Interpreted and generated signals

Private information is typically modeled as signals. A joint probability distribution captures relationships between signals and between signals and relevant variables. In this paper, we define and contrast two types of signals: generated and interpreted. We demonstrate that even though the standard assumption of conditional independence is a reasonable benchmark assumption for generated signals, it imposes a specific, and unlikely structure on interpreted signals. We also show that independent interpreted signals are negatively correlated in their correctness, but generated signals can be independent. Our findings may limit the contexts in which many models of information aggregation and strategic choices in auctions, markets, and voting apply.