A benevolent sender communicates non-instrumental information over time to a Bayesian receiver who experiences gain-loss utility over changes in beliefs ("news utility"). We show how to inductively compute the optimal dynamic information structure for arbitrary news-utility functions. With diminishing sensitivity over the magnitude of news, unlike in piecewise-linear news-utility models, one-shot resolution of uncertainty is strictly suboptimal under commonly used functional forms.  We identify additional conditions that imply the sender optimally releases good news in small pieces but bad news in one clump. By contrast, information structures that deliver bad news gradually are never optimal. When the sender lacks commitment power, good-news messages confront a credibility problem given the receiver's diminishing sensitivity . Without loss aversion, the babbling equilibrium is essentially unique. More loss-averse receivers may enjoy higher equilibrium news-utility, contrary to the commitment case  .
A benevolent sender communicates non-instrumental information over time to a Bayesian receiver who experiences gain-loss utility over changes in beliefs ("news utility"). We show how to compute the optimal dynamic information structure for arbitrary news-utility functions. With diminishing sensitivity over the magnitude of news, one-shot resolution of uncertainty is strictly suboptimal under commonly used functional forms. Information structures that deliver bad news gradually are never optimal. We identify additional conditions that imply the sender optimally releases good news in small pieces but bad news in one clump. When the sender lacks commitment power, diminishing sensitivity leads to a credibility problem for good-news messages . Without loss aversion, the babbling equilibrium is essentially unique. More loss-averse receivers may enjoy higher equilibrium news-utility, contrary to the commitment case . We discuss applications to media competition and game shows .
D
inductively
D
unlike in piecewise-linear news-utility models,
A
Information structures that deliver bad news gradually are never optimal.
D
By contrast, information structures that deliver bad news gradually are never optimal.
R
good-news messages confront
diminishing sensitivity leads to
R
given the receiver's diminishing sensitivity
for good-news messages
A
. We discuss applications to media competition and game shows