Procrastination with variable present bias
| Autor: | Nicole Immorlica, Emmanouil Pountourakis, Nick Gravin, Brendan Lucier |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
Discounting Mathematical optimization media_common.quotation_subject Procrastination Graph theory 02 engineering and technology Task (project management) Variable (computer science) Computer Science - Computer Science and Game Theory 020204 information systems Path (graph theory) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Dynamic inconsistency Constant (mathematics) Mathematics media_common Computer Science and Game Theory (cs.GT) |
| Zdroj: | EC |
| Popis: | 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 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. 19 pages, 2 figures. To appear in the 17th ACM Conference on Economics and Computation (EC 2016) |
| Databáze: | OpenAIRE |
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