I do research on the philosophy and psychology of rationality and well-being in a PhD project that is part of the interdisciplinary research project Modeling Human Happiness at the University of Oslo. I also make technologies to improve learning and reasoning through Disputas, a tech-startup I co-founded. See my website here: https://paalkvarberg.com/
Paal Fredrik Skjørten Kvarberg
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Yes, the 60 FFI supers were selected and evaluated on the same 150 questions (Beadle, 2022, 169-170). Beadle also identified the top 100 forecasters based on the first 25 questions, and evaluated their performance on the basis of the remaining 125 questions to see if their accuracy was stable over time, or due to luck. Similarly to the GJP studies, he found that they were consistent over time (Beadle, 2022, 128-131).
I should note that I have not studied the report very thoroughly, so I may be mistaken about this. I’ll have a closer look when I have the time and correct the answer above if it is wrong!
Good question! There were many differences between the approaches by FFI and the GJP. One of them is that no superforecasters were selected and grouped in the FFI tournament.
Here is google’s translation of a relevant passage: “In FFI’s tournament, the super forecasters consist of the 60 best participants overall. FFI’s tournament was not conducted one year at a time, but over three consecutive years, where many of the questions were not decided during the current year and the participants were not divided into experimental groups. It is therefore not appropriate to identify new groups of super forecasters along the way” (2022, 168). You can translate the entirety of 5.4 here for further clarification on how Beadle defines superforecasters in the FFI tournament.
Two directions for research on forecasting and decision making
Thank you for this thoughtful post! I am just about to start a PhD in philosophy on the psychology and metaethics of well-being, so I am fairly familiar with the research literature on this topic in particular. I totally agree with you that foundational issues should be investigated more deeply in EA circles. To me, it is baffling that there is so little discussion of meta-ethics and the grounds for central propositions to EA.
You are right that many philosophers, including some who write about method, think that ethics is about weighing intuitions in reflective equilibrium. However, I think that it is seriously misleading to state this as if it is an undisputed truth (like you do with the quote from Michael Plant). In the 2020 Philpapers survey, I think about half of respondents thought intuitions-based philosophy was the most important. However, the most cited authors that do contemporary work in philosophical methodology eg. Timothy Williamson and Herman Cappelen, dont think intuitions plays important roles at all, at least not if intuitions are thought to be distinct from beliefs.
I think that all the ideas you mention concerning how to move forward look very promising. I would just add “explore meta-ethics”, and in particular “non-intuitionism in ethics”. I think that there are several active research programs that might help us determine what matters, without relying on intuitions in a non-critical way. I would especially recommend Peter Railton’s project. I have also written about this and I am going to write about it in my PhD project. I would be happy to talk about it anytime!
Hey Harrison! This post evaded me until now. I am sorry to hear that you are not going to continue working on this. I hope that you will still follow progress of other projects, and that you will chime in with feedback when some of the people working on similar things post about it i the future!
Thank you for this! I think that there are several paths to impact for a scaled up version of this, but I am not at all sure what path is most consequential. I am curious about what you think is the most important way evaluations of this sort can have an impact.
Thank you for this! I think your framework for instructional design is likely to be very useful to several projects working to create educational content about EA. I happen to be one of these people, and would love to get in touch. Here is a onepager about the project I am currently pursuing. I shared your post with others who might fint it interesting.
I look forward to seeing what you decide to do next!
I participated in an activity of this sort some years ago. I really enjoyed the structured conversation, and working towards consensus in a group. The experience was way more intense than any other context of presentation or debate that I have been a part of otherwise. I don’t know whether EA groups should use the technique, but I wanted to share from my own experience:)
Thanks for writing up this idea in such a succinct and forceful way. I think the idea is good, and would like to help any way I can. However, I would encourage thinking a lot about the first part “If we get the EA community to use a lot of these”, which I think might be the hardest part.
I think that there are many ways to do something like this, and that it’s worth thinking very carefully about details before starting to build. The idea is old, and there is a big graveyard of projects aiming for the same goal. That being said, I think a project of this sort has amazing upsides. There are many smart people working on this idea, or very similar ideas right now, and I am confident that something like this is going to happen at some point.
Metaculus is also currently working on a similar idea (causal graphs). Here are some more people who are thinking or working on related ideas, (who might also appreciate your post): Adam Binks, Harrison Durland, David Manheim and Arieh Englander (see their MTAIR project).
Seems like I forgot to change “last updated 04.january 2021” to “last updated 04. january 2022″ when I made changes in january haha.
