Predicting your own effort

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, 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. Copyright © 2012, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.