If a product derives from Federally-funded research, the government owns a share of the IP for that product. (This share should be larger than the monetary investment in the grants that bore fruit since the US taxpayer funds a lot of early-stage research, only a little of which will result in IP. So, this system must account for the investments that didn’t pan out as part of the total investment required to produce that product.)
Fund grants based on models of downstream benefit. Four things that should be included as “benefits” in this model are increased health span, increased capacity for bioengineering, an increased competent researcher pool, and a diverse set of researchers. Readers from backgrounds like mine may balk at “diversity” as an explicit benefit; however, diversity is vital to properly exploring the hypothesis space without the bias imposed by limited perspectives. Edit: see the replies for a discussion of what I mean by diversity.
Classify aging as a disease/disorder for administrative purposes. Set the classification to be reviewed/revised in 20 years after we have a better picture. (Whether it should be considered a single disease from a reality-modeling perspective is uncertain, but being able to target it in grants will give us more research that will help us model it better.)
Encourage inclusionary zoning at a Federal level.
Create a secure government-wide password manager. (If necessary, the HHS is large enough to do this alone, but the benefit would scale if used by other agencies.) Currently, HHS passwords may not be placed in password managers, leaving the HHS open to phishing credential stealing attacks. The project could be open-sourced to allow private firms to benefit from the research and engineering.
Make all health spending tax-deductible, whether or not it is funneled through an insurance company. (This is probably the domain of Congress, but maybe there is something HHS can do.)
Reduce the bureaucracy/red tape for TANF recipients.
Combine FEMA and ASPR
Work with the Census Bureau to collect and publish statistics on human flourishing in the US and push/advertise to make those numbers top-line numbers that the electorate (and thus politicians) pay attention to. Improving these statistics can be a “benefit” in the grant funding proposal above. HHS can also work to create conditional markets to predict how different decisions will affect those statistics.
Readers from backgrounds like mine may balk at “diversity” as an explicit benefit; however, diversity is vital to properly exploring the hypothesis space without the bias imposed by limited perspectives.
There are different kinds of diversity.
It seems to me like the decision of the Ida Rolf Foundation to start funding research had good downstream effects that we see in recent advances in understanding fascia. That foundation being able to fund things that the NHI wouldn’t fund was important. Getting a knowledge community like the Rolfers included in academic researchers is diversity that produces beneficial research outcomes.
If you follow standard DEI criteria, it doesn’t help you with a task like integrating the Rolfing perspective. It doesn’t get you to fund a white man like Robert Schleip.
I would suspect that coming from a background of economic poverty means that you likely have less slack that you can use to learn about knowledge communities besides the mainstream academic community. Having the time to spent in relevant knowledge communities, seems to me like a sign of economic privilege.
Maybe, you could get something relevant by focusing on diversity of illness burden within your researcher community as people with chronic illnesses might have spent a lot of time acquiring knowledge that produces useful perspectives, but I doubt that standard DEI criteria get you there.
If you follow standard DEI criteria,
I’m commenting on LessWrong; I don’t do “standard.”😉
More seriously, I apologize. I should have clarified what I meant by diversity. In particular, I mean that diverse groups are spread out in a parsimonious description space.
A pretty detailed example
As a concrete example of one understanding that would match my idea of diversity, consider some very high-dimensional space representing available people who can also do the work measured on as many axes as you can use to characterize them (characteristics of mind, body, experiences, etc.) and reduced by a technique to cause the remaining dimensions to give little mutual information about one another. Define a “diversity-growing procedure” for adding members to be one that chooses new members farthest away from the current subset. The more ways a diversity-growing procedure would choose a particular group, and the fewer exceptions that need to be made to the procedure to end up with that group, the more diverse the group.
