We use data-provenance graphs to solve a problem within incentive engineering: motivating people to accept proposals generated in software. Across several provenance graphs created within the HAC-ER disaster-management system, we ran retrospectively a bespoke algorithm for subgraph matching in order to extract narrative information from the provenance data. The output of the algorithm comprised a series of text messages which, had they been generated at the time of the disaster trial, would have been transmissible with the specific intention of encouraging participants not to reject certain tasks. 

The algorithm found all expected subgraphs within the provenance graphs, on an any-time basis and in a time linearly proportional to the number of nodes. Our algorithm is extendable to other situations in which agents present tasks to humans. A link to the paper presented in the first half of today's talk is here: mebden.com/reports/Ebden_etal_AAMAS2015.pdf. This study connects with later work on the development of a mobile application that assists data users in their decision-making process: www.datatrie.com/advisor

This webinar is categorized under the Data Science and Engineering Analytics technical discipline.

All content contained within this webinar is copyrighted by Mark Ebden and its use and/or reproduction outside the portal requires express permission from Mark Ebden.

SPE Webinars are FREE to members courtesy of the