Chapter The Price of Uncertainty in Present-Biased Planning
Material type: ArticleLanguage: English Publication details: Springer Nature 2017Description: 1 electronic resource (15 p.)ISBN:- 978-3-319-71924-5_23
- Computing & information technology
- 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 bounds
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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.
H2020 European Research Council
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