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The dynamics of cultural systems

Fredrik Jansson

TL;DR

This work frames culture as a dynamic, multi-layered system where cognitive, social and material embeddings create interconnected trait networks that evolve through coherence-driven learning, filtering, and recombination. It integrates a spectrum of mechanisms—preservative transmission, biased transformation, dissonance-driven reconfiguration, and higher-order metafilters—into a unified view of cultural evolution, capable of generating attractor basins, epistmic niches, and rapid regime shifts. It also foregrounds the role of artificial intelligence, notably recommender systems and generative models, as both products and drivers of cultural dynamics, capable of stabilising existing patterns or enabling new recombinations that reshape future evolution. The framework highlights the practical significance of understanding these dynamics for forecasting cultural change, designing interventions, and anticipating AI-driven effects on information flows, polarization, and innovation across societies.

Abstract

Culture is not just traits but a dynamic system of interdependent beliefs, practices and artefacts embedded in cognitive, social and material structures. Culture evolves as these entities interact, generating path dependence, attractor states and tension, with long-term stability punctuated by rapid systemic transformations. Cultural learning and creativity is modelled as coherence-seeking information processing: individuals filter, transform and recombine input in light of prior acquisitions and dissonance reduction, thereby creating increasingly structured worldviews. Higher-order traits such as goals, skills, norms and cognitive gadgets act as emergent metafilters that regulate subsequent selection by defining what counts as coherent. Together, these filtering processes self-organise into epistemic niches, echo chambers, polarised groups and institutions that channel information flows and constrain future evolution. In this view, LLMs and recommender algorithms are products of cultural embeddings that now act back on cultural systems by automated filtering and recombination of information, reshaping future dynamics of cultural systems.

The dynamics of cultural systems

TL;DR

This work frames culture as a dynamic, multi-layered system where cognitive, social and material embeddings create interconnected trait networks that evolve through coherence-driven learning, filtering, and recombination. It integrates a spectrum of mechanisms—preservative transmission, biased transformation, dissonance-driven reconfiguration, and higher-order metafilters—into a unified view of cultural evolution, capable of generating attractor basins, epistmic niches, and rapid regime shifts. It also foregrounds the role of artificial intelligence, notably recommender systems and generative models, as both products and drivers of cultural dynamics, capable of stabilising existing patterns or enabling new recombinations that reshape future evolution. The framework highlights the practical significance of understanding these dynamics for forecasting cultural change, designing interventions, and anticipating AI-driven effects on information flows, polarization, and innovation across societies.

Abstract

Culture is not just traits but a dynamic system of interdependent beliefs, practices and artefacts embedded in cognitive, social and material structures. Culture evolves as these entities interact, generating path dependence, attractor states and tension, with long-term stability punctuated by rapid systemic transformations. Cultural learning and creativity is modelled as coherence-seeking information processing: individuals filter, transform and recombine input in light of prior acquisitions and dissonance reduction, thereby creating increasingly structured worldviews. Higher-order traits such as goals, skills, norms and cognitive gadgets act as emergent metafilters that regulate subsequent selection by defining what counts as coherent. Together, these filtering processes self-organise into epistemic niches, echo chambers, polarised groups and institutions that channel information flows and constrain future evolution. In this view, LLMs and recommender algorithms are products of cultural embeddings that now act back on cultural systems by automated filtering and recombination of information, reshaping future dynamics of cultural systems.
Paper Structure (18 sections, 1 figure)

This paper contains 18 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Embeddings
  3. Trait relations
  4. Embeddings and co-evolution
  5. Interaction with other systems
  6. Information processing
  7. Preservative selection versus biased transformation
  8. Dissonance
  9. Filters
  10. Recombination and creativity
  11. Metafilters and superstructures
  12. Emergence
  13. Complex patterns
  14. Cultural niches
  15. Groups
  16. ...and 3 more sections

Figures (1)

  • Figure 1: Information filtering. The sender exposes those beliefs most congruent with their self-image and its ideas about the receiver. The receiver accepts or rejects traits based on how they or the sender fit into their current belief system.