nep-evo New Economics Papers
on Evolutionary Economics
Issue of 2026–01–05
two papers chosen by
Matthew Baker, City University of New York


  1. Modelling cultural evolution By Jansson, Fredrik
  2. The dynamics of cultural systems By Jansson, Fredrik

  1. By: Jansson, Fredrik
    Abstract: Formal modelling provides a toolkit for understanding cultural dynamics, from individual decisions to recurring patterns of change. This chapter explains what models are and why they matter. Using a precise, shared language, they aid thinking and communication by turning fuzzy assumptions into clear, comparable, testable claims. The chapter describes the modelling process, trading explanatory clarity against predictive specificity. Four families of models are surveyed, from the micro-level with optimising agents to macro-level dynamics with heuristic or even implicit agents, covering reasoning (Bayesian inference, game theory), adaptive updating (reinforcement learning, evolutionary games), mean-field approaches (compartmental models, population dynamics), and complex systems (agent-based models, social networks). Building on these, a general template for modelling cultural evolution is outlined that connects system states, cognitive processes, behaviour, and macro-level outcomes in dynamic loops, linking individuals, groups, institutions, and their environments. Taken together, these tools support a pluralist but coherent understanding of cultural change.
    Date: 2026–01–02
    URL: https://d.repec.org/n?u=RePEc:osf:socarx:h4xjs_v1
  2. By: Jansson, Fredrik
    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.
    Date: 2026–01–02
    URL: https://d.repec.org/n?u=RePEc:osf:socarx:drmkw_v1

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