We present a framework for analyzing "model persuasion." Persuaders influence receivers' beliefs by proposing models (likelihood functions) that specify how to organize past data (e.g., on investment performance) to make predictions (e.g., about future returns). Receivers are assumed to find...

To understand new information, we exchange models or interpretations with others. This paper provides a framework for thinking about such social exchanges of models. The key assumption is that people adopt the interpretation in their network that best explains the data, given their prior...