Modeling and Analyzing Faults to Improve Election Process Robustness

Abstract

This paper presents an approach for continuous process improvement and illustrates its application to improving the robustness of election processes. In this approach, the Little-JIL process definition language is used to create a precise and detailed model of an election process. Given this process model and a potential undesirable event, or hazard, a fault tree is automatically derived. Fault tree analysis is then used to automatically identify combinations of failures that might allow the selected potential hazard to occur. Once these combinations have been identified, we iteratively improve the process model to increase the robustness of the election process against those combinations that seem the most likely to occur. We demonstrate this approach for the Yolo County election process. We focus our analysis on the ballot counting process and what happens when a discrepancy is found during the count. We identify two single points of failure (SPFs) in this process and propose process modifications that we then show remove these SPFs