Ted Hsuan Yun Chen
George Mason University, VA·Social Science
As climate risks grow, some people flood-proof their homes or move out of high-risk areas, while others do nothing. This project asks why.
- Fields
Social Science
Mathematics & Statistics
Earth & Environmental Science
- Topics
climate adaptation
belief networks
belief systems
- Grants
- 1 known grant, through May 2027
What they work on
- Develop a survey-based measurement approach and conceptual framework for belief networks, capturing how attitudes on climate, migration and social fairness influence one another within individuals.
- Map how belief networks vary across partisan, demographic and socioeconomic groups using large-scale U.S. surveys and a case study in Virginia.
- Run science-communication experiments testing how information that targets specific attitudes, or the links between them, changes people's motivation and capacity to adapt to climate risks.
- Build network science and statistical tools, released as software, for extracting, modeling and comparing individual-level belief systems at scale.
More about the research
Its starting point is that people's beliefs are linked. Views on the environment, politics, migration and fairness shape one another. Scientific messages about adaptation may therefore land very differently depending on how a person's beliefs connect. The project also examines how these choices could unintentionally deepen social and economic inequalities.
Why it matters
Communities facing floods and other climate hazards need adaptation messages that actually change behavior without leaving vulnerable groups behind. Clarifying how linked beliefs shape decisions such as flood-proofing or relocating could help scientists, nonprofits and planners design more effective, inclusive climate communication. The project's software could also aid other researchers who study belief systems.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Filippo Menczer
Indiana University, IN·Computer Science
Build an agent-based simulation of how content spreads through human social networks, modeling individual traits and network structure to predict large-scale information flow.
Mona Behl
William & Mary, VA·Social Science
Foster analysis and synthesis research on human mobility caused by coastal change, including displacement, migration and planned relocation, using existing datasets and legal and policy frameworks.
Kenneth Joseph
University at Buffalo, NY·Computer Science·Early-career
Develop mixed methods combining graph-based and natural-language machine learning with qualitative analysis to identify dominant beliefs about social issues in large datasets from X and Parler.
The school
George Mason University: 15 labs on PI Pages, $282M a year on research, 297 research doctorates a year.
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