Peekaboom: a game for locating objects in images
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Financial incentives and the "performance of crowds"
Proceedings of the ACM SIGKDD Workshop on Human Computation
Soylent: a word processor with a crowd inside
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Reflecting on the DARPA Red Balloon Challenge
Communications of the ACM
Local ground: a paper-based toolkit for documenting local geo-spatial knowledge
Proceedings of the First ACM Symposium on Computing for Development
Human computation: a survey and taxonomy of a growing field
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Human computation tasks with global constraints
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
CommunitySourcing: engaging local crowds to perform expert work via physical kiosks
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Adjunct proceedings of the 25th annual ACM symposium on User interface software and technology
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In this paper, we present a software tool to help emergency planners at Hampshire County Council in the UK to create maps for high-fidelity crowd simulations that require evacuation routes from buildings to roads. The main feature of the system is a crowdsourcing mechanism that breaks down the problem of creating evacuation routes into microtasks that a contributor to the platform can execute in less than a minute. As part of the mechanism we developed a concensus-based trust mechanism that filters out incorrect contributions and ensures that the individual tasks are complete and correct. To drive people to contribute to the platform, we experimented with different incentive mechanisms and applied these over different time scales, the aim being to evaluate what incentives work with different types of crowds, including anonymous contributors from Amazon Mechanical Turk. The results of the 'in the wild' deployment of the system show that the system is effective at engaging contributors to perform tasks correctly and that users respond to incentives in different ways. More specifically, we show that purely social motives are not good enough to attract a large number of contributors and that contributors are averse to the uncertainty in winning rewards. When taken altogether, our results suggest that a combination of incentives may be the best approach to harnessing the maximum number of resources to get socially valuable tasks (such for planning applications) performed on a large scale.