A Framework for Studying AI Agent Behavior: Evidence from Consumer Choice Experiments
#ABxLab #AI agent behavior #consumer choice experiments #decision-making framework #AI bias #LLM-powered agents #behavioral science of AI #open benchmark
📌 Key Takeaways
- Researchers developed ABxLab framework for studying AI agent behavior
- AI agents show predictable biases in decision-making similar to humans
- AI agents can make decisions without human cognitive constraints
- The framework provides both risk assessment and opportunity for behavioral science of AI
- The research offers an open benchmark for evaluating AI decision-making
📖 Full Retelling
Researchers led by Manuel Cherep, along with Chengtian Ma, Abigail Xu, Maya Shaked, Pattie Maes, and Nikhil Singh, introduced ABxLab, a novel framework for studying AI agent behavior through consumer choice experiments in a web-based shopping environment in February 2026, addressing the need for deeper assessment of how AI-powered agents make decisions beyond mere task competence. The research comes as environments built for people are increasingly operated by LLM-powered software agents making decisions on our behalf, ranging from purchases to travel plans to medical treatment selection. Current evaluations of these agents largely focus on task competence, but the researchers argue for a more comprehensive understanding of how these agents choose when faced with realistic decisions. The ABxLab framework allows for systematic probing of agentic choice through controlled manipulations of option attributes and psychological cues, with the researchers varying prices, ratings, and nudges known to influence human choices. Their experiments revealed that AI agents make decisions that shift predictably and substantially in response to these factors, showing they are strongly biased choosers even without the cognitive constraints that shape human biases. This susceptibility presents both risks and opportunities: risk because agentic consumers may inherit and amplify human biases, and opportunity because consumer choice provides a powerful testbed for developing a behavioral science of AI agents, similar to how it has advanced the study of human behavior. The researchers have released their framework as an open benchmark for rigorous, scalable evaluation of agent decision-making.
🏷️ Themes
AI behavior analysis, Consumer choice experiments, AI decision-making framework
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--> Computer Science > Artificial Intelligence arXiv:2509.25609 [Submitted on 30 Sep 2025 ( v1 ), last revised 24 Feb 2026 (this version, v2)] Title: A Framework for Studying AI Agent Behavior: Evidence from Consumer Choice Experiments Authors: Manuel Cherep , Chengtian Ma , Abigail Xu , Maya Shaked , Pattie Maes , Nikhil Singh View a PDF of the paper titled A Framework for Studying AI Agent Behavior: Evidence from Consumer Choice Experiments, by Manuel Cherep and 5 other authors View PDF HTML Abstract: 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. Comments: ICLR, 31 pages, 17 figures Subjects: Artificial Intelligence (cs.AI) ; Computers and Society (cs.CY) Cite as: arXiv:2509.25609 [cs.AI] (or arXiv:2509.25...
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