Moira Zellner
Professor of Public Policy and Urban Affairs; Director of Participatory Modeling and Data Science; Director, MS in Urban Informatics Program; Co-Director of NULab for Digital Humanities and Computational Social Science
Moira Zellner’s academic background lies at the intersection of Urban and Regional Planning, Environmental Science, and Complexity. She has served as Principal Investigator and Co-Investigator in interdisciplinary projects examining how specific policy, technological and behavioral factors influence the emergence and impacts of a range of complex socio-ecological systems problems, where interaction effects make responsibilities, burdens, and future pathways unclear. Her research also examines how participatory complex systems modeling with stakeholders and decision-makers can support collaborative policy exploration, social learning, and system-wide transformation. Moira has taught a variety of workshops on complexity-based modeling of socio-ecological systems, for training of both scientists and decision-makers in the US and abroad. She has served the academic community spanning across the social and natural sciences, as reviewer of journals and grants and as a member of various scientific organizations. She is dedicated to serving the public through her engaged research and activism.
Before coming to Northeastern, Moira was an Associate Professor in the Department of Urban Planning and Policy and the Institute for Environmental Science and Policy at University of Illinois at Chicago. She also headed the Urban Data Visualization Lab at UIC. Prior to her academic career, Moira worked as an environmental consultant for local and international environmental engineering firms and for the undersecretary of Environment in the City of Buenos Aires, Argentina.
- American Institute of Architects College of Fellows Latrobe Prize, 2022
- American Planning Association Inaugural Academic Tech Innovator Award, 2017
- University of Illinois at Chicago Great Cities Institute Scholar, 2009-2010
Journal Articles
- Zellner, M.; 2024. Participatory modeling for collaborative landscape and environmental planning: From potential to realization. Landscape and Urban Planning 247 (2024) 105063. https://doi.org/10.1016/j.landurbplan.2024.105063
- Zellner, M.; Massey, D.; 2024. Modeling benefits and tradeoffs of green infrastructure: Evaluating and extending parsimonious models for neighborhood stormwater planning. Heliyon 10(5), 15 March 2024, e27007.https://doi.org/10.1016/j.heliyon.2024.e27007.
- Zellner, M.L.; Milz, D.; Lyons, L.; Hoch, C.; Radinsky, J.; 2022. “Finding the Balance Between Simplicity and Realism in Participatory Modeling for Environmental Planning.” Environmental Modelling & Software 157 (2022): 105481. DOI: https://doi.org/10.1016/j.envsoft.2022.105481
- Elsawah, S.; Filatova, T.; Jakeman, A.J.; Kettner, A.J.; Zellner, M. L.; Athanasiadis, I.N.; Hamilton, S.H.; Axtell, R.L.; Brown, D.G.; Gilligan, J.M.; Janssen, M.A.; Robinson, D.T.; Rozenberg, J.; Ullah, I.I.T.; Lade, S.J.; 2019. “Eight grand challenges in socio-environmental systems modeling.” Socio-Environmental Systems Modeling 2(2020): https://doi.org/10.18174/sesmo.2020a16226.
- Sterling, E.; Zellner, M.; Jenni, K.E.; Leong, K.; Glynn, P.; BenDor, T.; Bommel, P.; Hubacek, K.; Jetter, A.; Jordan, R.; Schmitt Olabisi, L.; Paolisso, M.; Gray, S.. 2019. “Try, try again: Lessons learned from success and failure in participatory modeling.” Elementa: Science of the Anthropocene 7(1), p.9. DOI: https://doi.org/10.1525/elementa.347.
- Zellner, M.L.; Campbell, S.; 2015. “Planning for Deep-Rooted Problems: What Can We Learn from Aligning Complex Systems and Wicked Problems?” Planning Theory and Practice 16 (4): 457-478.
- Zellner, M.L., Reeves, H. W.; 2012. “Examining the contradiction in ‘sustainable urban growth’: An example of groundwater sustainability.” Journal of Environmental Planning and Management, 55 (5): 545-562.
- Zellner, M. L.; 2008. “Embracing Complexity and Uncertainty: The Potential of Agent-Based Modeling for Environmental Planning and Policy.” Planning Theory & Practice 9 (4): 437-457.
Book Chapters
- Zellner, M.; Campbell, S. D. (2020). “Planning with(in) complexity: pathways to extend planning with complex systems modeling.” In Handbook on Planning and Complexity; edited by De Roo, Yamu, and Zuidema, C.; Edward Elgar Publisher, Cheltenham.
- Zellner, M. L., Lyons, L., Milz, D., Shelley, J., Hoch, C., Massey, D., & Radinsky, J. (2020). Participatory Complex Systems Modeling for Environmental Planning: Opportunities and Barriers to Learning and Policy Innovation. In Z. Porter & L. Schmitt Olabisi (Eds.), Innovations in Collaborative Modeling: Transformations in Higher Education (pp. 189–214). https://doi.org/10.14321/j.ctvz9396g.14
Community Work
- Voceros Program, City of Buenos Aires Representative in Boston, 2021-present
- MWRD Stormwater Master Planning Member, Advisory Committee, 2018-2020
- Voceros Program, City of Buenos Aires Representative in Chicago, 2017-2020
- Chicago Metropolitan Agency for Planning
- Member, Environmental and Natural Resources Committee, 2015-2018
- Alternate member, Environmental and Natural Resources Committee, 2007-2015
- Calumet Stormwater Collaborative Advisor to Modeling Group and to Policy and Planning Group, 2014-2020
- American Planning Association Advisory Committee Member, Incorporating Local Climate Science to Help Communities Plan for Climate Extremes, 2016-2018
Professional Service
- TMDL Analysis and Modeling Task Committee of the American Society of Civil Engineers Environmental and Water Resource Institute (ASCE-EWRI) (2022 – present)
- Co-Chair, Human Dimensions Focus Research Group of the Community Surface Dynamics Modeling System (CSDMS) (2016-present)
- Elected Executive Committee Member, Network for Computational Modeling for SocioEcological Science (ComSES) (2016-present)
- Scientific Committee Member, 2021 Ibero-American Symposium on Computational Sociology, Ibero-American Network of Computational Sociology
- Scientific Committee Member, Agent-Based Modeling Conference 2017
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Education
PhD, University of Michigan
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Contact
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Introduces complexity-based models, most notably agent-based models, and their possible applications to a range of planning and public policy issues. Exposes students to complexity theory and methods, including interactions, adaptation, and evolution; cellular automata, agents, networks, and genetic algorithms; and epistemology—the meaning and applications of models. Focuses on modeling and software, including building on sample models; running experiments and analyzing results; and verification, sensitivity, and validation.
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Introduces the theory, methods, and tools of dynamic modeling for policy and investment decision making, with special focus on environmental issues. Makes use of state-of-the-art computing methods to translate theory and concepts into executable models and provides extensive hands-on modeling experience. Topics include discounting, intertemporal optimization, dynamic games, and treatment of uncertainty.
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Participatory Modeling for Knowledge Co-Production and Collaborative Decision-Making
PPUA 5390-2
Participatory modeling is a collaborative approach to formalize shared representations of a problem that allows researchers and policymakers to design and test solutions to that problem. Using various modeling techniques, participatory modeling helps elicit diverse stakeholder knowledge and harnesses this diversity to move from conflict to solutions. The collaborative approach strengthens relationship-building, empathy, trust, systems thinking, and collective agency for decision-making.
