document: Max algorithms in crowdsourcing environments Our work investigates the problem of retrieving the maximum item from a set in crowdsourcing environments . We first develop parameterized families of max algorithms , that take as input a set of items and output an item from the set that is believed to be the maximum . Such max algorithms could , for instance , select the best Facebook profile that matches a given person or the best photo that describes a given restaurant . Then , we propose strategies that select appropriate max algorithm parameters . Our framework supports various human error and cost models and we consider many of them for our experiments . We evaluate under many metrics , both analytically and via simulations , the tradeoff between three quantities : ( 1 ) quality , ( 2 ) monetary cost , and ( 3 ) execution time . Also , we provide insights on the effectiveness of the strategies in selecting appropriate max algorithm parameters and guidelines for choosing max algorithms and strategies for each application .
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