A Theory of Appropriateness That Accounts for Norms of Rationality

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 implicit norms, which we associate with long-term memory, the theory reconceptualizes several foundational ideas in cognitive science. In particular, it gives an alternative account to the data traditionally seen as supporting dual-process models, and it flips the role of rationality, allowing us to construe it as adherence to culturally-contingent justification standards.