In this thesis, we consider a rational inattentive agent who does not observe the environment perfectly and needs to acquire costly signal to make decisions. By observing agents actions, we formulate the inverse rational inattention framework to recover agents utility.
We formulate problems both in static and dynamic settings. In the static setting, we show the recovered utility is unique in equivalent classes. We propose efficient algorithms and show their convergence. We apply the model and algorithm to robo-advising problems of recovering investors utilities by observing their investment strategies in both mean-variance and target date investment settings. / 2024-05-20T00:00:00Z
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/44472 |
| Date | 20 May 2022 |
| Creators | Zhu, Zeyu |
| Contributors | Xing, Hao |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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