TY - JOUR AU - Halpern, Daniel AU - Kehne, Gregory AU - Peters, Dominik AU - Procaccia, Ariel D. AU - Shah, Nisarg AU - Skowron, Piotr PY - 2021/05/18 Y2 - 2024/03/28 TI - Aggregating Binary Judgments Ranked by Accuracy JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 6 SE - AAAI Technical Track on Game Theory and Economic Paradigms DO - 10.1609/aaai.v35i6.16687 UR - https://ojs.aaai.org/index.php/AAAI/article/view/16687 SP - 5456-5463 AB - We revisit the fundamental problem of predicting a binary ground truth based on independent binary judgments provided by experts. When the accuracy levels of the experts are known, the problem can be solved easily through maximum likelihood estimation. We consider, however, a setting in which we are given only a ranking of the experts by their accuracy. Motivated by the worst-case approach to handle the missing information, we consider three objective functions and design efficient algorithms for optimizing them. In particular, the recently popular distortion objective leads to an intuitive new rule. We show that our algorithms perform well empirically using real and synthetic data in collaborative filtering and political prediction domains. ER -