AI 中文总结
该研究聚焦AI与人类决策环境的交互，通过几何论证分析复合实验中AI最优训练的特性，指出最大化AI预测无条件准确率非最优，并探讨异质性用户或垄断训练者的影响。
AI 中文摘要
AI进行预测，人类利用其预测做出决策，这些预测会与人类验证分析、对其他统计模型的查询等相结合。因此，AI的经济价值取决于其与周边决策环境的交互方式。我们将AI的价值描述为这种“复合实验”的一部分，其中AI对世界状态做出粗略预测，通过几何论证说明这对最优模型训练的意义，解释为何最优训练在经济变量中可能不连续，并研究异质性用户或垄断模型训练者如何影响这些结果。特别地，最大化AI预测的无条件准确率通常并非最优选择。
英文摘要
AI predicts; humans use its predictions to make decisions. These predictions are combined with human verification and analysis, queries to other statistical models, and so on. The economic value of an AI, therefore, depends on how it interacts with the surrounding decision environment. We describe the value of AI as part of this ``composite experiment'' where AI makes a coarse prediction of the state of the world, show what this means for optimal model training via a geometric argument, explain why optimal training can be discontinuous in economic variables, and study how heterogeneous users or monopoly model trainers affect these results. In particular, maximizing the unconditional accuracy of AI predictions is generally suboptimal.
