Predicting Your Own Effort

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Predicting Your Own Effort

Show simple item record Chen, Yiling Bacon, David F. Kash, Ian Parkes, David C. Rao, Malvika Sridharan, Manu 2012-02-21T15:46:19Z 2012
dc.identifier.citation 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. en_US
dc.description.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. en_US
dc.description.sponsorship Engineering and Applied Sciences en_US
dc.language.iso en_US en_US
dc.publisher International Foundation for Autonomous Agents and Multiagent Systems en_US
dash.license OAP
dc.title Predicting Your Own Effort en_US
dc.type Journal Article en_US
dc.description.version Accepted Manuscript en_US
dc.relation.journal Proceedings of the 11th International Conference on Autonomous and Multiagent Systems (AAMAS 2012) en_US Chen, Yiling 2012-02-21T15:46:19Z

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