IV. Ecosmomics: Independent Complex Network Systems, Computational Programs, Genetic Ecode Scripts

3. Iteracy: A Rosetta Ecosmos Textuality

Melko, Roger and Juan Carrasquilla.. Language models for quantum simulation. Nature Computational Science. 4/1, 2024. University of Waterloo, Ontario theorists (search each) consider the latest cross-integrations of these widely separated natural and social realms which then increasingly appear to have an innate, common affinity. As Earth continues to learn into this year, the real presence of an actual recursive narrative from uniVerse to US gains a deep veracity.

A key challenge in the effort to simulate today’s quantum computing devices is the ability to learn and encode the complex correlations that occur between qubits. Emerging technologies based on language models adopted from machine learning have shown unique abilities to learn quantum states. We highlight the contributions that language models are making in the effort to build quantum computers and discuss their future role in the race to quantum advantage.

Min, Semi and Juyong Park. Mapping Out Narrative Structures and Dynamics Using Networks and Textual Information. arXiv:1604.03029. Korea Advanced Institute of Science and Technology (KAIST) linguists show how the new science of scale-free networks are exemplified even by cultural literary works from Victor Hugo’s Les Miserables to J. K. Rowling’s Harry Potter. The various characters and their dramatic interactions are found to exhibit typical node and link topologies and dynamics. Their project is seen as a companion to the statistical physics version by MacCarron and Kenna (search). At the same time we also log in The Network Behind the Cosmic Web (Coutinho) which similarly finds the scale-free networks to fit galactic geometries. Into this 21st century might a textual, cognitive, animate genesis procreation at last be witnessed and availed?

Human communication is often executed in the form of a narrative, an account of connected events composed of characters, actions, and settings. A coherent narrative structure is therefore a requisite for a well-formulated narrative -- be it fictional or nonfictional -- for informative and effective communication, opening up the possibility of a deeper understanding of a narrative by studying its structural properties. In this paper we present a network-based framework for modeling and analyzing the structure of a narrative, which is further expanded by incorporating methods from computational linguistics to utilize the narrative text. Modeling a narrative as a dynamically unfolding system, we characterize its progression via the growth patterns of the character network, and use sentiment analysis and topic modeling to represent the actual content of the narrative in the form of interaction maps between characters with associated sentiment values and keywords. This is a network framework advanced beyond the simple occurrence-based one most often used until now, allowing one to utilize the unique characteristics of a given narrative to a high degree. Given the ubiquity and importance of narratives, such advanced network-based representation and analysis framework may lead to a more systematic modeling and understanding of narratives for social interactions, expression of human sentiments, and communication. (Abstract)

Monakhov, Sergei and Holger Diessel. Complex Words as Shortest Paths in the Network of Lexical Knowledge.. Cognitive Science. 48/11, 2024. Friedrich-Schiller University, Jena system linguists carry out a latest, comprehensive analysis of the English language to show how it is wholly characterized by complex network topologies and emergent behaviors. See also Composition as Nonlinear Combination in Semantic Space: A Computational Characterization of Compound Processing by Tianqi Wang and Xu Xu in this Journal (49/2, 2025) for similar findings in Chinese script. In regard, an extensive 2025 verification of this deeper, common ecode, textual dimension is again achieved, which then by turns implies a natural literary narrative.

Lexical models diverge on how to represent complex words. Under the morpheme-based approach, each morpheme is treated as a separate unit, while in the word-based methods, morphological structure is derived from complex words. In this paper, we propose a computational model for word-based networks to view how complex words are learned, stored, and processed. Our study shows that complex words can be segmented into morphemes through the shortest pathway and novel terms are often formed along optimal paths. Our empirical results are tested by a usage-based grammar which reveals that network science provides a deep language structure. (Excerpt)

ity of the network. In conclusion, network science provides a powerful framework for analyzing language. In this paper, we have focused on central aspects of morphological productivity. However, if we think of language as an encompassing network, the network approach can also be applied to many other phenomena in phonology, morphology, and syntax. This approach is consistent with the way psychologists and neuroscientists analyze the human mind and brain and resonates with the emergentist view of grammar. (28)

Montemurro, Marcelo and Damian Zanette. Complexity and Universality in the Long-range Order of Words. arXiv:1503.01129. These University of Manchester, UK and Centro Atomico Bariloche, Argentina, systems scientists have been collaborating for some time on an illustrious synthesis of linguistics and physics, which as this section reports, are being found to have a deepest affinity. In 2015, a true “universality” can be robustly affirmed. See also Quantifying the Information in the Long-range Order of Words in Cortex (Vol. 55, 2014) by MM, Statistical Patterns in Written Language at arXiv:1412.3336 by DZ, and concurrent papers herein by Altmann, Blythe, and Thurner.

