A Theory of Appropriateness That Accounts for Norms of Rationality
🤖 AI Summary
Traditional rational choice theory struggles to account for the context-dependence, arbitrariness, automaticity, and dynamism of social norms due to its reliance on exogenously given utilities or preferences. This work proposes a “social-first” theory of normative appropriateness, modeling individuals as pre-trained agents endowed with a cognitive architecture akin to large language models. These agents determine action by answering the question, “What would someone like me do in this situation?” through a context-driven, distributed mechanism of symbolic pattern completion. The framework dispenses with exogenous reward assumptions, distinguishes explicit norms (contextually adapted) from implicit norms (stored in long-term memory), reconfigures dual-process models, and reconceptualizes rationality as adherence to culturally specific standards of justification. Offering a unified and parsimonious computational account of human normative behavior, this study challenges and extends foundational paradigms in both rational choice theory and cognitive science.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
normative appropriateness
social norms
rationality
cognitive architecture
predictive pattern completion
Innovation
Methods, ideas, or system contributions that make the work stand out.
normative appropriateness
predictive pattern completion
large language models
dual-process models
cultural rationality
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