The dynamics of cultural systems

TL;DR

This paper models culture as a dynamic, interconnected system where beliefs, practices, and artifacts evolve through information processing, influencing and being influenced by AI technologies like LLMs and recommender systems.

Contribution

It introduces a comprehensive framework for understanding cultural evolution as a self-organizing system driven by coherence-seeking information processing and emergent metafilters.

Findings

Culture exhibits path dependence and attractor states.

Cultural learning involves filtering, transforming, and recombining information.

AI systems like LLMs influence cultural dynamics through automated information filtering.

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.