As is the case of many signals produced by complex systems, language presents a statistical structure that is balanced between order and disorder. Here we review and extend recent results from quantitative characterisations of the degree of order in linguistic sequences that give insights into two relevant aspects of language: the presence of statistical universals in word ordering, and the link between semantic information and the statistical linguistic structure. We first analyse a measure of relative entropy that assesses how much the ordering of words contributes to the overall statistical structure of language. This measure presents an almost constant value close to 3.5 bits/word across several linguistic families. Then, we show that a direct application of information theory leads to an entropy measure that can quantify and extract semantic structures from linguistic samples, even without prior knowledge of the underlying language. (Abstract)

Montemurro, Marcelo and Pedro Pury. Long Range Fractal Correlations in Literary Corpora. Fractals. 10/4, 2002. National University of Cordoba and Ciudad University, Cordoba, Argentina mathematicians provide early insights into how the even the composite collection of human writings could be treated with the same self-similar mathematics and geometries as every else in nature.

In this paper, we analyze the fractal structure of long human language records by mapping large samples of texts onto time series. The particular mapping set up in this work is inspired on linguistic basis in the sense that is retains the word as the fundamental unit of communication. The results confirm that beyond the short-range correlations resulting from syntactic rules acting at sentence level, long-range structures emerge in large written language samples that give rise to long-range correlations in the use of words. (Abstract)

Montgomery, Scott. Science in Translation. Chicago: University of Chicago Press, 2000. A literate inquiry of how knowledge moves between cultures and over historical time. From Greece, Rome, Arabia, and China to medieval Europe and onto modern Japan, each script and language reflects the same starry heavens, physical forces and evolving organisms.

Morales, Jose, et al. Rank Dynamics of Word Usage at Multiple Scales. arXiv:1802.07258. We note this posting by National Autonomous University of Mexico, and Aalto University, Finland computer scientists including Carlos Gershenson as a current example of how linguistic textual writings can be treated by and seen to exemplify the same complex network systems just like quantum and genomic to neural and societal. A great truth and realization is thus arising in our midst whence natural phenomena becomes, in turn, deeply textual in kind, a library of cosmos and culture. See also Quantifying the Information in the Long Range Order of Words: Semantic Structures and Universal Linguistic Constraints by Marcelo Montemurro in Cortex (Vol. 55, 2014)

The recent dramatic increase in online data availability has allowed researchers to explore human culture with unprecedented detail, such as the growth and diversification of language. In particular, it provides statistical tools to explore whether word use is similar across languages, and if so, whether these generic features appear at different scales of language structure. Here we use the Google Books N-grams dataset to analyze the temporal evolution of word usage in several languages. Using different methods, results show that there are generic properties for different languages at different scales, such as a core of words necessary to minimally understand a language. (Abstract)

Mufwene, Salikoko, et al. Complexity in Language: Developmental and Evolutionary Perspectives. Cambridge: Cambridge University Press, 2017. A collection of chapters which consider an innate synthesis of these natural communicative aspects and affinities. It was conceived by Mufwene, the senior University of Chicago linguist (search), along with University of Lyon, France coeditors Christophe Coupe and Francois Pellegrino and draws on conferences and studies since 2011. A wide array includes Complex Adaptive Systems Approach to the Evolution of Language and the Brain by Thomas Schoenemann, A Complexity View of Ontogeny as a Window on Phylogeny by Barbara L. Davis, and Evolutionary Complexity of Social Cognition, Semasiographic Systems and Language by William Croft.

The question of complexity, as in what makes one language more 'complex' than another, is a long-established topic of debate amongst linguists. Recently, this issue has been complemented with the view that languages are complex adaptive systems, in which emergence and self-organization play major roles. However, few students of the phenomenon have gone beyond the basic assessment of the number of units and rules in a language (what has been characterized as 'bit complexity') or shown some familiarity with the science of complexity. This book reveals how much can be learned by overcoming these limitations, especially by adopting developmental and evolutionary perspectives. The contributors include specialists of language acquisition, evolution and ecology, grammaticization, phonology, and modeling, all of whom approach languages as dynamical, emergent, and adaptive complex systems.

Mukherjee, Animesh, et al. Self-Organization of the Sound Inventories: Analysis and Synthesis of the Occurrence and Co-occurrence Networks of Consonants. Journal of Quantitative Linguistics. 16/2, 2009. Indian Institute of Technology, and Microsoft Research India, computer scientists quantify the presence of nature’s constant creative principles even in all aspects of our human speech and script. The quotes could equally apply to genomic dynamics and discourse.

