Abstract We consider three equilibrium concepts proposed in the literature for time‐inconsistent stopping problems, including mild equilibria (introduced in Huang and Nguyen‐Huu (2018)), weak equilibria (introduced in Christensen and Lindensjö (2018)), and strong equilibria (introduced in Bayraktar et al. (2021)). The discount function is assumed to be log subadditive and the underlying process is one‐dimensional diffusion. We first provide necessary and sufficient conditions for the characterization of weak equilibria. The smooth‐fit condition is obtained as a by‐product. Next, based on the characterization of weak equilibria, we show that an optimal mild equilibrium is also weak. Then we provide conditions under which a weak equilibrium is strong. We further show that an optimal mild equilibrium is also strong under a certain condition. Finally, we provide several examples including one showing a weak equilibrium may not be strong, and another one showing a strong equilibrium may not be optimal mild.
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Adversarial Regression with Doubly Non-negative Weighting Matrice
Le, Tam; Nguyen, Truyen; Yamada, Makoto; Blanchet, Jose, and Nguyen, Viet Anh. Adversarial Regression with Doubly Non-negative Weighting Matrices. Advances in Neural Information Processing Systems. M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan, editors. Vol. 34, (2021). pp. 16964--16976. https://proceedings.neurips.cc/paper/2021/file/8cfef17bee2b7a75a3ce09d40b497f6b-Paper.pdf
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- Award ID(s):
- 1915967
- PAR ID:
- 10344987
- Date Published:
- Journal Name:
- Advances in neural information processing systems
- Volume:
- 34
- Issue:
- 2021
- ISSN:
- 1049-5258
- Page Range / eLocation ID:
- 16964--16976
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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