Making an instance of this concrete example, imagine that our parsimonious space is 2D and that candidates are at all integer intersections of (0,1,2,3,4) x (0,1,2,3,4). If we choose candidates (2,2),(0,0), and (2,4), how diverse is that group? If you start with (0,0), the farthest away is (4,4), so you’ll need to make an exception to add (2,4) (the farthest actually in the group). From (2,4), the farthest away is (0,4); once again, we need an exception to add (2,2). If we start with (2,2), then (0,0) is one of the farthest away, but we need an exception to add (2,4). The sequence (2,4) → (0,0) → (2,2) requires one exception. So, we have three ways with five exceptions. (I may have gotten some of this wrong since I did it in my head, but I think this gives the picture.)
This is just one example. Many ways to define diversity match my intuition of spread in a parsimonious description space.
A note on parsimony
Since we’re looking for diversity to help us in a particular context, we should choose dimensions that predict differences in that context. For example, a characteristic like “ability to roll your tongue” is probably less predictive of behavior in a research environment than gender, so we might want to down-weight it. However, we don’t have good models of what matters yet, so it might be hubris to down-weight characteristics until we know they don’t matter for covering research hypothesis space because we’ve determined the effects by looking at groups actually doing research.
If you promote “diversity” then you have not only take in mind what you mean with it, but also how policy is likely going to work in practice.
In practice, there are some dimensions that are easy to measure like race and gender. There are other dimensions that are harder to measure. Some dimensions are also not conducive to research progress. Researchers with IQ under a hundred are underrepresented in grant giving.
Then there are variables like vaccination status, where being unvaxxed does not result in you having a worse ability to do research in the same way as having a lower IQ but there are perspectives on medical research that will correlate with vaccination status.
If your policy tries to increase the representation of unvaxxed researchers, that might be threatening to hegemonic beliefs and thus a research bureaucracy likely prefers increasing representation of minority races that are unlikely to threaten any hegenomic beliefs.
If you don’t specify the dimensions, the dimensions that are going to selected are most likely those that don’t threaten hegemony of current opinions and thus the dimensions that are least likely to actually matter for diversity of ideas and the selected dimensions might even be chosen to strengthen the hegemony of the existing ideas.
If you actually want real diversity by doing things like calling for diversity in vaccination status you should do that explicitly.
If a product derives from Federally-funded research, the government owns a share of the IP for that product. (This share should be larger than the monetary investment in the grants that bore fruit since the US taxpayer funds a lot of early-stage research, only a little of which will result in IP. So, this system must account for the investments that didn’t pan out as part of the total investment required to produce that product.)
Fund grants based on models of downstream benefit. Four things that should be included as “benefits” in this model are increased health span, increased capacity for bioengineering, an increased competent researcher pool, and a diverse set of researchers. Readers from backgrounds like mine may balk at “diversity” as an explicit benefit; however, diversity is vital to properly exploring the hypothesis space without the bias imposed by limited perspectives. Edit: see the replies for a discussion of what I mean by diversity.
Classify aging as a disease/disorder for administrative purposes. Set the classification to be reviewed/revised in 20 years after we have a better picture. (Whether it should be considered a single disease from a reality-modeling perspective is uncertain, but being able to target it in grants will give us more research that will help us model it better.)
Encourage inclusionary zoning at a Federal level.
Create a secure government-wide password manager. (If necessary, the HHS is large enough to do this alone, but the benefit would scale if used by other agencies.) Currently, HHS passwords may not be placed in password managers, leaving the HHS open to phishing credential stealing attacks. The project could be open-sourced to allow private firms to benefit from the research and engineering.
Make all health spending tax-deductible, whether or not it is funneled through an insurance company. (This is probably the domain of Congress, but maybe there is something HHS can do.)
Reduce the bureaucracy/red tape for TANF recipients.
Combine FEMA and ASPR
Work with the Census Bureau to collect and publish statistics on human flourishing in the US and push/advertise to make those numbers top-line numbers that the electorate (and thus politicians) pay attention to. Improving these statistics can be a “benefit” in the grant funding proposal above. HHS can also work to create conditional markets to predict how different decisions will affect those statistics.
There are different kinds of diversity.
