A New Way to Think About Information

In the paper's words

The absence of a unified, ontologically grounded theory of information hampers interdisciplinary research, obscures the design of data‑centric technologies, and inflates costs in fields ranging from quantum physics to artificial intelligence. Existing syntactic accounts such as Shannon’s theory and semantic approaches remain fragmented, while proposals that treat information as a fundamental “stuff” lack clear derivations. This paper integrates the autaxic generative engine—spontaneous symmetry breaking, relational processing, and conservation of distinguishability—into a coherent ontology of information.

The abstract, verbatim, and its opening sentence is the claim. Every line below is quoted from the text.

Numbers it reports

1. We complement the conceptual development with two quantitative illustrations derived solely from published sources: (1) a combinatorial estimate of the configuration space of a spin‑glass system studied with up to $10^{3}$ spins, yielding $2^{1000}\approx1.07\times10^{301}$ distinct states; (2) an average conference acceptance rate computed from reported 10 %–20 % ranges, giving $15\%$ as a representative figure.

   from Abstract

2. Computation of an average conference acceptance rate from the 10 %–20 % range reported in [7] (Section 4).

   from 1. Introduction

3. Inputs (source [7]): Reported range: “acceptance rates … range between 10 % and 20 %.” Specific example: “ICSE … 13 %.” We compute the arithmetic mean $\bar{r}$ of the lower and upper bounds: $$ \begin{aligned} \bar{r} &= \frac{10\% + 20\%}{2} \\ &= \frac{0.10 + 0.20}{2} \\ &= \frac{0.30}{2} \\ &= 0.15 \; (=15\%). \end{aligned} $$ The result $15\%$ serves as a representative acceptance probability for a typical selective conference, quantifying the fraction of submitted informational artefacts that survive peer filtering.

   from 4.2 Average Conference Acceptance Rate

4. Result R2 (Section 4.2): The average acceptance rate across the reported 10 %–20 % range is $\bar{r}=15\%$, providing a quantitative measure of the informational bottleneck in scholarly communication channels.

   from 5. Results

5. Derivation of a combinatorial bound on the number of possible spin‑glass configurations using the $10^{3}$‑spin limit reported in [2] (Section 4).

   from 1. Introduction

6. This underscores the need for a unifying conceptual substrate that transcends disciplinary fashions. [2] demonstrates that “algorithms from computer science have ‘broken the size barrier’ for some problems in statistical physics” and explicitly states that “with the genetic algorithm, we can study up to of order $10^{3}$ spins.” The spin‑glass model therefore supplies a concrete bound on the size of a physical system amenable to computational analysis. [3] introduces a practical pipeline for visualising research papers by extracting multi‑word terms from titles, ranking and filtering them, and embedding them in a geographic‑map metaphor.

   from 2. Background and Related Work

How it is done

1. Derivation of a combinatorial bound on the number of possible spin‑glass configurations using the $10^{3}$‑spin limit reported in [2] (Section 4).

   from 1. Introduction

2. This underscores the need for a unifying conceptual substrate that transcends disciplinary fashions. [2] demonstrates that “algorithms from computer science have ‘broken the size barrier’ for some problems in statistical physics” and explicitly states that “with the genetic algorithm, we can study up to of order $10^{3}$ spins.” The spin‑glass model therefore supplies a concrete bound on the size of a physical system amenable to computational analysis. [3] introduces a practical pipeline for visualising research papers by extracting multi‑word terms from titles, ranking and filtering them, and embedding them in a geographic‑map metaphor.

   from 2. Background and Related Work

3. Our approach proceeds in three stages: Ontological Construction – We define four hierarchical levels of information (Section 2) based on autaxic dynamics: (i) Primordial Distinctions generated by SSB, (ii) Stabilised Process‑Patterns formed through feedback and resonance, (iii) Relational Information emerging via relational processing, and (iv) Functional/Semantic Information arising in complex adaptive systems.

   from 3. Methods

4. Inputs (source [7]): Reported range: “acceptance rates … range between 10 % and 20 %.” Specific example: “ICSE … 13 %.” We compute the arithmetic mean $\bar{r}$ of the lower and upper bounds: $$ \begin{aligned} \bar{r} &= \frac{10\% + 20\%}{2} \\ &= \frac{0.10 + 0.20}{2} \\ &= \frac{0.30}{2} \\ &= 0.15 \; (=15\%). \end{aligned} $$ The result $15\%$ serves as a representative acceptance probability for a typical selective conference, quantifying the fraction of submitted informational artefacts that survive peer filtering.

   from 4.2 Average Conference Acceptance Rate

What would falsify it

1. The autaxic framework predicts that informational capacity scales exponentially with the number of primordial distinctions.

   from 6.2 Failure Modes and Falsifiability

2. If a physical system with $N$ binary degrees of freedom were shown to possess fewer than $2^{N}$ distinguishable states due to a fundamental principle that caps distinguishability, the exponential scaling claim would be falsified.

   from 6.2 Failure Modes and Falsifiability

3. Observation of a deviation from $2^{N}$ would falsify the primitive‑distinction counting assumption.

   from 7. Practical Implications and Engineering Pathways

Where the claim stops

1. Assumption Sensitivity Binary spin model (each spin $\pm1$) High: multi‑state spins would increase $C$ dramatically.

   from 6.1 Limitations

2. The framework thus offers a parsimonious, testable foundation for future formalisation of information across disciplines, while clearly delineating its assumptions and limits.

   from 8. Conclusion

3. Assumptions are explicitly stated (binary spins, uniform acceptance distribution) and labelled as such.

   from 3. Methods

Where to read

1. Abstract187 words · 1 min
2. 1. Introduction245 words · 1 min
3. 2. Background and Related Work243 words · 1 min
4. 3. Methods176 words · 1 min
5. 4. Analysis201 words · 1 min
6. 5. Results72 words · 1 min
7. 6. Discussion206 words · 1 min
8. 7. Practical Implications and Engineering Pathways236 words · 1 min
9. 8. Conclusion77 words · 1 min
10. Version History40 words · 1 min
11. Provenance and AI Involvement225 words · 1 min