Mecha-nudges for Machines

TL;DR

This paper introduces the concept of mecha-nudging, combining economic and computer science frameworks to quantify how environments influence AI decision-making, with empirical evidence from Etsy listings showing significant shifts post-ChatGPT release.

Contribution

It develops a novel framework for measuring environment-induced influence on AI agents and provides large-scale empirical evidence of mecha-nudging in online marketplaces.

Findings

Listings contain 0.143 more bits of machine-usable info after ChatGPT's release.

The shift is consistent across different prompts and models.

No significant change in human-usable information was observed.

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.