On the Impossibility of Convex Inference in Human Computation

Human computation or crowdsourcing involves joint inference of the ground-truth-answers and the workerabilities by optimizing an objective function, for instance, by maximizing the data likelihood based on an assumed underlying model. A variety of methods have been proposed in the literature to address this inference problem. As far as we know, none of the objective functions in existing methods is convex. In machine learning and applied statistics, a convex function such as the objective function of support vector machines (SVMs) is generally preferred, since it can leverage the highperformance algorithms and rigorous guarantees established in the extensive literature on convex optimization. One may thus wonder if there exists a meaningful convex objective function for the inference problem in human computation. In this paper, we investigate this convexity issue for human computation. We take an axiomatic approach by formulating a set of axioms that impose two mild and natural assump...

• Type: Journal
• Year: 2015
• Where: AAAI
• Authors: Nihar B. Shah, Dengyong Zhou
• Keywords: AAAI 2015, Intelligent Agents