Given the time it takes to form relationships with nodes in decision making networks, and the difficulty of reducing uncertainty from the outside, at some point it makes sense to either aim people at such jobs or to make friends with people in them. That lobbying and working in government aren't unique tactics or roles in society doesn't matter if they are neglected by those who are capable of pursuing similar goals: different organizations compete for influence in different directions. Early investment to enable direct interaction with decision making networks can be how you get the "when" right, figure out "who" to target, and sometimes even figure out the "what to improve" by seeing what is going wrong in the first place.
If an outside organization only does outside research and competitors invest more in making internal connections, the competitors gain advantage and influence with time. Even if one gains a more objective perspective by looking in from the outside and avoiding political fights, a lot of the most valuable information for decision making is going to be internal. This failure mode leads to forms of naivety that are persistent: external actors can see things that clearly look like mistakes, by actors with biases that are obvious to outsiders, and then conclude more confidently than is justified that their own views are correct.
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:
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.
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:
Sorry for taking so long to respond.
This is the comment:
Great summary of why I hate when people walk across the road instead of running. Or when people space themselves out instead of clustering so that no cars can get by.