Apologies for the delay. Just finished things up. The methodology overall is new and I hope to publish it soon as well as the results. It is based on general morphological analysis which is quite old and has been used in a host of different areas.  The morphological society has a good run down and list of publications here. 

Here are a few solid examples here and here. The closest example to what I did is this climate report and this policy design. 

Mine uses the same basic framework, I just added the survey and computational clustering component.

A Quick point I forgot to make (or understand fully on the point). RE the fast takeoff comment at the end. Agreed. I had both on the original, fast takeoff controlled, fast takeoff uncontrolled, as well as CAIS fast, mod, slow, totaling about 6 choices. It got butchered. Way too many choices to rank. 

So, I dropped it down to 4; I was told to go to 3, but I thought the "anticipated or unanticipated" points you make are quite valid and key, especially for moderate (equivalent to Christiano-style relatively fast takeoff) which is why there are two options - Moderate uncontrolled, a complete surprise in capability jumps, and moderate controlled, which suggests a competitive anticipated race dynamic, perhaps due to conflict and competition. So, fast unfortunately was left to include both anticipated, and unanticipated. I hope to break that out further, but I'll likely be confined to the literature for that. 

Yes, I'm having a tough time explaining the purpose of the model which has led to very long convoluted descriptions. I am not predicting or attempting to predict any of these conditions. I understand your skepticism and share it. I believe overall that it can be a waste of time to concentrate on forecasting very difficult or impossible to measure issues. That is certainly not the purpose of this. 

The point of this is to construct broad categories of plausible (approximately) scenarios and impactful ones (broadly, a lot of these should be marked no effect) simply to create categories. However, the output does not say what is or is not going to happen, or is certainly best or worst, it will be a narrative showing all options which will have mixed values (the combining values process changes these all up regardless).  The likelihood survey values are much more valuable, but the best assessment of impact is important too (but admittedly much less clear). 

For example, all the values for individual conditions (e.g., paradigm) will be calculated with every other, but the output is not "fast takeoff scenario is 80% likely," or "greatly decrease x" the output will be potential scenario elements that are mixed e.g., "fast takeoff" (unlikely, but high impact) and "new paradigm"(likely, but moderate impact) which will just be one of the many possible outputs. Thus, thousands of these pairs will be clustered and we'll use the clusters to develop scenarios.

For the likelihood questions this is clearer I think, it multiplies (or adds) depending on the variable to highlight how one condition is affected by the other. Ideally, and this is the plan depending on how this goes, is to have a workshop or roundtable to go through each one of these pairs (e.g., fast takeoff, and distribution, is a value pair) and request expert judgment on how one may affect the other. 

While this is somewhat imprecise by design but an AI researcher's view on whether deep learning will lead to AGI, or if prosaic AI is potentially more or less destabilizing, I believe is much more trustworthy than a random guess.

I have realized though that in future iterations (if there are any) I most certainly will not ask likelihood questions. That tends to get folks thinking about probability which would require more precise questions. And the impact is just a tough one. But the combination is important. Other projects we've done with this have been for climate change and arctic politics which were also quite vague, yet valuable in the end. 

It looks like I just submitted another long-convoluted description lol. I get carried away attempting to explain the issue. 

In any event, what I'm requesting is the best estimates from knowledgeable people to form groups for the model. Which will be used to paint the range of hopefully quite unique combinations of scenarios and test the GMA method. Who knows, it may provide important insights or a new tool for the community to use. 

If you have any suggestions on how to frame this better or explain (now or in the future) please let me know. 

Updated the survey with a google form that seems to move a little faster. https://forms.gle/92TAeBxE4zttT5P9A

That's a very interesting project. I'd be very curious to see the finished product. That has become a frequently discussed aspect of AI safety. One member of my panel is a significant advocate of the importance of AI risk issues and another is quite skeptical and reacts quite negatively to any discussion approaches the A*I word ("quite" may be a weak way of putting it). 

But concerning policy communication, I think those are important issues to understand and pinpoint. The variance is certainly strange. 

Side note: As a first-time poster, I realized looking at your project, I failed to include a TL;DR and a summary for the expected output on mine. I'll try and edit, or on the next post, I suppose.