用大模型模拟消费者行为，帮营销策略提前试错。
LLM-Based Multi-Agent System for Simulating and Analyzing Marketing and Consumer Behavior
- 让大模型驱动的智能体自主思考、互动和形成消费习惯。
- 在促销场景中发现传统方法无法捕捉的群体行为模式。
- 适合想低成本测试营销方案的市场人员或研究者。
在真实部署前，模拟消费者决策对设计和评估营销策略至关重要。然而，事后分析和基于规则的代理模型难以捕捉人类行为与社会互动的复杂性。我们提出一种基于大语言模型的多智能体仿真框架，能够建模消费者决策与社会动态。依托近期在沙盒环境中大语言模型模拟的进展，该框架使生成式智能体可在无预设规则的情况下进行交互、表达内部推理、形成习惯并做出购买决策。在价格折扣营销场景中，系统输出可操作的策略测试结果，并揭示了传统方法难以发现的涌现社会模式。该方法为营销人员提供了一种可扩展、低风险的预实施测试工具，减少了对耗时的事后评估依赖，降低了策略表现不佳的风险。
原文摘要 · Abstract (English)
Simulating consumer decision-making is vital for designing and evaluating marketing strategies before costly real-world deployment. However, post-event analyses and rule-based agent-based models (ABMs) struggle to capture the complexity of human behavior and social interaction. We introduce an LLM-powered multi-agent simulation framework that models consumer decisions and social dynamics. Building on recent advances in large language model simulation in a sandbox environment, our framework enables generative agents to interact, express internal reasoning, form habits, and make purchasing decisions without predefined rules. In a price-discount marketing scenario, the system delivers actionable strategy-testing outcomes and reveals emergent social patterns beyond the reach of conventional methods. This approach offers marketers a scalable, low-risk tool for pre-implementation testing, reducing reliance on time-intensive post-event evaluations and lowering the risk of underperforming campaigns.
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