Large language models (LLMs) are increasingly deployed as autonomous agents that make consumption decisions on behalf of users. This shift raises fundamental questions for consumer theory, which has traditionally modeled humans as the primary decision-makers. In this paper, we introduce LLM Consumer Behavior Theory, a new field of study concerned with analyzing consumer behavior in agentic markets. Drawing on classical and behavioral economics alongside recent advances in Natural Language Processing, we formalize how human preferences are reflected and acted upon by LLM-based agents, and how agent-level decisions aggregate into market demand. We unify previously fragmented literature on LLM decision-making, human behavior simulation, and preference elicitation under a common economic lens, highlighting where assumptions, such as rationality and heterogeneity, may fail in agentic markets. Rather than providing empirical validation, this paper outlines the scope of LLM consumer behavior and identifies open research questions related to alignment, preference representation, and market dynamics.

翻译：大型语言模型（LLM）正越来越多地被部署为自主代理，代表用户做出消费决策。这一转变对消费者理论提出了根本性问题，因为传统上该理论将人类视为主要决策者。在本文中，我们提出LLM消费者行为理论，这是一个关注分析代理市场中消费者行为的新研究领域。借鉴古典与行为经济学以及自然语言处理领域的最新进展，我们形式化了人类偏好如何被基于LLM的代理反映并执行，以及代理层面的决策如何聚合为市场需求。我们将以往关于LLM决策、人类行为模拟和偏好诱导的碎片化文献统一到共同的经济视角下，强调在代理市场中理性、异质性等假设可能失效的环节。本文并未提供实证验证，而是概述了LLM消费者行为的范畴，并指出与对齐、偏好表征及市场动态相关的未决研究问题。