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Inferring Cultural Landscapes with the Inverse Ising Model.

Victor Møller Poulsen1, Simon DeDeo1,2

  • 1Department of Social and Decision Sciences, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.

Entropy (Basel, Switzerland)
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

Researchers adapted machine learning to map cultural evolution, revealing a complex landscape of human beliefs. This method overcomes data limitations, showing distinct patterns for state religions versus other spiritual practices.

Keywords:
anthropologyarchaeologycultural evolutionhistoryinverse Ising modelmachine learningreligionrobust statisticsspin glass

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Area of Science:

  • Computational Social Science
  • Cultural Evolution
  • Machine Learning Applications

Background:

  • Human cultural evolution explores a vast possibility space, constrained by cognitive and social factors.
  • Understanding the structure of this cultural
  • fitness landscape
  • is crucial but challenging due to historical data limitations.

Purpose of the Study:

  • To adapt machine learning algorithms for analyzing sparse, inconsistent historical data on cultural evolution.
  • To reconstruct the underlying constraints shaping cultural configurations without introducing bias.

Main Methods:

  • Adapted the minimum probability flow algorithm and Inverse Ising model for historical datasets.
  • Incorporated extensions like dynamical estimation of missing data and cross-validation with regularization.
  • Applied methods to the Database of Religious History (407 religious groups).

Main Results:

  • Successfully reconstructed the cultural fitness landscape from historical religious data.
  • Identified a complex, rugged landscape with distinct regions.
  • Observed state-endorsed religions concentrated on sharp peaks, while evangelical, non-state, and mystery religions occupied diffuse floodplains.

Conclusions:

  • The adapted machine learning approach reliably reconstructs cultural evolutionary landscapes.
  • The findings reveal a structured cultural landscape influenced by historical constraints.
  • This methodology offers a bias-aware tool for studying diverse cultural histories.