Chapter The Price of Uncertainty in Present-Biased Planning
Albers, Susanne
Chapter The Price of Uncertainty in Present-Biased Planning - Springer Nature 2017 - 1 electronic resource (15 p.)
Open Access
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.
Creative Commons
English
978-3-319-71924-5_23
10.1007/978-3-319-71924-5_23 doi
Computing & information technology
Alice and Bob approximation algorithms approximation algorithms behavioral economics behavioral economics Decision problem Graph theory Graphical model heterogeneous agents heterogeneous agents incentive design incentive design NP (complexity) penalty fees penalty fees Time complexity Upper and lower bounds variable present bias variable present bias
Chapter The Price of Uncertainty in Present-Biased Planning - Springer Nature 2017 - 1 electronic resource (15 p.)
Open Access
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.
Creative Commons
English
978-3-319-71924-5_23
10.1007/978-3-319-71924-5_23 doi
Computing & information technology
Alice and Bob approximation algorithms approximation algorithms behavioral economics behavioral economics Decision problem Graph theory Graphical model heterogeneous agents heterogeneous agents incentive design incentive design NP (complexity) penalty fees penalty fees Time complexity Upper and lower bounds variable present bias variable present bias