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Institutional dynamics and learning networks.

Philip Poon1, Jessica C Flack1, David C Krakauer1

  • 1Santa Fe Institute, Santa Fe, NM, United States of America.

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Summary
This summary is machine-generated.

This study introduces a learning framework for understanding how institutions evolve. It explains cultural resistance to change through a systemic endowment effect, offering insights into social and scientific revolutions.

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Area of Science:

  • Social Sciences
  • Economics
  • Sociology
  • Science and Technology Studies

Background:

  • Institutions are humanly devised constraints structuring political, economic, and social interactions.
  • Existing definitions are broad, encompassing social norms, laws, companies, and scientific theories.
  • Understanding institutional dynamics is key to explaining social and scientific change.

Purpose of the Study:

  • To present a non-equilibrium, multi-scale learning framework for institutional dynamics.
  • To explain phenomena like quasi-stationarity, periodicity, and switching in institutions.
  • To provide a novel explanation for cultural resistance to change and novelty.

Main Methods:

  • Modeling collective ledger construction by agents.
  • Analyzing how agents perceive and act on institutional 'public positions'.
  • Employing a framework that supports multi-scale learning and non-equilibrium dynamics.

Main Results:

  • The framework supports institutional quasi-stationarity, periodicity, and switching.
  • Public positions, reflecting collective perception, bias agent preferences.
  • A systemic endowment effect through hysteresis explains resistance to novelty.

Conclusions:

  • The proposed learning framework offers a new perspective on institutional evolution.
  • It elucidates the mechanisms behind cultural inertia and resistance to change.
  • This model provides insights into historical social and scientific revolutions.