Director of Epoch, an organization investigating the future of Artificial Intelligence.
Currently working on:
Macroeconomic models of AI takeoff
Trends in Artificial Intelligence
Forecasting cumulative records
Improving forecast aggregation
I am also one of the coordinators of Riesgos Catastróficos Globales, a Spanish-speaking network of experts working on Global Catastrophic Risks.
I also run Connectome Art, an online art gallery where I host art I made using AI.
(speculating) The key property you are looking for IMO is to which degree people are looking at different information when making forecasts. Models that parcel reality into neat little mutually exclusive packages are more amenable , while forecasts that obscurely aggregate information from independent sources will work better with geomeans.
In any case, this has little bearing on aggregating welfare IMO. You may want to check out geometric rationality as an account that lends itself more to using geometric aggregation of welfare.