FWIW, Los Alamos claims they replicated Hiroshima and the Berkeley Hills Fire Smoke Plumes with their fire models to within 1 km of plume height. It's pretty far into the presentation though, and most of their sessions are not public, so I can hardly blame anyone for not encountering this.
This is a really good summary. I think the main area I have remaining uncertainty about these types of arguments is around if tech decisively favors finders or not. I believe there's been a lot of analysis that implies that modern nuclear submarines are essentially not feasible to track and destroy, and there are others that argue AI-enabled modeling and simulation could be applied as a "fog of war" machine to simulate an opponent's sensor system and optimize concealment of nuclear forces.
Nevertheless, without more detail these sorts of counterarguments seem highly contingent to me: they depend on the state of technology that actually gets deployed, and there may be decisive counter-counter arguments based on other military capabilities.
It would be interesting to see more EAs "grok" these sorts of arguments and to think through corresponding nuclear modernization and arms control strategies that would be implied by trying to assure a desirable long-term future. I've tended to think more redundant and accurate arsenals of low-yield weapons like neutron bombs would tend to strengthen deterrence while eliminating most of the risk of nuclear winter and long-term radiation, but it's easy to imagine there might be better directions yet to ratchet competition and negotiation since that kind of proposal could also be very destabilizing!
I am not sure we should focus more into this area, I just want to make sure that in general, people who go into policy or advocacy don't propagate bad ideas, or discredit EA with important people who would otherwise be aligned with our goals in the future.
I do think that knowing the history of transformative technologies (and policies that effected how they were deployed) will have a lot of instrumental value for EAs trying to make good decisions about things like gene editing and AI.
F-15s and MRAPs still have to be operated by multiple people, which requires incentive alignment between many parties. Some autonomous weapons in the future may be able to self-sustain and repair, (or be a part of a self-sustaining autonomous ecosystem) which would mean that they can be used while being aligned with fewer people's interests.
A man-at arms wouldn't be able to take out a whole town by himself if more than a few peasants coordinate with pitchforks, but depending on how LAWS are developed, a very small group of people could dominate the world.
I actually agree with a lot of your arguments, but I don't agree overall. AI weapons will be good and bad in many ways, and if the are good or bad overall depends on who has control, how well they are made, and the dynamics of how different countries race and adapt.
The reason it is appropriate to call this ethical reaction time, rather than just reaction time is because the focus of planning and optimization is around ethics and future goals. To react quickly with respect to an opportunity that is hard to notice, you have to be looking for it.
Technical reaction time is a better name in some ways, but it implies too narrow of a focus, while just reaction time implies too wide of a focus. There probably is a better name though.
I think you may be misunderstanding what I mean by ethical reaction time, but I may change the post to reduce the confusion. I think adding examples would make the post a lot more valuable.
Basically, what I mean by ethical reaction time is just being able to make ethical choices as circumstances unfold and not be caught unable to act, or acting in a bad way.
Here’s a few examples, some hypothetical, some actual:
Policy:
One can imagine the Reach Every Mother and Child Act might have passed last year if a few congressmen were more responsive in adjusting it to get past partisan objections (left wing opposing funding religious groups, right wing opposing funding contraception). That likely would have saved a few thousand lives, and possibly millions according to USAID. (http://effective-altruism.com/ea/pk/how_to_support_and_improve_the_reach_every_mother/) My model of the political constraints on the Reach Act may be wrong here though.
Any regulation of technology that starts occurring in response to the technology rapidly coming into existence: Uber executed its plans faster than it could be banned, which was probably good. We don’t necessarily want the same to be true for certain tech risks in AI and biology, which makes it important to figure out quickly the correct way to regulate things.
Anything on the U.S. Federal Register that is going poorly: if you don’t notice a new rule come up relevant to your area of interest (easy to imagine this with animal welfare and new tech) and respond within the comment period, your concerns aren’t going to inform the regulation (if you put in relevant research, and get ignored, you can sue the federal agency and win: this happened when the FDA first failed to ban trans fat). This is also a sort of situation that may actually require you to do research under a lot of time pressure.
