The diversity of opinions is both cure and curse for the effective use of crowdsourced intelligence. To unify crowd­sourced intelligence for a well­informed decision, we propose an algorithmic approach for decision aggregation that accurately quantifies the reliability of information from multiple sources. The key idea is to model the propagation of reliability in decisions based on an ensemble of relevance graphs, where the optimization of both the reliability propagation and the graph ensemble are mutually reinforced. The propagated reliability is to aggregate intelligence from multiple sources and facilitate decision­making by leveraging various types of intercorrelations of information sources and the subjects. Meanwhile, the optimized graph ensemble can retain the relevance structures with respect to the crowdsourced intelligence. We evaluate our approach with large­scale data sets of stock markets, and find that our approach not only outperforms alternative methods, but also provides interesting insights into the reliability of the information.


Paper Number 1647; Track AI; Complete Paper



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