Using Models to Persuade

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 models more compelling when they better explain the data, fixing receivers’ prior beliefs over states of the world. A key tradeoff facing persuaders is that models that better fit the data induce less movement in receivers’ beliefs. Model persuasion sometimes makes the receiver worse off than he would be in the absence of persuasion. Even when the receiver is exposed to the true model, the wrong model often wins because it better fits the past. The receiver is most misled by persuasion when there is a lot of data that is open to interpretation and exhibits randomness, as this gives the persuader “wiggle room” to highlight false patterns. With multiple persuaders, competition pushes towards models that provide overly good fits, which tend to neutralize the data by leading receivers to view it as unsurprising. With multiple receivers, a persuader is more effective when he can send tailored, private messages than a menu of public messages. We illustrate with examples from finance, business, politics, and law.