The "Fake News" Effect: Experimental Design

The design gives subjects information such that a Bayesian would not infer anything, but motivated beliefs are evoked. Directional inference is attributed to motivated reasoning.

There are three steps:

Step 1: Elicit Subjects' Median Beliefs

The subject states a belief m about a factual question: "How did the murder rate change under Obama?" I elicit a median belief m, such that she believes P(answer > m) = P(answer < m) = 1/2.

Step 2: Give Subjects Personalized News

The subject receives one customized message from an unknown source: Either True News or Fake News. She doesn't know the source, and has to infer it from the message.

  • True News: Always tells the truth.

  • Fake News: Always lies.

The message either says:

G: "The answer is greater than [subject's median belief] m." or

L: "The answer is less than [subject's median belief] m."

Step 3: Elicit Subjects' News Veracity Beliefs

The subject is asked to predict P(True News | message). Because of the median elicitation, there's nothing to infer.

  • Prior (P(True)) is fixed.

  • Likelihood (1/2) is same for both G and L.

    • Median elicited, so subjective likelihood P(G | True) = P(G | Fake)

  • But motivated beliefs are different for G and L.

If subjects are more likely to trust a news source that reports G as compared to a source that reports L, this is evidence for motivated reasoning towards G.