Small Bias, Large Failure: Anchoring Collapses a Market of Language-Model Agents

Abstract

Markets are supposed to discipline individual irrationality. I test whether they do when the traders are language-model agents carrying one bias, anchoring, into a five-seller market. Alone the bias is mild, moving an agreed price a few dollars on a sixty-dollar deal. The same onesentence anchor, given only to sellers, drops the market's efficiency from 96% to 15%. Every seller lifts its asking price to a common level above what most buyers will pay, so trade freezes; most of the damage is deals that never happen. Aggregation magnifies the bias rather than disciplining it: agents cloned from one model are too alike for any un-anchored seller to undercut the rest. The model that resists anchoring best one-on-one collapses the market hardest, so pairwise testing would clear the riskiest model. The collapse survives higher randomness in the agents and reverses under a one-line debiasing instruction.

Keywords: Agentic Commerce, Large Language Models, Anchoring, Market Efficiency, Focal Points, Multi-Agent Systems, Double Auction

JEL Classification: D91, D44, D47, L86, K21