The sound inventories of the world's languages self-organize themselves, giving rise to similar cross-linguistic patterns. In this work, we attempt to capture this phenomenon of self-organization, which shapes the structure of the consonant inventories, through a complex network approach. For this purpose we define the occurrence and co-occurrence networks of consonants and systematically study some of their important topological properties. A crucial observation is that the occurrence as well as the co-occurrence of consonants across languages follows a power law distribution. This property is arguably a consequence of the principle of preferential attachment. In order to support this argument we propose a synthesis model, which reproduces the degree distribution for the networks to a close approximation. Finally, we discuss how preferential attachment manifests itself through the evolutionary nature of language. (Abstract, 157)

Sound inventories of human languages show a considerable degree of symmetry. This symmetry is primarily a reflection of the self-organizing behavior that goes on in shaping the structure of the inventories (Oudeyer, 2006) and for that matter language. In fact, the main premise of synergetic linguistics is that language is a self-organizing and self-regulating system and its existence, properties, and change can be successfully explained within this framework. The symmetries, therefore, are primarily an outcome of the dynamic interdependence of the structure and the functions of a language. (157-158)

Murugan, Arvind, et al. Discriminatory Proofreading Regimes in Nonequilibrium Systems. Physical Review X. 4/021016, 2014. Murugan, with Stanislas Leibler, Systems Biology, and David Huse, Physics, Princeton University first note that biological activities could be viewed as an organic editorial process, which might then have broader apply across natural realms. The work draws on an earlier paper Speed, Dissipation, and Error in Kinetic Proofreading by the authors in PNAS (109/12034, 2012). Our interest is a perception of life and our human phase as engaged in some cosmic writing and reading project, ever trying for better clarity and content.

We use ideas from kinetic proofreading, an error-correcting mechanism in biology, to identify new kinetic regimes in nonequilibrium systems. These regimes are defined by the sensitivity of the occupancy of a state of the system to a change in its energy. In biological contexts, higher sensitivity corresponds to stronger discrimination between molecular substrates with different energetics competing in the same reaction. We study this discriminatory ability in systems with discrete states that are connected by a general network of transitions. (Abstract)

Najafi, Elham and Amir Darooneh. A New Universality Class in Corpus of Texts: A Statistical Physics Study. Physics Letters A. 382/1140, 2018. University of Zanjan, Iran physicists continue their endeavor to achieve a complex network systems analysis of written documents, which are similarly amenable to this approach, just as all other domains. See also The Fractal Patterns of Words in a Text by the authors in PloS One (Online June 19, 2015). In cultural regard, as a natural, and social realms become deeply scriptural, could these multiple findings be the advent of a worldwise 21st century dispensation so as to newly unite the many historic divisions?

Text can be regarded as a complex system. There are some methods in statistical physics which can be used to study this system. In this work, by means of statistical physics methods, we reveal new universal behaviors of texts associating with the fractality values of words in a text. The fractality measure indicates the importance of words in a text by considering distribution pattern of words throughout the text. We observed a power law relation between fractality of text and vocabulary size for texts and corpora. We also observed this behavior in studying biological data. (Abstract)

In this research, by studying texts from statistical physics stand-point, we introduced some new universal behaviors of texts. The universal behaviors are observed about fractal value of words in texts. To explain this issue, we used the concept of fractals to as-sign an importance value to each word-type in a text. Then, we defined text fractality and studied the relation between this quantity and vocabulary size for a large number of texts from Open National American Corpus and also a lot of DNA sequences. We also studied the relation between maximum fractality in a text and the relation between text fractality and sum of combined measure valuand reported some power behaviors in these cases. (9)

Nefdt, Ryan. Biolinguistics and Biological Systems: A Complex Systems Analysis of Language. Biology & Philosophy. March, 2023. A University of the Cape Town, RSA (search RN website) enters a latest synthesis of life’s communicative essence, as biosemiotic now articulates (see Ch. V), with a wide array of animate nonlinear features as they have likewise become recently identified. With some 140 references across 21st century studies from Chomsky to Pattee, Deacon, De Boer and many more, it is asserted that typical modular, robust, nested structures, adaptive, recursion, qualities are also strongly present in speech and script. See also A Note on Retrodiction and Machine Evolution by Gustavo Caetano-Anolles at arXiv:2303.14590 for a companion view.

In their recent book, Ladyman and Wiesner (What is a Complex System?, Yale UP, 2020) offer an exemplary synopsis of the interdisciplinary field of complexity science to date. Here, I extend their feature survey to include the formal study of natural language, i.e. linguistics. Indeed, I argue that language exhibits many of the hallmarks of a complex biological system. In regard, I will advocate the ‘Minimalist Program’ (Chomsky, MIT, 1995), which cites basic underlying mechanisms that, in their idealizations, such a novel biolinguistics should embrace a ‘Maximalist Program’ in which multiple subfields contribute component explanations to an emerging whole. (Excerpt)

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