REVIEWER
Certification Program for Compassionate Systems Master Practitioners:
Class of 2023-24
Peter S. Hovmand
Peter Hovmand | Ph.D., MSW
(they/he)

Pamela B. Davis MD PhD Professor of Medicine
Professor, Center for Community Health Integration, School of Medicine
Professor, Biomedical Engineering, Case School of Engineering
Professor (secondary appointment), Mandel School of Applied Social Sciences

Dr. Hovmand is an internationally recognized leader in developing and applying participatory methods in system dynamics and systems science in public health and medicine. His research focuses on focuses on advancing methods for understanding and preventing structural violence with a specific emphasis on advancing knowledge on multilevel feedback systems. Dr. Hovmand founded and led the Brown School’s Social System Design Lab at Washington University in St. Louis from 2009 to 2020, authored Community Based System Dynamics and led the creation of Scriptapedia as a knowledge common for group model building scripts and serves as an associate editor for System Dynamics Review.

Dr. Hovmand’s work has been supported by the National Science Foundation, National Institutes of Health, Lupina Foundation, SkipNV, US Department of Agriculture, St. Louis Federal Reserve Bank, Robert Wood Johnson Foundation, Substance Abuse and Mental Health Services Administration, Foundation for Food and Agriculture Research, Administration for Children & Families, Ohio Department of Mental Health, Economic and Social Research Council United Kingdom, Wellcome Trust, Centers for Disease Control and Prevention, Bill and Melinda Gates Foundation, YMCA, RegionWise, Save the Children UK, Wellesley Institute, Google (now Alphabet), and Government of Singapore.

Current research interest areas include closing the gap between engineering/science and lived experience to advance equity, social epistemology of participatory systems methods for advancing equity in policy and practice, sustaining common pool resources for community and public health, addressing bias in machine learning/artificial intelligence, and increasing and retaining diversity of STEM faculty with an emphasis on advancing the design sciences and regional equity.