Using Models to Persuade
研究了模型说服者如何通过提出解释数据的模型来影响接收者的信念，发现模型拟合度与说服效果之间存在权衡，竞争会促使模型更拟合数据，而针对不同接收者的私人信息更有效。
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.