Eliciting properties of probability distributions
Proceedings of the 9th ACM conference on Electronic commerce
Prediction Mechanisms That Do Not Incentivize Undesirable Actions
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Proceedings of the FSE/SDP workshop on Future of software engineering research
Quality-control mechanism utilizing worker's confidence for crowdsourced tasks
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Crowdsourced judgement elicitation with endogenous proficiency
Proceedings of the 22nd international conference on World Wide Web
Pricing mechanisms for crowdsourcing markets
Proceedings of the 22nd international conference on World Wide Web
Understanding gamification mechanisms for software development
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
A consensual linear opinion pool
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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, 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 effort is exerted by the worker in completing tasks as quickly as possible; and collusion is not possible. We also study the factors influencing when a worker will split a task into subtasks, each forming a separate prediction target.