Mecha-nudges for Machines
AI 摘要
论文提出Mecha-nudges概念,优化AI代理的决策环境,并使用贝叶斯劝说框架进行形式化。
主要贡献
- 提出Mecha-nudges概念
- 结合贝叶斯劝说框架和V-usable信息
- 分析Etsy产品列表,发现ChatGPT发布后机器可用信息增加
方法论
结合贝叶斯劝说框架和V-usable信息,构建统一的度量标准,分析Etsy产品列表的变化。
原文摘要
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.