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Displaying 3 of 3 results complex systems science clear search

Gina Graham Member since: Fri, Apr 03, 2015 at 10:14 PM

MPH in process

complex systems science; implementation science; agent based modeling; health care infrastructure and population health; public health

S.R. Aurora (also known as Mai P. Trinh) Member since: Wed, Apr 20, 2022 at 05:23 PM Full Member

Ph.D., Organizational Behavior, Case Western Reserve University

S.R. Aurora, also known as Mai P. Trinh, is an Assistant Professor of Management at The University of Texas Rio Grande Valley. Her interdisciplinary work intersects leadership, complex systems science, education, technology, and inclusion. Her research harnesses technology to cultivate future leaders and helps people thrive in our volatile, uncertain, complex, and ambiguous (VUCA) high-tech world, aligning with four United Nations’ sustainable development goals: Quality education (#4), Gender equality (#5), Decent work and economic growth (#8), and Reduced inequalities (#10). She has published in top-tiered peer-reviewed journals such as The Leadership Quarterly and The Academy of Management Learning and Education and received multiple national and international awards for her research, teaching, and mentoring. Dr. Aurora earned her doctoral degree in Organizational Behavior from the Weatherhead School of Management at Case Western Reserve University in 2016.

Leader development, leading complex systems, agent-based modeling, experiential learning, innovations in online education

Wolfram Barfuss Member since: Thu, Aug 10, 2023 at 12:41 PM

Hi. I’m Wolf. I’m the Argelander (Tenure-Track Assistant) Professor for Integrated System Modeling for Sustainability Transitions at the University of Bonn, Germany.

We reshape human-environment modeling to identify critical leverage points for sustainability transitions.

Cooperation at scale – in which large collectives of intelligent actors in complex environments seek ways to improve their joint well-being – is critical for a sustainable future, yet unresolved.

To move forward with this challenge, we develop a mathematical framework of collective learning, bridging ideas from complex systems science, multi-agent reinforcement learning, and social-ecological resilience.

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