TY  - GEN
AU  - Albers,Susanne
AU  - Kraft,Dennis
TI  - Chapter The Price of Uncertainty in Present-Biased Planning
SN  - 978-3-319-71924-5_23
PY  - 2017///
PB  - Springer Nature
KW  - Computing & information technology
KW  - bicssc
KW  - behavioral economics
KW  - incentive design
KW  - heterogeneous agents
KW  - approximation algorithms
KW  - variable present bias
KW  - penalty fees
KW  - Alice and Bob
KW  - Decision problem
KW  - Graph theory
KW  - Graphical model
KW  - NP (complexity)
KW  - Time complexity
KW  - Upper and lower bounds
N1  - Open Access
N2  - 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
UR  - https://library.oapen.org/bitstream/20.500.12657/30615/1/644832.pdf
UR  - https://library.oapen.org/bitstream/20.500.12657/30615/1/644832.pdf
UR  - https://library.oapen.org/bitstream/20.500.12657/30615/1/644832.pdf
UR  - https://directory.doabooks.org/handle/20.500.12854/31947
ER  -