Playing the Odds: Decision Support and Risk Assessment in an Elastic Framework

摘要

Crowd sourcing is a paradigm where activities are outsourced to human actors (i.e. The crowd) with the aim of discovering and evaluating solutions. This paradigm can also be extended to develop a collective intelligence of large-scale crowd communities that when combined with traditional computing resources can derive solutions that neither humans nor machines can solve alone. Such hybrid systems, or elastic systems, could involve large numbers of people with varying expertise, skills, interests, and incentives and varied computing resources. Elastic frameworks have been proposed to improve the performance of these systems to make them more efficient, robust, and scalable. To meet these requirements, we investigate a novel approach that provides decision support and risk assessment in an elastic framework. In our approach, we infer a probabilistic framework of a hybrid system and use probabilistic odds as a quantitative measure of the capability of human and computing resources to execute a task. As new evidence becomes available, we propagate updated odds throughout our framework to update our prior belief and risks for computing elements. In this approach, the elastic framework can exploit this information in such a way that self-learning is coupled with the ability to extract actionable insights that optimize judgment under uncertainty.