Predicting Your Own Effort

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Predicting Your Own Effort

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Title: Predicting Your Own Effort
Author: Chen, Yiling; Bacon, David F.; Kash, Ian; Parkes, David C.; Rao, Malvika; Sridharan, Manu

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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.
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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.
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:8191221

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  • FAS Scholarly Articles [7078]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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