The establishment of norms in the multivariate Illegal Strategy Metanorm Game model

Xing Jin, Wenhao Zhang, Ziyou Fan, Chao Wang and Zhen Wang

Chaos, Solitons & Fractals, 2025, vol. 201, issue P1

Abstract: Social norms (collective solutions to social dilemmas) in the real world regulate degrees of wrongdoing, yet classical metanorm games treat violations as binary. We propose the Multivariate Illegal Strategy Metanorm (MISM) model, extending Axelrod’s framework to an n-level offense scale with matching gradient punishments, and introduce a reinforcement-learning updater, Multivariate Boldness-Vengefulness Learning (MBVL), that blends Q-learning and evolutionary imitation. Large-scale Monte Carlo simulations on lattice, Watts–Strogatz, and Barabási–Albert networks reveal that (i) finer-grained violation levels accelerate the eradication of severe offenders; (ii) larger neighborhood amplify cascading peer punishment; and (iii) graded sanctions achieve higher collective welfare than uniform ones. MISM thus bridges binary-norm models and graded real-world governance, providing a scalable test-bed for studying multilevel norm emergence and compliance. The source code for the MISM model and MBVL algorithm is publicly available at: https://github.com/DAISec-Lab/MISM.

Keywords: Social dilemma; Norm emergence; Punishment mechanism; Metanorm; Multivariate decision-making (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:201:y:2025:i:p1:s0960077925012172

DOI: 10.1016/j.chaos.2025.117204

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