I am still working on this. I agree with Ozzie’s comment below that doing a small part of this well is the best way to make progress. We are currently looking at the UX part of things. As I describe under this heading in the doc, I don’t think it is feasible to expect many non-expert forecasters to enter a platform to give their credences on claims. And the expert forecasters are, as Ian mentions below, in short supply. Therefore, we are trying to make it easier to give credences on issues while reading about them the same place you read about them. I tested this idea out in a small experiment this fall (with google docs), and it does seem like motivated people who would not enter prediction platforms to forecast issues might give their takes if elicited this way. Right now we are investigating this idea further through an mvp of a browser extension that lets users give credences on claims found in texts on the web. We will experiment some more with this during the fall. A more tractable version of the long doc is likely to appear at the forum at some point.
I’m not wedded to the concrete ideas presented in the doc, I just happen to think they are good ways to move closer to the grand vision. I’d be happy to help any project moving in that direction:)
Thank you for this. This is all very helpful, and I think your explanations of giving differential weights to factors for average orgs and EA orgs seems very sensible. The 25% for unknown unknowns is probably right too. It doesn’t seem unlikely to me that most folks at average orgs would fail to understand the value of prediction markets even if they turned out to be valuable (since it would require work to prove it).
It would really surprise me if the ‘main reason’ why there is a lack of prediction markets had nothing to do with anything mentioned in the post. I think all unknown unknowns might conjunctly explain 25% of why prediction markets aren’t adopted, but the chance of any single unknown factor being the primary reason is, I think, quite slim.
On 4., I very much agree that this section could be more nuanced by mentioning some positive side-effects as well. There might be many managers who fear being undermined by their employees. And surely many employees might feel shameful if they are wrong all the time. However, I think the converse is also true. That managers are insecure, and would love for the company to take decisions on complex hard to determine issues collectively. And that employees would like an arena to express their thoughts on things (where their judgments are heard, and maybe even serves to influence company strategy). I think this is an important consideration that didn’t get through very clearly. There are other plausible goods of prediction markets that aren’t mentioned in the value prop, but which might be relevant to their expected value.
Thank you all for posting this! I am one of the people who are confused by the puzzle you make serious inroads towards shedding light on in this post. I really appreciate that you break down explanatory factors in the way you do. To me, it seems like all four factors are important pieces of the puzzle. Here they are:
The markets must have a low enough cost to create and maintain.
The markets must provide more value to decision-makers than the cost to create them and to subsidize predictions on them.
The markets must be attractive enough to traders to elicit accurate predictions.
The markets must not have large negative side-effects, such as costs to the company’s dynamics and morale.
Although you explain the idea behind each of these, I have a hard time making a mental model of their relative importance compared to each other. Do you think that such an exercise is feasible, and if so, do any of you have a conception of the relative explanatory strength of any factor when considered against the others? Also, do you think that it is likely that the true explanation has nothing to do with any of these? In that case, how likely?
Strong upvoted! I think something like this would introduce exactly the kinds of people whom we would like to use the wiki, to the wiki. I like the first version best, as many writers might not be aware of the ways to link to tags, and not be aware of what tags exist. Also, this nudges writers to use the same concepts for their words (because it is embarrassing to use a word linked to a tag in another sense then is explained in that tag).
Cool idea! I think there are some others that are also thinking about this, and they would probably love a helping hand:) More info in DM
Good! Yeah, I didn’t mean to say that any of these capture all the specifics of your idea, but merely that there is a lot of interest in this sort of thing. It’s probably worthwhile pursuing this in more detail, I’d be interested in seeing more on this.
Hi! I’ve also been thinking and working a bit on this idea. Here are some brief answers to your questions.
Yes, something like this exist. There are many projects pursuing different varieties of the idea you are sketching. Perhaps the smoothest computer program for this is https://www.swarmcheck.ai/. An older, more complicated software is https://www.norsys.com/. It is also possible to use https://roamresearch.com/, or https://obsidian.md/ for similar applications. https://www.kialo-edu.com/ also does something similar. As you are probably well aware, there is a host of related forecasting initiatives. In analytical philosophy, theorists have been discussing the structure of an ideal graph like this for a while. See J. S. Ullian and W. V. O. Quine’s The Web of Belief (1970) for a short intro to relevant concepts.
I tend to think so. Everything depends on implementation though. It is not feasible if usage is tedious or complex.
I have given this a bit of thought, and now tend to think that it would be quite useful to get an overview of the main arguments.
I think it could be useful to most areas, and particularly to see interconnections between cause areas.
The idea is typically referred to as mind-mapping or argument-diagrams. For clarity, it is probably best to use these names. However, the name I like the most (even though it is not practical) is ‘logical knowledge graph’, because the edges in the graph structure would constitute logical inferences, and the nodes would be propositions. I also like ‘digital knowledge infrastructure’.
This is a cool project! I, for one, would love to see more thought invested in this.
Thank you for a cool list of ideas! On the BOTEC-tool idea I want to note that there are many ongoing projects related to this that you should check out (if you haven’t already). I have listed serious efforts that I have found so far in this section of this post. Also, I’m curious about how far you and Tom have come in working on this. Are you just starting out, or are you making this now?