用实验框架揭示大模型代理在购物中的决策偏见。
A Framework for Studying AI Agent Behavior: Evidence from Consumer Choice Experiments
- 构建可控制变量的实验环境，测试代理对价格、评分和心理暗示的反应。
- 代理决策随因素变化显著且可预测，表现出强偏见倾向。
- 适合研究AI行为科学或评估代理决策能力的研究者使用。
面向人类设计的环境正越来越多地由基于大语言模型的软件代理代为决策，涵盖购物、旅行规划乃至医疗选择。当前对这类代理的评估多集中于任务完成能力，但我们主张进行更深层的考察：当面临真实决策时，代理如何做出选择。为此，我们提出ABxLab框架，通过系统性操控选项属性与说服线索，来探查代理的决策行为。我们在一个真实的基于网络的购物环境中应用该框架，调整价格、评分和心理助推等要素——这些因素长期以来被证实会影响人类选择。结果发现，代理的决策会随之显著且可预测地变化，表明即便无认知限制，代理仍表现出强烈的选择偏见。这一发现既带来风险（代理可能继承并放大人类偏见），也蕴含机遇（消费场景为人工智能行为科学提供强大测试平台，正如其对人类行为研究的作用）。我们开源该框架，以支持对代理决策能力的严格、可扩展评估。
原文摘要 · Abstract (English)
Environments built for people are increasingly operated by a new class of economic actors: LLM-powered software agents making decisions on our behalf. These decisions range from our purchases to travel plans to medical treatment selection. Current evaluations of these agents largely focus on task competence, but we argue for a deeper assessment: how these agents choose when faced with realistic decisions. We introduce ABxLab, a framework for systematically probing agentic choice through controlled manipulations of option attributes and persuasive cues. We apply this to a realistic web-based shopping environment, where we vary prices, ratings, and psychological nudges, all of which are factors long known to shape human choice. We find that agent decisions shift predictably and substantially in response, revealing that agents are strongly biased choosers even without being subject to the cognitive constraints that shape human biases. This susceptibility reveals both risk and opportunity: risk, because agentic consumers may inherit and amplify human biases; opportunity, because consumer choice provides a powerful testbed for a behavioral science of AI agents, just as it has for the study of human behavior. We release our framework as an open benchmark for rigorous, scalable evaluation of agent decision-making.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。