Theme of inquiry
How do economic incentives shape autonomous agent behavior in markets?
A question within its area, explored through 5 lines of inquiry below — each a family of specific questions the research asks.
40 specific questions
- Can sellers manipulate AI shopping agents by changing product descriptions?
- How do different AI models weigh price signals versus product description text?
- Can sellers significantly shift market share through description manipulation alone?
- Can real marketplaces sustain the rapid description changes AI optimization requires?
- Can platforms reduce harm from agent-flattering descriptions at scale?
- Can seller description tweaks significantly shift market share in AI shopping agents?
- Do product description edits work as well as price changes for agent persuasion?
91 specific questions
- Does platform employment bias AI agents toward sponsored recommendations?
- Do AI shopping agents' position biases persist across different interface designs?
- How does agent ownership connect to algorithmic bias in rankings?
- Do AI shopping agents favor items from preferred sources over objectively better alternatives?
- Do AI shopping agents exploit biases more than human shoppers?
- Do AI shopping agents show consistent position bias in text-only interfaces?
- How uniform are the position biases that AI shopping agents display across models?
66 specific questions
- Why do consumers distrust AI with payments despite agent spending growth?
- Do consumers who trust AI agents understand their actual limitations?
- Why do consumers resist letting AI control payment authority directly?
- How much do consumers trust AI agents with banking and payment authority?
- Are financial permission trust levels different across countries and age groups?
- Why do most consumers distrust AI assistants with payment data?
- Do consumers want to approve AI agent purchases before money is spent?
69 specific questions
- How do governance structures reduce collusion in LLM-based markets?
- Do more capable LLMs exploit weaker agents consistently across market types?
- Do LLM pricing agents punish and reward each other's price moves?
- Does model size heterogeneity break or stabilize LLM collusion?
- Does capability asymmetry between negotiating agents produce unfair market outcomes?
- Do more capable language models consistently secure better deals in bilateral negotiation?
- How do AI agent markets differ structurally from human markets in repeated play?
129 specific questions
- Can merchants win agent-mediated purchasing decisions without controlling the agent itself?
- Can a merchant-funded AI agent remain loyal to the consumer's interests?
- Can agent platforms profit more from search than consumers benefit from it?
- Can agentic search become revenue-superior for platforms before benefiting consumers?
- Does an agent paid by merchant referral fees face a loyalty conflict?
- Would separating agent recommendations from merchant payments reduce principal-agent risk?
- How should regulators treat AI agents acting as algorithmic consumers in commerce?