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AI is giving governments new tools to understand their populations, simulate policy outcomes, and make decisions more efficiently. But if governments can increasingly predict what citizens think or how they might behave, what happens to the role of citizens themselves?
This winter, I will undertake a research residency at the City Science Lab at Taipei Tech in Taiwan, part of the MIT City Science Network. The lab is developing synthetic population models that could help governments better understand urban populations and anticipate the effects of policy decisions, particularly in mobility and urban planning.
My goal is to understand how this technology works and explore how it could be integrated with citizen participation rather than used as a substitute for it. Through observation, interviews with researchers and local government practitioners, and continued research at Yale, I hope to develop a practical framework for responsible adoption by U.S. governments.
I've already established contacts with officials working on AI governance in Utah, New York, and Maryland, and hope to convene a working-group discussion to explore potential applications and eventually a pilot.
My underlying belief is simple: predicting citizens' behavior is not the same as giving them a voice. The goal should not simply be to make government better at predicting what citizens want, but better at listening to them.
First, I want to understand the technical capabilities and limitations of synthetic population models. During my month-long residency at Taipei Tech, I will observe how researchers develop, validate, and interpret these models, with particular attention to their applications in urban mobility and planning. I also plan to interview local government practitioners to understand how these tools might inform real policy decisions.
Second, I want to explore how governments can incorporate AI simulations into existing participatory democratic processes. A synthetic population might predict how citizens will respond to a proposed transportation system, but it cannot necessarily tell us whether they consider the project fair or what alternatives they would prefer. I hope to develop a practical framework identifying when simulations are useful, when direct citizen engagement remains essential, and how the two might complement one another.
Third, I want to explore how this framework could be applied in the United States. I've already initiated conversations with officials in Utah, New York, and Maryland who are involved in AI governance and public-sector innovation. Following the residency, I hope to convene a working-group discussion to share my findings and assess the feasibility of a small pilot with an interested government partner.
I will continue refining this work through a directed reading with Professor Ted Wittenstein, who leads Yale's Schmidt Program on AI and Emerging Technologies.
Ultimately, I want to move beyond another academic discussion about AI's promises and risks and develop something governments can actually use.
I will consider this project successful if I can demonstrate a clear pathway from studying emerging technology to identifying practical applications for governments.
At the conclusion of my Taiwan residency, I hope to have achieved three concrete outcomes:
A documented understanding of how synthetic population models are constructed, validated, and applied to urban policy decisions, informed by direct engagement with researchers and government practitioners.
A preliminary policy framework identifying where AI-enabled simulations can support public decision-making, where their limitations become significant, and how citizen participation could be incorporated alongside them.
A presentation of my findings to U.S. government contacts, with the aim of identifying at least one potential application or pilot opportunity.
The third outcome is particularly important to me. I don't want this project to end with a research paper that sits on a shelf. I want to understand whether these tools could actually improve how governments operate, and whether public officials see a realistic opportunity to adopt them.
I recognize that securing a pilot depends on factors beyond my control, including government priorities, technical feasibility, and institutional capacity. Even if a pilot does not immediately materialize, I would consider the project valuable if I can identify a concrete use case, establish relationships for continued collaboration, and clearly articulate the conditions under which the technology should or should not be used.
The ultimate measure of success is whether this research brings us closer to making governments both more effective and more accountable to the citizens they serve.
I am seeking $3,800 to cover the remaining costs of a month-long research residency at Taipei Tech in Taiwan, scheduled for December 2026 through January 2027.
My estimated total budget is $4,300, broken down as follows:
Round-trip airfare: $2,000
Accommodation (approximately one month): $1,400
Food: $400
Mandarin language instruction: $500
I have already secured $500 from Yale's Schmidt Program on AI and Emerging Technologies, which has reached its funding cap. However, airfare and accommodation remain significant financial barriers to undertaking the residency.
The funding will allow me to work directly with researchers developing synthetic population models, observe how this technology is being applied, and interview local government practitioners about its potential uses and limitations.
I also plan to take Mandarin refresher courses at Taipei Tech. Having previously studied Mandarin for nine years, I hope to rebuild my conversational skills and engage more meaningfully with the people and institutions I encounter. My Peace Corps experience in North Macedonia taught me how valuable language immersion can be in understanding a community and building relationships.
The research residency is the first step toward developing a framework for integrating AI simulations with democratic participation and exploring its potential application with U.S. government partners. Without additional funding, the travel and accommodation costs present a substantial obstacle to accessing this opportunity.
I am currently leading this project independently, with institutional and academic support from Yale University and the City Science Lab at Taipei Tech.
My research residency is being facilitated through a Yale World Fellow affiliated with the City Science Lab. I will also be working under the academic guidance of a professor who leads Yale's Schmidt Program on AI and Emerging Technologies and has agreed to supervise a directed reading following my residency.
My background is in public policy, democratic participation, and working directly with communities. Before Yale, I served in the Peace Corps, where I partnered with a local NGO on anti-corruption initiatives and participatory democracy. I helped implement citizen climate assemblies, including a pilot in Skopje where 65 citizens unanimously agreed on 19 policy recommendations. I also independently conducted research on youth attitudes toward corruption, using surveys and focus groups to examine why so many young people saw their futures outside the country.
I recognize that I am not a computer scientist, nor am I trying to become one. My strength is understanding how institutions operate, engaging with communities, and bringing stakeholders together.
I've already begun establishing relationships with officials working on AI governance in Utah, New York, and Maryland. I hope to bring these perspectives together to explore how the technology being developed in Taiwan might be responsibly applied in the United States.
The greatest risk is that the technology may be less applicable to real-world government decisions than anticipated.
Synthetic population models are promising, but they are still emerging tools. Their predictive capabilities may be limited, the data required to operate them may not be readily available, or their outputs may be difficult for policymakers to interpret. It's also possible that governments will lack the resources, institutional capacity, or political willingness to adopt them.
Another risk is that incorporating citizen participation into AI-assisted policymaking proves more difficult than expected. I am particularly concerned about the possibility that governments might use simulations as a convenient alternative to public engagement rather than as a complement to it. If the technology makes decisions more efficient but less accountable, I would not consider that a successful outcome.
Finally, there is no guarantee that my conversations with U.S. government officials will lead to a pilot. Public-sector adoption can be slow, and government priorities can change.
If the project does not produce a feasible application, I still hope to generate useful findings about the limitations of these technologies and the circumstances under which governments should approach them with caution.
I would rather identify early on that a technology is unsuitable for a particular policy application than encourage governments to adopt something that undermines public trust or democratic participation.
Ultimately, the purpose of this project is not to prove that synthetic populations are necessarily the future of governance. It is to understand whether, where, and how they can meaningfully improve public decision-making.
I have secured $500 in funding from Yale University's Schmidt Program on AI and Emerging Technologies to support this research project. This is the maximum amount available to me through the program.
The total anticipated cost of my Taiwan residency is $4,300, leaving a remaining funding gap of $3,800.
I have also secured institutional support for the project, including a research residency opportunity at Taipei Tech's City Science Lab and academic guidance from a Yale Professor. While these arrangements provide valuable research access and mentorship, they do not cover my travel and living expenses.
I am seeking additional grant funding to make the residency financially feasible.