It seems to me like the decision of the Ida Rolf Foundation to start funding research had good downstream effects that we see in recent advances in understanding fascia. That foundation being able to fund things that the NHI wouldn’t fund was important. Getting a knowledge community like the Rolfers included in academic researchers is diversity that produces beneficial research outcomes.
If you follow standard DEI criteria, it doesn’t help you with a task like integrating the Rolfing perspective. It doesn’t get you to fund a white man like Robert Schleip.
I would suspect that coming from a background of economic poverty means that you likely have less slack that you can use to learn about knowledge communities besides the mainstream academic community. Having the time to spent in relevant knowledge communities, seems to me like a sign of economic privilege.
Maybe, you could get something relevant by focusing on diversity of illness burden within your researcher community as people with chronic illnesses might have spent a lot of time acquiring knowledge that produces useful perspectives, but I doubt that standard DEI criteria get you there.
More seriously, I apologize. I should have clarified what I meant by diversity. In particular, I mean that diverse groups are spread out in a parsimonious description space.
A pretty detailed example
As a concrete example of one understanding that would match my idea of diversity, consider some very high-dimensional space representing available people who can also do the work measured on as many axes as you can use to characterize them (characteristics of mind, body, experiences, etc.) and reduced by a technique to cause the remaining dimensions to give little mutual information about one another. Define a “diversity-growing procedure” for adding members to be one that chooses new members farthest away from the current subset. The more ways a diversity-growing procedure would choose a particular group, and the fewer exceptions that need to be made to the procedure to end up with that group, the more diverse the group.
Making an instance of this concrete example, imagine that our parsimonious space is 2D and that candidates are at all integer intersections of (0,1,2,3,4) x (0,1,2,3,4). If we choose candidates (2,2),(0,0), and (2,4), how diverse is that group? If you start with (0,0), the farthest away is (4,4), so you’ll need to make an exception to add (2,4) (the farthest actually in the group). From (2,4), the farthest away is (0,4); once again, we need an exception to add (2,2). If we start with (2,2), then (0,0) is one of the farthest away, but we need an exception to add (2,4). The sequence (2,4) → (0,0) → (2,2) requires one exception. So, we have three ways with five exceptions. (I may have gotten some of this wrong since I did it in my head, but I think this gives the picture.)
This is just one example. Many ways to define diversity match my intuition of spread in a parsimonious description space.
A note on parsimony
Since we’re looking for diversity to help us in a particular context, we should choose dimensions that predict differences in that context. For example, a characteristic like “ability to roll your tongue” is probably less predictive of behavior in a research environment than gender, so we might want to down-weight it. However, we don’t have good models of what matters yet, so it might be hubris to down-weight characteristics until we know they don’t matter for covering research hypothesis space because we’ve determined the effects by looking at groups actually doing research.
If you promote “diversity” then you have not only take in mind what you mean with it, but also how policy is likely going to work in practice.
In practice, there are some dimensions that are easy to measure like race and gender. There are other dimensions that are harder to measure. Some dimensions are also not conducive to research progress. Researchers with IQ under a hundred are underrepresented in grant giving.
Then there are variables like vaccination status, where being unvaxxed does not result in you having a worse ability to do research in the same way as having a lower IQ but there are perspectives on medical research that will correlate with vaccination status.
If your policy tries to increase the representation of unvaxxed researchers, that might be threatening to hegemonic beliefs and thus a research bureaucracy likely prefers increasing representation of minority races that are unlikely to threaten any hegenomic beliefs.
If you don’t specify the dimensions, the dimensions that are going to selected are most likely those that don’t threaten hegemony of current opinions and thus the dimensions that are least likely to actually matter for diversity of ideas and the selected dimensions might even be chosen to strengthen the hegemony of the existing ideas.
If you actually want real diversity by doing things like calling for diversity in vaccination status you should do that explicitly.
How would you do that in practice? Is it a matter of adding a standard paragraph to NIH grants?