Donor coordination:
If your organization is not prepared to accept/talk to donors, there are often times you will lose a lot that you’d otherwise get. This I think is one of the reasons David Goldberg with Founder’s Pledge would carry a backpack with him containing everything needed for someone to legally commit some % of their venture cash out value to effective charities.
Start-ups:
If initially you need partnerships/funding and others are competing for those partnerships/funding then OODA loop applies (but less for funding since there can be more sources).
Salary negotiations:
It makes a lot of sense to know how you are going to negotiate ahead of time, or to be very quick in thought. (http://haseebq.com/my-ten-rules-for-negotiating-a-job-offer/) Saying the wrong thing could cost you thousands of dollars, and if you are donating that to AMF, that’s likely costing lives.
Grants/research opportunities:
Grant opportunities = competitive = OODA loop. Less true when there is a deadline that is far away however.
In 2015, there were several EA organizations that had the opportunity to get free research from grad students who were interested in Effective Altruism from the School of Public Policy at the University of Maryland. In order to get free research, an organization would have had to submit a general/rough research proposal (<1 page), in which they could enumerate some of the means by which a study or literature review would be undertaken to maintain rigor/ guidelines for the advisor monitoring grad students. No one was able to react within the month after solicitation, so other non-EA organizations got free research instead for the grad student projects class. It does seem reasonable that EA orgs may have had better priorities, and that there is reason to be skeptical of the grad students, but it would have been a good way to get a bunch of students going into policy more bought into EA even if they didn’t produce work at a level we’d accept. This is also partly my fault, since I could have informed groups earlier, though not by a lot.
Handling the throughput vs. responsiveness trade off:
If you set up systems so that you can be reactive without draining as much of your attention, you can get more things done generally. Dropping responsiveness and responding to emails once per day or less may make sense if you are a coder/researcher, but it doesn’t make sense if you are an information node between organizations that need to coordinate. Adopting simple algorithms like writing down everything in my working memory before taking a call has made me both a lot more willing to take calls, and sped up my ability to get right back to work after interruption.
Time sensitive opportunities:
If factory farming and or malaria are going to be gone at some point in the next 20 years due to development/economics, then there won’t be the same opportunity to reduce suffering/save lives in the future that there is now. That being said, donations don’t require prompt reaction to opportunity the way policy opportunities with respect to these do.
FWIW, Los Alamos claims they replicated Hiroshima and the Berkeley Hills Fire Smoke Plumes with their fire models to within 1 km of plume height. It's pretty far into the presentation though, and most of their sessions are not public, so I can hardly blame anyone for not encountering this.
This is a really good summary. I think the main area I have remaining uncertainty about these types of arguments is around if tech decisively favors finders or not. I believe there's been a lot of analysis that implies that modern nuclear submarines are essentially not feasible to track and destroy, and there are others that argue AI-enabled modeling and simulation could be applied as a "fog of war" machine to simulate an opponent's sensor system and optimize concealment of nuclear forces.
Nevertheless, without more detail these sorts of counterarguments seem highly contingent to me: they depend on the state of technology that actually gets deployed, and there may be decisive counter-counter arguments based on other military capabilities.
It would be interesting to see more EAs "grok" these sorts of arguments and to think through corresponding nuclear modernization and arms control strategies that would be implied by trying to assure a desirable long-term future. I've tended to think more redundant and accurate arsenals of low-yield weapons like neutron bombs would tend to strengthen deterrence while eliminating most of the risk of nuclear winter and long-term radiation, but it's easy to imagine there might be better directions yet to ratchet competition and negotiation since that kind of proposal could also be very destabilizing!
I am not sure we should focus more into this area, I just want to make sure that in general, people who go into policy or advocacy don't propagate bad ideas, or discredit EA with important people who would otherwise be aligned with our goals in the future.
