Keeping in the Dark with Hard Evidence

We present a dynamic learning setting in which the periodic data observed by a decision-maker is mediated by an agent. We study when, and to what extent, this mediation can distort the decision-maker’s long-run learning, even though the agent’s reports are restricted to consist of verifiable hard evidence and must adhere to certain standards. We first illustrate the extent and mechanisms of manipulation in specific economic settings. We then derive a general manipulation-proof law of large numbers: when it holds, the decision-maker’s learning is guaranteed in the long-run; when it fails, the scope for manipulation is essentially unrestricted.

(with Alexander Frug)

The paper: https://drive.google.com/file/d/1RnBmw7AgvTp4je-4KtFRArvcr3Mov5fU/view?usp=sharing
Daniel Bird's homepage: https://sites.google.com/view/danielbird