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Expectation Formation Under Uninformative Signals
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AI
DOI：10.1287/mnsc.2023.03367.png)
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Abstract
En 中文
How do individuals process nondiagnostic information? According to Bayes' theorem, signals that do not carry relevant information are treated as if no signal occurred. This paper provides evidence that individuals update their expectations even after observing uninformative signals. Importantly, the direction in which they update depends on the valence of the signal. Prior beliefs become more optimistic after desirable uninformative signals and more pessimistic after undesirable uninformative signals. Our results provide novel insights why individuals form and entertain false beliefs in environments in which potentially new information is easily accessible but costly to verify (e.g., online media).
Keywords:
belief formation
information processing
uninformative signals
Bayes' theorem