Shopping by algorithm: How agentic AI deploys human heuristics as a surrogate consumer
econ.GN, cs.AI, q-fin.EC
Submitted: 2026-09-23
Updated: 2026-10-13
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
The gist: Consumers increasingly delegate purchasing decisions to Large Language Models (LLMs) acting as surrogate consumers.
Terminology
Abstract
Consumers increasingly delegate purchasing decisions to Large Language Models (LLMs) acting as surrogate consumers. Using "Tool-Lab," an adaptation of information-board process tracing that places product attributes behind costly tool calls, we examine how marketing pricing cues (i.e., just-below pricing and promotional framing) influence AI shopping agents. Across eight commercially deployed LLMs from three providers, we trace pre-choice information acquisition. Under zero cost, pricing cues rarely mislead. Imposing acquisition costs under a vague goal prompt leads LLMs to omit diagnostic attributes required to compute unit price and choose suboptimal choices resembling human heuristics. Relative to a specific goal prompt that mainly preserves diagnostic search and choice optimality, a vague goal prompt under constraints creates a search-mediated vulnerability. This research demonstrates that marketing heuristics in delegated AI shopping are governed by storefront information architecture, not necessarily immutable LLM flaws.
Sources
- The Curious Case of Neural Text Degeneration
- The Bias is in the Details: An Assessment of Cognitive Bias in LLMs
- Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf
- Whom Do AI Agents Work For? Role Assignment Induces Sponsorship Bias in LLM Recommenders
- Every Token Counts: Exact Likert-Scale Distributions for Measuring LLM Attitudes and Biases
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