Mecha-nudges for Machines
This addresses the challenge of optimizing choice presentation for AI in shared human-machine environments, with incremental implications for e-commerce and AI interaction design.
The paper tackles the problem of how choices are presented to AI agents, introducing 'mecha-nudges' as subtle changes that influence machines without harming human decision-making, and finds that after ChatGPT's release, Etsy listings show significantly more machine-usable information, indicating systematic nudging.
Nudges are subtle changes to the way choices are presented to human decision-makers (e.g., opt-in vs. opt-out by default) that shift behavior without restricting options or changing incentives. As AI agents increasingly make decisions in the same environments as humans, the presentation of choices may be optimized for machines as well as people. We introduce mecha-nudges: changes to how choices are presented that systematically influence AI agents without degrading the decision environment for humans. To formalize mecha-nudges, we combine the Bayesian persuasion framework with V-usable information, a generalization of Shannon information that is observer-relative. This yields a common scale (bits of usable information) for comparing a wide range of interventions, contexts, and models. Applying our framework to product listings on Etsy -- a global marketplace for independent sellers -- we find that following ChatGPT's release, listings have significantly more machine-usable information about product selection, consistent with systematic mecha-nudging.