Ozzie Gooen

Build infrastructure for the future of effective forecasting efforts

From the application:

What I will do

I applied a few months ago and was granted $20,000 (thanks!). My purpose for this money is similar but greater in scope to the previous round. The previous funding has given me the security to be more ambitious, but I’ve realized that additional guarantees of funding should help significantly more. In particular, engineers can be costly and it would be useful to secure additional funding in order to give possible hires security.

My main overall goal is to advance the use of predictive reasoning systems for purposes most useful for Effective Altruism. I think this is an area that could eventually make use of a good deal of talent, so I have come to see my work at this point as foundational.

This work is in a few different areas that I think could be valuable. I expect that after a while a few parts will emerge as the most important, but think it is good to experiment early when the most effective route is not yet clear.

I plan to use additional funds to scale my general research and development efforts. I expect that most of the money will be used on programming efforts.

Foretold

Foretold is a forecasting application that handles full probability distributions. I have begun testing it with users and have been asked for quite a bit more functionality. I’ve also mapped out the features that I expect people will eventually desire, and think there is a significant amount of work that would be significantly useful.

One particular challenge is figuring out the best way to handle large numbers of questions (1000 active questions plus, at a time.) I believe this requires significant innovations in the user interface and backend architecture. I’ve made some wireframes and have experimented with different methods, and believe I have a pragmatic path forward, but will need to continue to iterate.

I’ve talked with members of multiple organizations at this point who would like to use Foretold once it has a specific set of features, and cannot currently use any existing system for their purposes. […]

Ken

Ken is a project to help organizations set up and work with structured data, in essence allowing them to have private versions of Wikidata. Part of the project is Ken.js, a library which I’m beginning to integrate with Foretold.

Expected Impact

The main aim of EA forecasting would be to better prioritize EA actions. I think that if we could have a powerful system set up, it could make us better at predicting the future, better at understanding what things are important and better at coming to a consensus on challenging topics.

Measurement

In the short term, I’m using heuristics like metrics regarding user activity and upvotes on LessWrong. I’m also getting feedback by many people in the EA research community. In the medium to long term, I hope to set up evaluation/estimation procedures for many projects and would include this one in that process.

My thoughts and reasoning

This grant is to support Ozzie Gooen in his efforts to build infrastructure for effective forecasting. Ozzie requested $70,000 to hire a software engineer who would support him on his work on the prediction platform www.foretold.iothat he is working on.

• When thinking about how to improve the long-term future, I think we are confused about what counts as progress and what specific problems need solving. We can already see that there are a lot of technical and conceptual problems that have to be solved to make progress on a lot of the big problems we think are important.
• I think that in order to make effective intellectual progress, you need some way for many people to collaborate on solving problems and to document the progress they have made so far.
• I think there is potentially a lot of low-hanging fruit in designing better online platforms for making intellectual progress (which is why I chose to work on LessWrong + AI Alignment Forum + EA Forum). Ozzie works in this space too, and previously built Guesstimate (a spreadsheet where every cell is a probability distribution), which I think displayed some real innovation in the way we can use technology to communicate and clarify ideas. It was also produced to a very high standard of quality.
• Forecasting platforms in particular have already displayed significant promise and tractability, with recent work by Philip Tetlock showing that a simple prediction platform can outperform major governmental institutions like the CIA, and older work by Robin Hanson, showing ways that prediction markets could help us make progress on a number of interesting problems.
• The biggest concerns I have with Ozzie’s work, as well as the work on other prediction and aggregation platforms, is that the problem of getting people to actually use the product turns out to be very hard. Matt Fallshaw’s team at Trike Apps built https://predictionbook.com/, but then found it hard to get people to actually use it. Ozzie’s last project, Guesstimate, seemed quite well-executed, but similarly faltered due to low user numbers and a lack of interest from potential customers in industry. As such, I think it’s important not to underestimate the difficulty of making the product good enough that people actually use it.
• I do think that the road to building knowledge aggregation platforms will include many failed projects and many experiments that never get traction; as such, I do think that one should not over-update on the lack of users for some of the existing platforms. As a positive counterexample, the Good Judgment Project seems to have a consistently high number of people making predictions.
• I’ve also frequently interacted with Ozzie in person, and generally found his reasoning and judgment in this domain to be good. I also think it is quite good that he has been writing up his thinking for the community to read and engage with, which will allow other people to build off of his thinking and efforts, even if he doesn’t find traction with this particular project.

