The Third Ambition: Artificial Intelligence and the Science of Human Behavior
W. Russell Neuman, Chad Coleman

TL;DR
This paper proposes using large language models as scientific tools to study human behavior, culture, and morality, leveraging their ability to encode social regularities from vast human-generated text.
Contribution
It introduces the concept of LLMs as instruments for social science research, connecting AI development with traditional methods and clarifying epistemic limits.
Findings
LLMs encode large-scale social regularities.
Alignment can reshape or obscure learned cultural patterns.
Methodologies like prompt experiments and synthetic sampling are effective at scale.
Abstract
Contemporary artificial intelligence research has been organized around two dominant ambitions: productivity, which treats AI systems as tools for accelerating work and economic output, and alignment, which focuses on ensuring that increasingly capable systems behave safely and in accordance with human values. This paper articulates and develops a third, emerging ambition: the use of large language models (LLMs) as scientific instruments for studying human behavior, culture, and moral reasoning. Trained on unprecedented volumes of human-produced text, LLMs encode large-scale regularities in how people argue, justify, narrate, and negotiate norms across social domains. We argue that these models can be understood as condensates of human symbolic behavior, compressed, generative representations that render patterns of collective discourse computationally accessible. The paper situates…
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Taxonomy
TopicsComputational and Text Analysis Methods · Language and cultural evolution · Social Power and Status Dynamics
