My background is in political science and economics, and those are the tools I tend to reach towards. I have a passable knowledge of data science, though I usually avoid ML models. I like interpretability. I'm interested in both joint projects and imbalanced ones, so long as there is interesting and important work to be done.
I have at least one funder in mind for every listed project.
I'm interested in projects at a wide range of scales, from academic publications or multi-year projects to giving some advice on a two week project that turns into a blog post.
New Governance Methods
Learning from the current flowering: There are lots of fascinating prediction markets being experimented with right now, but little in the way of centralized public knowledge, making it hard to advance collectively and risking that insights will be forgotten when projects shut down
I can provide initial introductions, possible funding, and history and insights
I'm looking for someone interested in interviewing people across the space and writing down their insights, likely embargoed for some number of years for competitive reasons
The ultimate goal would be a handbook of mistakes and experiences that could be handed to someone interested in starting up a prediction market for their company/hobby/country.
Why does Estonian e-voting work, and could it be extended to liquid democracy?
In a materialist framing, (techno-)authoritarian regimes will (probably) face a severe disadvantage at work that involves higher chosen risks and more uneven rewards. What policy shifts could incentivize these economic trends?
Defense Industrial Base Analysis
Current Research: I'm writing a dissertation on a new theoretical framework for describing defense industrial base analysis that aims to be useful to fellow practitioners and anyone interested in comparability. I think the framework is a useful one, and I'd be happy to bring it to your project. It works best in the middle stages, when insights can be used to refine other research, but it can also help with communication and presentation even after your research is completed.
I have received $139,000 from the Effective Altruist Long Term Future Fund and am not looking for additional funding for this project.
The single thing the field most needs to move forward is public data. Unfortunately, contractors are understandably very shy about sharing. It may, however, be possible to build a historical database that could generate useful insights. Without loss of generality, airplane manufacturing costs 1920-1960 seem like something where publishable results might be more possible than usual. If you're interested in diving into economic history with me, I have a few funding sources in mind and a solid econ framework but no experience with archival work.
For what Air Force needs will AI underperform conventional computational methods over the next 5-10 years?
What are the bottlenecks/production functions/scaling laws of AI production?