Optimal Revelation of Life-Changing Information

Information about the future may be instrumentally useful yet scary. For example, many patients shy away from precise genetic tests about their dispositions for severe diseases. They are afraid that a bad test result could render them desperate as a result of anticipatory feelings. We show that partially revealing tests are typically optimal when anticipatory utility interacts with an instrumental need for information. The same result emerges when patients rely on probability weighting. Optimal tests provide only two signals, which renders them easily implementable. While the good signal is typically precise, the bad one remains coarse. This way, patients have a substantial chance to learn that they are free of the genetic risk in question. Yet even if the test outcome is bad, they do not end in a situation without hope. This paper was accepted by Manel Baucells, decision analysis.

• 저널Management Science · 64(11) · 5250–5262
• 토픽Decision-Making and Behavioral Economics · General Decision Sciences
• DOI10.1287/mnsc.2017.2913
• 저자Nikolaus Schweizer, Nora Szech