Predicting Your Own Effort

 
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Bacon, David F.
Kash, Ian
Sridharan, Manu
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Bacon, David F., Yiling Chen, Ian Kash, David C. Parkes, Malvika Rao, and Manu Sridharan. Forthcoming. Predicting your own effort. In Proceedings of the 11th International Conference on Autonomous and Multiagent Systems (AAMAS 2012), June 4–8, 2012, Valencia, Spain, eds. Conitzer, Winikoff, Padgham, and van der Hoek.
Abstract
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 affects 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, a 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 such that information is truthfully reported, best effort is exerted by the worker in completing tasks as quickly as possible, and collusion is not possible. We study the factors influencing when a worker will split a task into subtasks, each forming a separate prediction target.
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http://nrs.harvard.edu/urn-3:HUL.InstRepos:8191221

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