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Modelling cultural evolution

Fredrik Jansson

TL;DR

Modelling cultural evolution addresses how ideas spread and change by linking micro-level cognitive processes to macro-level patterns. It surveys four modelling paradigms—Reasoning (Bayesian inference and game theory), Adaptivity (reinforcement learning and evolutionary game theory), Mass-action/mean-field (population dynamics and compartmental models), and Complexity (agent-based models and social network analysis)—and presents a general template that traces how system states, cognitive updates, behaviour, and macro outcomes form dynamic feedback loops. The chapter argues for a pluralist, coherent framework rather than a single model, and highlights when analytical versus computational approaches are appropriate. It demonstrates how simple, transparent models reveal core mechanisms such as conformity biases, tipping points, and diffusion through networks, while connecting models to empirical data to understand cultural change across domains like norms, language, technology diffusion, and memes. Overall, it offers a precise language and flexible toolkit for testing hypotheses about cultural dynamics with broad applicability.

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.

Modelling cultural evolution

TL;DR

Modelling cultural evolution addresses how ideas spread and change by linking micro-level cognitive processes to macro-level patterns. It surveys four modelling paradigms—Reasoning (Bayesian inference and game theory), Adaptivity (reinforcement learning and evolutionary game theory), Mass-action/mean-field (population dynamics and compartmental models), and Complexity (agent-based models and social network analysis)—and presents a general template that traces how system states, cognitive updates, behaviour, and macro outcomes form dynamic feedback loops. The chapter argues for a pluralist, coherent framework rather than a single model, and highlights when analytical versus computational approaches are appropriate. It demonstrates how simple, transparent models reveal core mechanisms such as conformity biases, tipping points, and diffusion through networks, while connecting models to empirical data to understand cultural change across domains like norms, language, technology diffusion, and memes. Overall, it offers a precise language and flexible toolkit for testing hypotheses about cultural dynamics with broad applicability.

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.
Paper Structure (16 sections, 13 equations, 5 figures)

This paper contains 16 sections, 13 equations, 5 figures.

Table of Contents

  1. Introduction
  2. On modelling
  3. Modelling paradigms in cultural evolution
  4. Reasoning
  5. Bayesian inference
  6. Game theory
  7. Adaptivity
  8. Reinforcement learning
  9. Evolutionary game theory
  10. Mass action or mean field
  11. Population dynamics
  12. Compartmental models
  13. Complexity
  14. Agent-based models
  15. Social network analysis
  16. ...and 1 more sections

Figures (5)

  • Figure 1: Simulations without and with a conformity bias. The number of guests at each of five restaurants is plotted over time.
  • Figure 2: Adaptation of Coleman’s boat for cultural evolution. Relationship between macrolevel cultural context and microlevel individual actions.
  • Figure 3: Modelling approaches in two dimensions.
  • Figure 4: A model of the modelling process.
  • Figure 5: Common modelling paradigms in cultural evolution. The paradigms are mapped out in a coordinate system according to agents’ level of evaluation and the scale of the modelled entities. The paradigms are grouped into four grey areas, representing groups of models.