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oapen-20.500.12657-306152024-03-25T09:51:39Z 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 thema EDItEUR::U Computing and Information Technology 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. 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 2020-04-01T13:03:04Z 2017 chapter 644832 OCN: 1076689890 http://library.oapen.org/handle/20.500.12657/30615 eng application/pdf Attribution 4.0 International 644832.pdf Springer Nature Web and Internet Economics 10.1007/978-3-319-71924-5_23 10.1007/978-3-319-71924-5_23 6c6992af-b843-4f46-859c-f6e9998e40d5 22a6fc0d-505e-4eb6-a842-029d12d9280d 178e65b9-dd53-4922-b85c-0aaa74fce079 European Research Council (ERC) 15 1 691672 H2020 H2020 European Research Council H2020 Excellent Science - European Research Council open access
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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.
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