Mecha-nudges for Machines

[Submitted on 24 Mar 2026 (v1), last revised 4 Jul 2026 (this version, v3)]

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Abstract:AI agents are becoming active decision-makers on the Internet. As they make decisions in the same environments as humans, the environments themselves can change to influence them. We call this $\textit{mecha-nudging}$: changes to how choices are presented that systematically influence AI agents without materially degrading the decision environment for humans. To measure this phenomenon, we combine two frameworks — Bayesian persuasion from economics and $\mathcal{V}$-usable information from computer science — to get a common unit (bits) for quantifying how environments change across a wide range of interventions, contexts, and models. We apply this framework to over six million Etsy listings and find that, after ChatGPT’s release, listings contain significantly more machine-usable information for predicting agent curation decisions, increasing by 0.143 bits out of a maximum possible increase of 0.355. This shift is robust across prompts, token choices, labeling models, and fine-tuning architectures; absent in a regulated-text placebo; and far larger than the effect of generic LLM rewriting. In contrast, a human study finds little to no change in human-usable information. Our results provide the first large-scale evidence that systematic mecha-nudging is already occurring in the wild, but going unnoticed.

Submission history

From: Giulio Frey [view email]
[v1] Tue, 24 Mar 2026 17:02:21 UTC (248 KB)
[v2] Thu, 14 May 2026 22:59:58 UTC (368 KB)
[v3] Sat, 4 Jul 2026 19:38:08 UTC (415 KB)