Ozzie sought funding to build an online community of EA forecasters, researchers, and data scientists to predict variables of interest to the EA community. Ozzie proposed using the platform to answer a range of questions, including examples like “How many Google searches will there be for reinforcement learning in 2020?” or “How many plan changes will 80,000 hours cause in 2020?”, and using the results to help EA organizations and individuals to prioritize. We decided to make this grant based on Ozzie’s experience designing and building Guesstimate, our belief that a successful project along these lines could be very valuable, and some team members’ discussions with Ozzie about this project in more detail. This grant funds the project’s basic setup and initial testing.

Conducting research into forecasting and pandemics during an economics Ph.D.

Joel Becker is a first-year Ph.D. student at NYU who is pursuing forecasting research. The grant will act as a replacement salary for teaching for three years, to allow Joel to pursue research instead.

He intends to work on a few projects, including (text taken from the application):

• Extend 'external validity' projects with Rajeev Dehejia, using expert forecasting to select treated units in development interventions.
• Explore the accuracy of long-term forecasts in simulated environments (e.g. real history with blinded participants, games with different time horizons).
• Explore trade-offs in forecast elicitation techniques (accuracy vs. cost of training, accuracy from full distributions vs. point estimates, returns to different accuracy rewards, etc).

I think Joel could plausibly become a successful academic: He’s been part of the Philanthropy Advisory Fellowship at Harvard, and will soon join the Global Priorities Fellowship of the Forethought Foundation. I get the impression from this that he’s fairly committed to improving the long-term future.

Joel seemed to me to currently be working on somewhat standard academic forecasting topics. This has the benefits of having accessible advisors and an easier time integrating with other work. But it has the downside that the current academic forecasting topics are likely different than would be ideal for longtermists. My intuition is that much of the most valuable work for generic forecasting will be a combination of theoretical foundations and advanced software implementations, and the existing economics work is fairly separate from this. For example, much of the literature explores laboratory experimentation with what seem to me to be unrealistic and inhibited scenarios.

I’ve read some of Joel’s in-progress work, and he seems to get some things right (asking better questions than other academic work, and using more modern software), but this work is very early-stage.

The big question is what direction Joel goes after doing this research. I could see the research helping to build valuable credentials and skills. This could put him in a position to spearhead bold and/or longtermism-centric projects later on.

Providing general purpose support via the Berkeley Existential Risks Initiative.

The Stanford Existential Risks Initiative (SERI) is a new existential risk mitigation program at Stanford. They have 12 undergraduate student affiliates and what seems like a strong team of affiliates and mentors.

They seem to do a lot of things. From their website: “Our current programs include a research fellowship for Stanford undergraduates, an annual conference, speaker events, discussion groups, and a Thinking Matters class (Preventing Human Extinction) taught annually by two of the initiative's faculty advisors.”

This grant is to support them via BERI. I’ve personally had very good experiences with BERI and have heard positive reviews from others. BERI doesn’t just provide useful operations help; more importantly, they provide an alternative funding structure for a large class of things that universities tend not to fund. (It seems like the most prestigious universities sometimes have the most intensely restrictive funding policies, so this alternative could be a big deal.) I used BERI for help funding contractors for Foretold.io, and am confident I wouldn’t have been able to do this otherwise. (Note to those reading this: if you have an organization and would like help from BERI, I suggest reading this page and emailing them.)

My guess is that dollars given to requested BERI organizational grants would be somewhat more valuable than marginal ones going to the corresponding organizations (if this were not the case, then we would just donate directly to the organizations). For one, BERI organizational grants typically represent efforts to hire part-time help and perform small but valuable operations tasks horizontally through many projects of an organization. I think “making sure that existing projects go more smoothly” is rather safe.

This specific grant would be spent on a combination of:

• Stipends and researcher support for approximately one year
• Existential risk seed grants to graduate students
• Support for a spring conference
• Support for future courses on existential risks at Stanford
• Miscellaneous funds and BERI core operations expenses

This was a fairly easy bet to make. The Stanford EA community has historically been successful, and the current community seems promising. I reached out to a few people close to the community and got glowing reviews. The program seems to have a lot of activity and senior longtermist mentors.

The main downsides for this decision are more about the structure of the LTFF and the longtermist funding landscape than about SERI. This grant was somewhat unusual in that SERI was already funded by Open Philanthropy, but multiple parties (OP, BERI) wanted them to have a more diverse set of funding sources. One key question for me was whether the LTFF would really provide the intended benefits of a diverse funding ecosystem. It would be easy for the LTFF to simply defer to Open Phil on which groups are worth funding, for example. But if we did, that would defeat the main benefits of funding diversification.

This donation seemed like a good enough bet, and the organization still relatively small enough, for the grant to be a solid use of LTFF funding.