Predicting your own effort

  • David F. Bacon ,
  • Yiling Chen ,
  • Ian Kash ,
  • David C. Parkes ,
  • Malvika Rao ,
  • Manu Sridharan

Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 (AAMAS) |

Publication

We consider a setting in which a worker and a manager may each have information about the likely completion time of a task, and the worker also aff ects the completion time by choosing a level of effort. The task itself may further be composed of a set of subtasks, and the worker can also decide how many of these subtasks to split out into an explicit prediction task. In addition, the worker can learn about the likely completion time of a task as work on subtasks completes. We characterize a family of scoring rules for the worker and manager that provide three properties: information is truthfully reported; best eff ort is exerted by the worker in completing tasks as quickly as possible; and collusion is not possible. We also study the factors in unflencing when a worker will split a task into subtasks, each forming a separate prediction target.