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Procrastination with Variable Present Bias
Conference proceeding   Open access

Procrastination with Variable Present Bias

Nick Gravin, Nicole Immorlica, Brendan Lucier, Emmanouil Pountourakis and ACM
Proceedings of the 2016 ACM Conference on Economics and Computation, pp 361-361
21 Jul 2016
DOI: https://doi.org/10.1145/2940716.2940774
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://arxiv.org/pdf/1606.03062View
Submitted Open arXiv.org - Non-exclusive license to distribute

Abstract

Mathematics of computing -- Discrete mathematics -- Graph theory Theory of computation -- Theory and algorithms for application domains -- Algorithmic game theory and mechanism design Theory of computation -- Theory and algorithms for application domains -- Algorithmic game theory and mechanism design -- Algorithmic game theory Theory of computation -- Theory and algorithms for application domains -- Algorithmic game theory and mechanism design -- Algorithmic mechanism design Theory of computation -- Theory and algorithms for application domains -- Algorithmic game theory and mechanism design -- Solution concepts in game theory
Individuals working towards a goal often exhibit time inconsistent behavior, making plans and then failing to follow through. One well-known model of such behavioral anomalies is present-bias discounting: individuals over-weight present costs by a bias factor. This model explains many time-inconsistent behaviors, but can make stark predictions in many settings: individuals either follow the most efficient plan for reaching their goal or procrastinate indefinitely. We propose a modification in which the present-bias parameter can vary over time, drawn independently each step from a fixed distribution. Following Kleinberg and Oren (2014), we use a weighted {\it task graph} to model task planning, and measure the cost of procrastination as the relative expected cost of the chosen path versus the optimal path. We use a novel connection to optimal pricing theory to describe the structure of the worst-case task graph for any present-bias distribution. We then leverage this structure to derive conditions on the bias distribution under which the worst-case ratio is exponential (in time) or constant. We also examine conditions on the task graph that lead to improved procrastination ratios: graphs with a uniformly bounded distance to the goal, and graphs in which the distance to the goal monotonically decreases on any path.

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18 citations in Web of Science

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Industry collaboration
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Web of Science research areas
Computer Science, Interdisciplinary Applications
Economics
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