[Fis] FIS discussion

[Magnús S Magnússon - HI]

I would like to offer the following text as a discussion starter. It summarizes a long-running research program that moves from real-time behavioral patterns to molecular strings and to the organization of societies. Its central claim is that modern human mass societies are uniquely self-similar to the protein societies of their own cells. I would very much welcome your comments and criticism.

Best regards,
Magnus S. Magnusson
Human Behavior Laboratory, University of Iceland

>From Biological T-Strings to AI Autonomous Text Agents: A Final Stage in the Evolution of "T-Societies"?
My main claim is simple. Around 1800, human mass societies suddenly became self-similar to the intracellular protein societies of their own members' cells. No other animal species, and indeed no other multicellular species, shows this self-similarity. I suggest it marks a major break in the evolution of multicellular life. Here is how I got there.
T-patterns
My research began in the 1970s, inspired by ethology, and focused on interaction and social organization in humans, primates, and social insects. A T-pattern is a hierarchical, multi-ordinal, self-similar pattern. It consists of event types that recur in the same order, with significantly invariant intervals between them, across multiple point series on a discrete time scale. I developed the T-pattern Analysis, TPA, Algorithm (later the THEME software, first presented in an international AI workshop in 1981) to find repeated real-time patterns in children's dyadic interactions. It revealed far more complex patterns than expected. Later applications to animal behavior and brain neuronal networks also detected complex, highly significant T-patterns. The approach began with minimal computing power (a PDP-8) and small, manually coded video samples, thus differs greatly from later big-data AI.
T-strings, GEIPITs, and T-societies
Moving to the molecular level, T-strings are physical strings with T-patterns, such as DNA, RNA, proteins, and text. In the intracellular protein society, ribosomes build each protein from a gene, a segment of DNA. The protein's behavioral potential thus largely depends on information stored outside the protein itself. I call a society of many kinds of specialized individuals, each defined by segments of a Giant Extra-individual Purely Informational T-string (GEIPIT), a T-society. The genome is the GEIPIT of the protein society. The textome, the totality of human text, is the GEIPIT of literate human society.
T-societies differ from Interactive Emergence Societies (IES). In an IES, individuals' behavior arises mainly through interaction rather than from an external informational string. The RNA world and all animal societies are IES. Even the highly specialized castes of social insects arise mainly through development and interaction, not from segments of an external string. Human societies long remained IES too. Writing, from around 3000 BC, created the human GEIPIT. But only from around 1800 did mass schooling shape most individuals' behavioral potentials and tendencies through textual GEIPIT segments called curricula. Human societies thus became T-societies, self-similar to protein T-societies.
In biological terms, this self-similarity appeared in a split second, and it is unique among multicellular species. It coincided with an unprecedented expansion of human behavioral potential, notably in science and technology. Genes and curricula are both GEIPIT segments that specify specialized individuals. Given this close structural analogy, it seems hard to regard this unique self-similarity as a mere coincidence. The expansion now extends to digital agents and robots. These are new kinds of individuals with far more efficient access to the human GEIPIT, and they can extend it independently. Some foresee them eventually surpassing or merging with humans.
Toward Artificial Social Pattern Intelligence
Current AI is inspired by neuronal networks. I suggest it could be extended into an Artificial Social Pattern Intelligence. This would model the networks of human T-societies, which are far more complex than individual brains. Given the striking behavior of human masses, individual people sometimes act less like autonomous intelligences and more like nodes in a larger network. Influence waves, whether religious or political, spread across such networks somewhat as signals spread among neurons.
References

  *   Magnusson, M. S. (2000). Discovering hidden time patterns in behavior: T-patterns and their detection. Behavior Research Methods, Instruments, & Computers, 32(1), 93–110.
  *   Magnusson, M. S. (2023). Sudden bio-mathematical self-similarity and the uniqueness of human mass societies: from T-patterns and T-strings to T-societies. Frontiers in Psychology, 14, 1157315. doi.org/10.3389/fpsyg.2023.1157315
  *   Magnusson, M. S. (2025). Understanding T-societies: How patterns shape our lives. Open Access Government, January 2025.

Short video summary: scitube.io/professor-magnus-s-magnusson-the-surprising-similarities-between-human-cells-and-societies/


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Magnus S. Magnusson, Ph.D.
Emeritus Research Professor
Human Behavior Laboratory: https://urldefense.com/v3/__http://www.hbl.hi.is__;!!D9dNQwwGXtA!W6mM61KA63vUGPBq3Q20SCGKVdJCqF5m6Dy_mikXdJU-VPeqs-2y_KZElD9h2Q8rf4sL0qHpazM$ <https://urldefense.com/v3/__http://www.hbl.hi.is/__;!!D9dNQwwGXtA!W6mM61KA63vUGPBq3Q20SCGKVdJCqF5m6Dy_mikXdJU-VPeqs-2y_KZElD9h2Q8rf4sLvyUj3ps$ >

School of Health Sciences

University of Iceland: https://urldefense.com/v3/__http://www.hi.is__;!!D9dNQwwGXtA!W6mM61KA63vUGPBq3Q20SCGKVdJCqF5m6Dy_mikXdJU-VPeqs-2y_KZElD9h2Q8rf4sLQzsfosw$ <https://urldefense.com/v3/__http://www.hi.is/__;!!D9dNQwwGXtA!W6mM61KA63vUGPBq3Q20SCGKVdJCqF5m6Dy_mikXdJU-VPeqs-2y_KZElD9h2Q8rf4sLPulADQE$ >
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