Pedro Nascimento de Lima is an assistant policy researcher at RAND and a Ph.D. candidate at the Pardee RAND Graduate School. He has a B.S. and an M.S. in production engineering from UNISINOS University in Brazil. His primary research interests are the policy-relevant applications of Decision Making Under Deep Uncertainty (DMDU) methods.
Prior to joining Pardee RAND, he was a lecturer at UNISINOS, where he taught simulation modeling and other quantitative courses. Both his undergraduate and master’s dissertations, focusing on wicked problems and decision under deep uncertainty, have received the Best Brazilian Dissertation in Production Engineering Prize from ABEPRO. As a member of the GMAP|UNISINOS Research Group, he developed a Monte Carlo simulation R package and contributed to several applied research and consulting projects for government agencies and private clients. He is currently the communications and outreach chair of the RAND-affiliated Society for Decision Making Under Deep Uncertainty.
Vardavas, Raffaele, Aaron Strong, Jennifer Bouey, Jonathan William Welburn, Pedro Nascimento de Lima, Lawrence Baker, Keren Zhu, Michelle Priest, Lynn Hu, and Jeanne S. Ringel, The Health and Economic Impacts of Nonpharmaceutical Interventions to Address COVID-19: A Decision Support Tool for State and Local Policymakers, RAND Corporation (TL-A173-1), 2020
Dresch A., Veit D.R., Lima P.N., Lacerda D.P., Collatto D.C., "Inducing Brazilian manufacturing SMEs productivity with Lean tools," International Journal of Productivity and Performance Management, 2019
Veit D.R., Lacerda D.P., Morandi M.I.W.M., Dresch A., Rodrigues L.H., de Lima P.N. "The Impacts of Additive Manufacturing on Production Systems," in Mula J., Barbastefano R., Díaz-Madroñero M., Poler R., New Global Perspectives on Industrial Engineering and Management, Springer, 2018
Lima, P.N., Dresch, A., Lacerda, D.P., "Do socio-economic contextual factors influence SMEs' service quality? A cross-sector and cross-city SERVPERF analysis," International Journal of Business Performance Management, 20(3), 2019