Chapter The Price of Uncertainty in Present-Biased Planning

• Format: eBook
• Language: English
• Published: Springer Nature 2017
• Subjects: Computing and Information Technology; Alice and Bob; approximation algorithms; behavioral economics; Decision problem; Graph theory; Graphical model; heterogeneous agents; incentive design; NP (complexity); penalty fees; thema EDItEUR::U Computing and Information Technology; Time complexity; Upper and lower bounds; variable present bias
• Online Access: Open Access: DOAB: description of the publication; Open Access: DOAB, download the publication
• Physical Description: 1 online resource (15 p.)
• ISBN: 978-3-319-71924-5_23
• Access: 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.