I do think that knowing the history of transformative technologies (and policies that effected how they were deployed) will have a lot of instrumental value for EAs trying to make good decisions about things like gene editing and AI.
You seem to be missing the part where most people are disagreeing with the post in significant ways.
F-15s and MRAPs still have to be operated by multiple people, which requires incentive alignment between many parties. Some autonomous weapons in the future may be able to self-sustain and repair, (or be a part of a self-sustaining autonomous ecosystem) which would mean that they can be used while being aligned with fewer people's interests.
A man-at arms wouldn't be able to take out a whole town by himself if more than a few peasants coordinate with pitchforks, but depending on how LAWS are developed, a very small group of people could dominate the world.
I actually agree with a lot of your arguments, but I don't agree overall. AI weapons will be good and bad in many ways, and if the are good or bad overall depends on who has control, how well they are made, and the dynamics of how different countries race and adapt.
That is the point.
The reason it is appropriate to call this ethical reaction time, rather than just reaction time is because the focus of planning and optimization is around ethics and future goals. To react quickly with respect to an opportunity that is hard to notice, you have to be looking for it.
Technical reaction time is a better name in some ways, but it implies too narrow of a focus, while just reaction time implies too wide of a focus. There probably is a better name though.
I just added some examples to make it a bit more concrete.
I think you may be misunderstanding what I mean by ethical reaction time, but I may change the post to reduce the confusion. I think adding examples would make the post a lot more valuable.
Basically, what I mean by ethical reaction time is just being able to make ethical choices as circumstances unfold and not be caught unable to act, or acting in a bad way.
Here’s a few examples, some hypothetical, some actual:
Policy:
One can imagine the Reach Every Mother and Child Act might have passed last year if a few congressmen were more responsive in adjusting it to get past partisan objections (left wing opposing funding religious groups, right wing opposing funding contraception). That likely would have saved a few thousand lives, and possibly millions according to USAID. (http://effective-altruism.com/ea/pk/how_to_support_and_improve_the_reach_every_mother/) My model of the political constraints on the Reach Act may be wrong here though.
Any regulation of technology that starts occurring in response to the technology rapidly coming into existence: Uber executed its plans faster than it could be banned, which was probably good. We don’t necessarily want the same to be true for certain tech risks in AI and biology, which makes it important to figure out quickly the correct way to regulate things.
Anything on the U.S. Federal Register that is going poorly: if you don’t notice a new rule come up relevant to your area of interest (easy to imagine this with animal welfare and new tech) and respond within the comment period, your concerns aren’t going to inform the regulation (if you put in relevant research, and get ignored, you can sue the federal agency and win: this happened when the FDA first failed to ban trans fat). This is also a sort of situation that may actually require you to do research under a lot of time pressure.
Donor coordination:
Start-ups:
Salary negotiations:
Grants/research opportunities:
Grant opportunities = competitive = OODA loop. Less true when there is a deadline that is far away however.
In 2015, there were several EA organizations that had the opportunity to get free research from grad students who were interested in Effective Altruism from the School of Public Policy at the University of Maryland. In order to get free research, an organization would have had to submit a general/rough research proposal (<1 page), in which they could enumerate some of the means by which a study or literature review would be undertaken to maintain rigor/ guidelines for the advisor monitoring grad students. No one was able to react within the month after solicitation, so other non-EA organizations got free research instead for the grad student projects class. It does seem reasonable that EA orgs may have had better priorities, and that there is reason to be skeptical of the grad students, but it would have been a good way to get a bunch of students going into policy more bought into EA even if they didn’t produce work at a level we’d accept. This is also partly my fault, since I could have informed groups earlier, though not by a lot.
Handling the throughput vs. responsiveness trade off:
Time sensitive opportunities:
This is the referenced program:
http://www.bia.gov/WhoWeAre/BIA/OIS/HumanServices/HousingImprovementProgram/
Sorry for taking so long to respond. This is the comment:
https://drive.google.com/file/d/0BzKyV3bimCTiWDhreW91Tm9RT1E/view?usp=sharing