Using Models to Persuade

We present a framework where "model persuaders" influence receivers' beliefs by proposing models that organize past data to make predictions. Receivers are assumed to find models more compelling when they better explain the data, fixing receivers' prior beliefs. Model persuaders face a trade-off: better-fitting models induce less movement in receivers' beliefs. Consequently, a receiver exposed to the true model can be most misled by persuasion when that model fits poorly, competition between persuaders tends to neutralize the data by pushing toward better-fitting models, and a persuader facing multiple receivers is more effective when he can send tailored, private messages.