Chapter The Price of Uncertainty in Present-Biased Planning

• Main Author: Albers, Susanne (auth)
• Other Authors: Kraft, Dennis (auth)
• Format: Electronic Book Chapter
• Language: English
• Published: Springer Nature 2017
• Subjects: Computing & information technology; 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
• Online Access: OAPEN Library: download the publication; OAPEN Library: description of the publication

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.