Chapter The Price of Uncertainty in Present-Biased Planning
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Author(s)
Albers, Susanne
Kraft, Dennis
Collection
European Research Council (ERC)Language
EnglishAbstract
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.
Keywords
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 boundsPublisher
Springer NaturePublisher website
http://www.springernature.com/oabooksPublication date and place
2017Grantor
Classification
Computing & information technology