Personally, I’m a huge fan of exploring ways of improving research efficiency and quality, and I am glad to see another person working on this.
Of potential relevance: I’m currently experimenting with an approach to supporting AI-relevant policy research via collaborative node-and-link “reality modeling / claim aggregation, summarization, and organization” (I’m not exactly sure what to call it).
For example, questions/variables like “How much emphasis would the Chinese government/Chinese companies put on (good) safety and alignment work (relative to America)” and “would more research/work on interpretability would be beneficial for powerful-AI/AGI alignment” seem like they could be fairly important in shaping various policy recommendations while also not being easy to evaluate. Additionally, there may be some questions or “unknown unknowns” that may not seem obvious to ask—especially for younger researchers and/or researchers who are trying to do research outside of their normal fields of expertise.
Having a hub for collecting and interrelating arguments/evidence on these and other questions seems like it could save time by reducing duplication/wasted effort and also increase the likelihood of encountering good points, among other potential benefits (e.g., better keeping track of back-and-forth responses and overall complexity for a question which doesn’t lend itself well to traditional mathematical equations). Additionally, if junior researchers (including even interns) are the primary source of labor for this project—which I expect would be fairly practical given that much of the “research” aspect of the work is akin to conducting literature reviews (which is then formalized into the node-and-link structure )—then you could potentially get comparative advantage benefits by having less-experienced researchers save research time of more-experienced researchers, all while providing a “training ground/on-ramp” for less-experienced researchers to become more familiar with various fields.
Because I am still testing out which approaches/structures seem to work best, the networks in the following screenshots arequite raw:there are still some inconsistencies in terms of node types and presence, little attention paid to aesthetics, many undefined placeholder relationships, inconsistencies in terms of granularity, etc. (That being said, the purple squares are generally “claims/assertions/arguments”, yellow triangles are policy proposals, blue hexagons are research questions, and green diamonds are generally “reality variables”)
Note: I do not think that the map view above should be the only way of viewing the research/analysis; for example, I think that one of the viewing methods should ideally be something like a minimal-information interface with dropdowns that let you search for and branch out from specific nodes (e.g., “what are the arguments for and against policy proposal X… what are the responses to argument Y”).
Related to this is my post on “epistemic mapping”, which put a greater emphasis on the academic literature around non-policy questions (including identifying authors, studies, the inputs for those studies (e.g., datasets), etc.) as opposed to supporting policy research directly—although both systems could probably be used for the same purposes with minor adaptations.
Also relevant—and more developed + better resourced than my project—is Modeling Transformative AI Risks (MTAIR), which puts more emphasis on quantitative analysis via input elicitation/estimation and output/effect calculation (and at the moment seems to focus a bit more on the factors and pathways of AI risk vs. the direct effects of policy, although my understanding is that it is also intended to eventually focus on policy analysis/recommendations).
Thanks for your comment Harrison. It looks like a really interesting problem you’re working on. In my experience, summaries and visualisation can be really powerful for coordination and alignment (of humans, let alone AIs!)
I’ll take a look at the links you’ve provided to learn more.
Personally, I’m a huge fan of exploring ways of improving research efficiency and quality, and I am glad to see another person working on this.
Of potential relevance: I’m currently experimenting with an approach to supporting AI-relevant policy research via collaborative node-and-link “reality modeling / claim aggregation, summarization, and organization” (I’m not exactly sure what to call it).
For example, questions/variables like “How much emphasis would the Chinese government/Chinese companies put on (good) safety and alignment work (relative to America)” and “would more research/work on interpretability would be beneficial for powerful-AI/AGI alignment” seem like they could be fairly important in shaping various policy recommendations while also not being easy to evaluate. Additionally, there may be some questions or “unknown unknowns” that may not seem obvious to ask—especially for younger researchers and/or researchers who are trying to do research outside of their normal fields of expertise.
Having a hub for collecting and interrelating arguments/evidence on these and other questions seems like it could save time by reducing duplication/wasted effort and also increase the likelihood of encountering good points, among other potential benefits (e.g., better keeping track of back-and-forth responses and overall complexity for a question which doesn’t lend itself well to traditional mathematical equations). Additionally, if junior researchers (including even interns) are the primary source of labor for this project—which I expect would be fairly practical given that much of the “research” aspect of the work is akin to conducting literature reviews (which is then formalized into the node-and-link structure )—then you could potentially get comparative advantage benefits by having less-experienced researchers save research time of more-experienced researchers, all while providing a “training ground/on-ramp” for less-experienced researchers to become more familiar with various fields.
Because I am still testing out which approaches/structures seem to work best, the networks in the following screenshots are quite raw: there are still some inconsistencies in terms of node types and presence, little attention paid to aesthetics, many undefined placeholder relationships, inconsistencies in terms of granularity, etc. (That being said, the purple squares are generally “claims/assertions/arguments”, yellow triangles are policy proposals, blue hexagons are research questions, and green diamonds are generally “reality variables”)
Note: I do not think that the map view above should be the only way of viewing the research/analysis; for example, I think that one of the viewing methods should ideally be something like a minimal-information interface with dropdowns that let you search for and branch out from specific nodes (e.g., “what are the arguments for and against policy proposal X… what are the responses to argument Y”).
Related to this is my post on “epistemic mapping”, which put a greater emphasis on the academic literature around non-policy questions (including identifying authors, studies, the inputs for those studies (e.g., datasets), etc.) as opposed to supporting policy research directly—although both systems could probably be used for the same purposes with minor adaptations.
Also relevant—and more developed + better resourced than my project—is Modeling Transformative AI Risks (MTAIR), which puts more emphasis on quantitative analysis via input elicitation/estimation and output/effect calculation (and at the moment seems to focus a bit more on the factors and pathways of AI risk vs. the direct effects of policy, although my understanding is that it is also intended to eventually focus on policy analysis/recommendations).
Thanks for your comment Harrison. It looks like a really interesting problem you’re working on. In my experience, summaries and visualisation can be really powerful for coordination and alignment (of humans, let alone AIs!)
I’ll take a look at the links you’ve provided to learn more.