False Positives and Transparency
构建了一个成本信息获取的理论模型，评估实证研究中的透明度要求。发现接收者可能偏好隐藏某些维度，即使这些维度会导致偏差，因为发送者可通过可观测维度补偿，从而减轻偏差感知。
I develop a theoretical model of costly information acquisition in order to evaluate transparency requirements in empirical research. A sender chooses an experiment characterized by multiple dimensions, while a receiver observes the experiment’s outcome (though not necessarily all dimensions). I show that the receiver may prefer to keep dimensions hidden, even those contributing to bias, despite preferring more informative experiments. This can occur if the perception of bias is lessened when the sender compensates along a dimension that is observed. I elucidate how complementarity between dimensions underlies this result.