A Theory of Appropriateness That Accounts for Norms of Rationality
Joel Z. Leibo, Alexander Sasha Vezhnevets, Manfred Diaz, John P. Agapiou, William A. Cunningham, Peter Sunehag, Logan Cross, Raphael Koster, Stanley M. Bileschi, Minsuk Chang, Iyad Rahwan, Simon Osindero, James A. Evans

TL;DR
This paper introduces a society-first theory of normative appropriateness based on predictive pattern completion in individuals modeled as LLM-like agents, explaining human norms without scalar rewards.
Contribution
It offers a novel cognitive model of norms based on pattern completion, challenging rational-choice theories and reconceptualizing dual-process data.
Findings
Norms are context-dependent, arbitrary, automatic, and dynamic.
Explicit norms relate to in-context adaptation, implicit norms to long-term memory.
The theory accounts for social sanctioning and challenges traditional rationality models.
Abstract
We propose a society-first theory of normative appropriateness where individuals, modeled as pre-trained actors with cognitive architectures analogous to Large Language Models (LLMs), generate behavior via predictive pattern completion. Our theory posits that individuals act by completing distributed symbolic patterns based on context, answering questions such as "What does a person such as I do in a situation such as this?". This sense-making mechanism provides a parsimonious account of the key features of human norms: their context-dependence, arbitrariness, automaticity, dynamism, and their support from social sanctioning. It challenges rational-choice theories of social norms by accounting for their key features without needing to exogenously posit scalar rewards or preference relations. By distinguishing between explicit norms, which we associate with in-context adaptation, and…
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Taxonomy
TopicsLanguage and cultural evolution · Embodied and Extended Cognition · Child and Animal Learning Development
