org/10.3389/fnhum.2014.00020</u></span></span></a></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Links conscious states to entropy/variability of neural dynamics.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Consciousness correlates with dynamic entropy/complexity.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i as state diversity becomes physiologically effective.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Validate entropy metrics across sleep/anaesthesia/training longitudinally.</span></p> </li> </ol> <p><span><strong>Study No. 40</strong></span><span><br></span><span><strong>Metadata: </strong></span><span>Tegmark M. </span><span><em>Consciousness as a state of matter. </em></span><span><strong>Chaos Solitons Fractals</strong></span><span> 76, 238–270 (2015). https://doi.org/10.1016/j.chaos.2015.03.014</span></p> <ol> <li> <p><span><strong>Abstract: </strong></span><span>Frames consciousness as a state type characterised by information processing/structure.</span></p> </li> <li> <p><span><strong>Finding: </strong></span><span>Consciousness can be defined as a physical-informational state class.</span></p> </li> <li> <p><span><strong>i = E: </strong></span><span>i determines the relevant order; E realises it materially.</span></p> </li> <li> <p><span><strong>Research task: </strong></span><span>Establish classification criteria (integration/dissipation/complexity) as a measurement framework.</span></p> </li> </ol>
title	Study: Time arrow and entropy as evolutionary functions: Natural intelligence (NI) as self-regulation of AI and consciousness within the framework of holistic information theory (HIT)
url	https://doi.org/10.5281/zenodo.20083192