Chapter The Price of Uncertainty in Present-Biased Planning
The tendency to overestimate immediate utility is a common cognitive bias. As a result people behave inconsistently over time and fail to reach long-term goals. Behavioral economics tries to help affected individuals by implementing external incentives. However, designing robust incentives is oft...
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| פורמט: | Online |
| שפה: | אנגלית |
| יצא לאור: |
Springer Nature
2021
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| נושאים: | |
| גישה מקוונת: | 644832 |
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| _version_ | 1869517156258938880 |
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| author | Albers, Susanne Kraft, Dennis |
| author_browse | Albers, Susanne Kraft, Dennis |
| author_facet | Albers, Susanne Kraft, Dennis |
| author_sort | Albers, Susanne |
| collection | Directory of Open Access Books |
| description | The tendency to overestimate immediate utility is a common cognitive bias. As a result people behave inconsistently over time and fail
to reach long-term goals. Behavioral economics tries to help affected individuals
by implementing external incentives. However, designing robust
incentives is often difficult due to imperfect knowledge of the parameter
β ∈ (0, 1] quantifying a person’s present bias. Using the graphical model
of Kleinberg and Oren [8], we approach this problem from an algorithmic
perspective. Based on the assumption that the only information about
β is its membership in some set B ⊂ (0, 1], we distinguish between two
models of uncertainty: one in which β is fixed and one in which it varies
over time. As our main result we show that the conceptual loss of effi-
ciency incurred by incentives in the form of penalty fees is at most 2
in the former and 1 + max B/ min B in the latter model. We also give
asymptotically matching lower bounds and approximation algorithms. |
| format | Online |
| id | doab-20.500.12854ir-31947 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Springer Nature |
| publisherStr | Springer Nature |
| record_format | ojs |
| spelling | doab-20.500.12854ir-319472025-05-08T09:43:52Z Chapter The Price of Uncertainty in Present-Biased Planning Albers, Susanne Kraft, Dennis behavioral economics incentive design heterogeneous agents approximation algorithms variable present bias penalty fees behavioral economics incentive design heterogeneous agents approximation algorithms variable present bias penalty fees Alice and Bob Decision problem Graph theory Graphical model NP (complexity) Time complexity Upper and lower bounds The tendency to overestimate immediate utility is a common cognitive bias. As a result people behave inconsistently over time and fail to reach long-term goals. Behavioral economics tries to help affected individuals by implementing external incentives. However, designing robust incentives is often difficult due to imperfect knowledge of the parameter β ∈ (0, 1] quantifying a person’s present bias. Using the graphical model of Kleinberg and Oren [8], we approach this problem from an algorithmic perspective. Based on the assumption that the only information about β is its membership in some set B ⊂ (0, 1], we distinguish between two models of uncertainty: one in which β is fixed and one in which it varies over time. As our main result we show that the conceptual loss of effi- ciency incurred by incentives in the form of penalty fees is at most 2 in the former and 1 + max B/ min B in the latter model. We also give asymptotically matching lower bounds and approximation algorithms. 2021-02-10T12:58:18Z 2020-03-18 13:36:15 2020-04-01T13:03:04Z 2018-03-03 23:55 2020-03-18 13:36:15 2020-04-01T13:03:04Z 2018-02-01 23:55:55 2020-03-18 13:36:15 2020-04-01T13:03:04Z 2017 chapter 644832 OCN: 1076689890 http://library.oapen.org/handle/20.500.12657/30615 https://directory.doabooks.org/handle/20.500.12854/31947 eng open access image/jpeg image/jpeg image/jpeg image/jpeg image/jpeg Attribution 4.0 International Attribution 4.0 International Attribution 4.0 International Attribution 4.0 International Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/30615/1/644832.pdf https://library.oapen.org/bitstream/20.500.12657/30615/1/644832.pdf https://library.oapen.org/bitstream/20.500.12657/30615/1/644832.pdf https://library.oapen.org/bitstream/20.500.12657/30615/1/644832.pdf https://library.oapen.org/bitstream/20.500.12657/30615/1/644832.pdf Springer Nature 10.1007/978-3-319-71924-5_23 10.1007/978-3-319-71924-5_23 9fa3421d-f917-4153-b9ab-fc337c396b5a Web and Internet Economics H2020 European Research Council 178e65b9-dd53-4922-b85c-0aaa74fce079 European Research Council (ERC) EU collection 15 691672 H2020 open access |
| spellingShingle | behavioral economics incentive design heterogeneous agents approximation algorithms variable present bias penalty fees behavioral economics incentive design heterogeneous agents approximation algorithms variable present bias penalty fees Alice and Bob Decision problem Graph theory Graphical model NP (complexity) Time complexity Upper and lower bounds Albers, Susanne Kraft, Dennis Chapter The Price of Uncertainty in Present-Biased Planning |
| title | Chapter The Price of Uncertainty in Present-Biased Planning |
| title_full | Chapter The Price of Uncertainty in Present-Biased Planning |
| title_fullStr | Chapter The Price of Uncertainty in Present-Biased Planning |
| title_full_unstemmed | Chapter The Price of Uncertainty in Present-Biased Planning |
| title_short | Chapter The Price of Uncertainty in Present-Biased Planning |
| title_sort | chapter the price of uncertainty in present biased planning |
| topic | behavioral economics incentive design heterogeneous agents approximation algorithms variable present bias penalty fees behavioral economics incentive design heterogeneous agents approximation algorithms variable present bias penalty fees Alice and Bob Decision problem Graph theory Graphical model NP (complexity) Time complexity Upper and lower bounds |
| topic_facet | behavioral economics incentive design heterogeneous agents approximation algorithms variable present bias penalty fees behavioral economics incentive design heterogeneous agents approximation algorithms variable present bias penalty fees Alice and Bob Decision problem Graph theory Graphical model NP (complexity) Time complexity Upper and lower bounds |
| url | 644832 |
| work_keys_str_mv | AT alberssusanne chapterthepriceofuncertaintyinpresentbiasedplanning AT kraftdennis chapterthepriceofuncertaintyinpresentbiasedplanning |