We are less sure that letting students choose for themselves from a list of topics proposed by EA research institutions is an effective way to utilize this branching moment.
I think this is worth re-considering. I see strong potential for applied projects on specific topics, tied to specific measures and datasets of interest. This might not be the best for people looking to pursue broad ‘theoretical/academic’ research careers. However, it could be very useful as a stepping stone to working at an EA research or impact-driven org.
I had a version of this (list of topics) when I was supervising the undergrad Econ dissertation modules at Essex and Exeter universities. Seemed to work well.
I especially see value in things like cost-effectiveness analysis, shallow reviews of potential high impact interventions/cause areas, fermi/Monte-carlo estimation, forecasting, meta-analysis/synthesis, and data science stuff, including re-analysis of existing data from trials and experiments in movement building, charitable giving, etc.
EA and EA research orgs often have more questions and more data than we have the capacity to handle. And these are often very interesting contexts from an academic and research PoV. Students can work on these, and get some feedback and recognition for their work.
There should be little risk, because if the students’ analysis ends up not being useful, the organization and decisionmakers do not have to use it.
E.g., a case in point (sketch):
High Impact Athletes is interested in modeling the estimated lifetime income over an athlete’s career, as a function of their sport, age, gender, ranking, etc. They would also like to model expected value of atheletes donations, and of their voice/impact on others. (The latter is harder still, but note there is some academic lit on, e.g., the value of celebrity Twitter posts
From experience, I know students are interested in this sort of topic and I suspect there is some research out there already. This could then be part of a larger model (Fermi Montecarlo guesstimate squiggle) of expected donations from athletes who pledge.
Why: this can help them consider which athletes to target more, and how much to emphasize their different theories of change (athlete donations, athletes getting their supporters to donate, etc.)
I think this is worth re-considering. I see strong potential for applied projects on specific topics, tied to specific measures and datasets of interest. This might not be the best for people looking to pursue broad ‘theoretical/academic’ research careers. However, it could be very useful as a stepping stone to working at an EA research or impact-driven org.
I had a version of this (list of topics) when I was supervising the undergrad Econ dissertation modules at Essex and Exeter universities. Seemed to work well.
I especially see value in things like cost-effectiveness analysis, shallow reviews of potential high impact interventions/cause areas, fermi/Monte-carlo estimation, forecasting, meta-analysis/synthesis, and data science stuff, including re-analysis of existing data from trials and experiments in movement building, charitable giving, etc.
EA and EA research orgs often have more questions and more data than we have the capacity to handle. And these are often very interesting contexts from an academic and research PoV. Students can work on these, and get some feedback and recognition for their work.
There should be little risk, because if the students’ analysis ends up not being useful, the organization and decisionmakers do not have to use it.
E.g., a case in point (sketch):
High Impact Athletes is interested in modeling the estimated lifetime income over an athlete’s career, as a function of their sport, age, gender, ranking, etc. They would also like to model expected value of atheletes donations, and of their voice/impact on others. (The latter is harder still, but note there is some academic lit on, e.g., the value of celebrity Twitter posts
From experience, I know students are interested in this sort of topic and I suspect there is some research out there already. This could then be part of a larger model (Fermi Montecarlo guesstimate squiggle) of expected donations from athletes who pledge.
Why: this can help them consider which athletes to target more, and how much to emphasize their different theories of change (athlete donations, athletes getting their supporters to donate, etc.)