Research

Research Overview

My research is currently focused on human reliability analysis (HRA) with an application to nuclear power operations inside the main control room. My work is on the conceptualization, modeling and quantification of dependency in HRA, which is how causal factors and other variables are causally related to each other. The current state-of-the-practice in HRA is to view dependency in an "error-begets-error" paradigm, where human errors are assumed to increase the likelihood of subsequent errors, with the increase in probability being tabulated from a checklist of assumed dependency drivers (e.g., closeness in time of the tasks, crew makeup, presence of additional cues, etc.). However, there is no explicit inclusion of causal relationships as the driver of dependency - the factors used to determine dependency are essentially correlational/coincidental commonalities between the two tasks under consideration. Further, the mathematics that determine the increase in error probability are not grounded in any experiments, data or literature, and applying dependency analysis to HRA can actually "drown out" previous analyses. 

My research is focused on correcting these issues with dependency analysis. I take a causal view of dependency - that the relationships of importance in HRA are necessarily due to causal connections (otherwise, we are changing results based on coincidences). My early research reviewed the current state-of-the-practice and state-of-the-art in dependency research for HRA, which found among other things that dependency was never adequately defined as a concept. I further found that almost every dependency method in use today can trace aspects back to a single genesis methodology laid down in the Technique for Human Error Rate Prediction (THERP) in the 1970s. My research is improving dependency analysis by: 

1. Defining dependency robustly as a causal relationship between any two HRA variables that changes the probabilities involved

2. Developing a set of causal relationship archetypes (idioms) that describe the possible relationships between HRA variables

3. Building a Bayesian Network-based architecture to model and quantify dependency 

Peer-reviewed Journal Papers

J2: Paglioni, V. P., & Groth, K. M. Dependency Idioms for Quantitative Human Reliability Analysis. Nuclear Science and Engineering (under review).

J1: Paglioni, V. P., & Groth, K. M. (2022). Dependency definitions for quantitative human reliability analysis. Reliability Engineering & System Safety, 220. https://doi.org/https://doi.org/10.1016/j.ress.2021.108274

Refereed Conference Papers

C6: Paglioni, V. P., Levine, C. S., Al-Douri, A. & Groth, K. M. (2023). "Why Do Human-Machine Teams Fail: Investigating Failure Mechanisms in Human Reliability Analysis." 2023 International Topical Meeting on Probabilistic Safety Assessment and Analysis (PSA 2023). Knoxville, TN.

C5: Paglioni, V. P., & Groth, K. M. (2023). "Bridging the Data-Model Gap for HRA: Creating Bayesian Networks from HRA Data." 2023 International Topical Meeting on Probabilistic Safety Assessment and Analysis (PSA 2023). Knoxville, TN.

C4:  Paglioni, V. P., Mortenson, T., & Groth, K. M. (2022). "The human failure event: what is it and what should it be?" 2022 Probabilistic Safety Assessment and Management Conference (PSAM16). Honolulu, HI.

C3: Ruiz-Tagle, A., Paglioni, V. P., Lopez-Droguett, E., & Groth, K. M. (2021). "A Framework to Extrapolate and Evaluate Human Reliability Causal Models from Event Report Narratives." 2021 International Topical Meeting on Probabilistic Safety Assessment and Analysis (PSA 2021). Columbus, OH. 

C2: Paglioni, V. P., & Groth, K. M. (2021). "Defining Dependency in HRA." 2021 International Topical Meeting on Probabilistic Safety Assessment and Analysis (PSA 2021). Columbus, OH.

C1: Paglioni, V. P., & Groth, K. M. (2020). "Unified Definitions for Dependency in Quantitative Human Reliability Analysis." Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference. Venice, Italy.

Conference, Workshop, and Invited Presentations

P4: Vincent P. Paglioni, Camille S. Levine, and Katrina M. Groth, UMD Systems Risk and Reliability Analysis (SyRRA) Lab: HRA Research - Improving the Foundational Knowledge of Dependency in HRA, Presented to Sandia National Laboratory (invited), Albuquerque NM, March 23, 2022.

P3: Katrina M. Groth and Vincent P. Paglioni, Using Bayesian Networks in Human Reliability Analysis, Presented to Sandia National Laboratory (invited), Virtual, November 5, 2021.

P2: Vincent P. Paglioni, and Katrina M. Groth, Temporal Behaviors of Dependency Relationships in Human Reliability Analysis, Presented at the Annual Meeting of the Society for Risk Analysis, Virtual, December 2020.

P1: Vincent P. Paglioni and Katrina M. Groth, Can HRA Data Address HFE Dependency?, Presented at the NRC HRA Data Workshop, Virtual, March 2020.

Non-Technical Articles

NT1: Paglioni, V. (2015, November). The Ethics of Intelligent Machines. Investments & Wealth Monitor, 50–52. https://investmentsandwealth.org/getattachment/f3614756-1e1d-49c7-a201-29dbc22d8fbf/IWM15NovDec-EthicsIntelligentMachines.pdf

Working Articles

W3: Paglioni, V. P., & Groth, K. M. Perspectives on Improving the HRA Modeling and Data Lifecycle. In preparation. 

W2: Paglioni, V. P., & Groth, K. M. Creating Formative Dependency Bayesian Network Models Using the HRA Dependency Idioms and HRA Data, Part II: Model Quantification. In preparation for publication in: Reliability Engineering & System Safety. 

W1: Paglioni, V. P., & Groth, K. M. Creating Formative Dependency Bayesian Network Models Using the HRA Dependency Idioms and HRA Data, Part I: Model Construction. In preparation for publication in: Reliability Engineering & System Safety.