If this is your first time reading about Effective Altruism Policy Analytics, you can look at our website here for more information: https://eapolicy.wordpress.com/


Effective Altruism Policy Analytics has now been working for eight weeks, and is more than halfway through its experimental period. Our overall strategy for finding regulations to comment on has been refined with changes to how we search for feedback, but for the most part we are continuing our initial strategy with more skill and less distractions.

Our current policy comment process typically proceeds as follows:

  • Find proposed rules on Regulations.gov

  • Select rules to comment on after considering difficulty, replaceability, feedback, and potential impact

  • Conduct research on:

    • How the regulation currently works, compared to the proposed rule

    • What the powers the agency has over the rule and how much control they have over the issue area

    • What useful information or support the regulatory agency needs

    • Which experts can assist us

    • Which papers can we learn from and cite

    • What are the best changes to make, and their expected benefits

  • Draft a comment, while seeking assistance, verification, and feedback

  • Revise the comment based on feedback

  • Submit the final comment

  • Send feedback surveys and request further feedback


Biggest changes we have made:


  • Increased specialization and division of labor among team members - Having project members focus their attention on one regulation at a time, and using interns to gather feedback and handle distracting tasks has saved us a lot of time, and allowed us to increase comment length and quality. By going more in-depth, we can address more issues and create more topics for agencies to consider.


  • Changing our feedback system, spending less time contacting more people - When we started the project, it became apparent that waiting for government feedback takes a long time (sometimes over a year), and final rules that guarantee fast feedback only make guarantees because they have already been thoroughly researched and considered. We attempted to use expert feedback as a mechanism for improvement, however our initial attempts involved many emails back and forth with little useful information acquired, despite iterating and changing our methods. After assigning an intern to work exclusively on this problem for roughly a week, we distributed a wide net of emails to many contacts and started getting lots of useful feedback. We are still facing difficulty convincing respondents to fill out the quantitative surveys, as they opt to write their own qualitative opinions of our comments.

  • Focusing more on producing comments than other efforts - Throughout the project, we encountered a lot of tasks that we tried to tackle, like registering as lobbyists, increasing our transparency, and obtaining feedback. Many of these ideas were not immediately actionable, and some were fairly time-consuming. Also, many of these ideas were less important than simply measuring the effectiveness of comments. This project is an experiment, the larger our sample size is, the more useful information we gain about making policy comments. Given how generalized the project already is, it is likely more valuable to focus on the goal of our experiment: to determine if policy comments are a cost effective way for effective altruists to create policy change.


  • Focusing more on increasing the number of points we make in comments - Regulatory agencies address points separately, which allows comments with more points to receive more feedback. As research time is a costly, diving deeper into a given policy which we have become familiar with often has higher returns than searching for entirely new things to comment on.



Progress:

We have been producing about 1 policy comment per week, which is half as much as what we initially estimated. Our initial estimates were based on earlier comments submitted in the fall, and in retrospect it is easy to identify reasons why those comments were produced faster:

 

  • We had full time access to experts - Having Richard Bruns and Matt Dahlhausen free to work in 8 hour blocks on weekends allowed us to solve various knowledge problems very quickly. Matt had specialized knowledge on relevant aspects of both of the proposed rules, and Richard is very good at finding new ways to produce economic effect estimates when the relevant data is not available.

  • There was unaccounted for research time - Matt Dahlhausen had done some of his own research before we worked on comments in the fall. One of our current most time consuming activities is researching regulations to comment on, and figuring out if a comment is justified. There have been many times where we did hours of research and work on producing a comment only to find out a desired change was going to happen anyway, that a change cannot be made for legal reasons, or that an apparent mistake is only represented on Regulations.gov and not the official proposed rule.

  • There was unaccounted passive work - Hours worked were stretched out over multiple weekends, rather than all at once. This allowed us to sleep on ideas longer, and bring them up in discussions at DC Effective Altruism meetups. Having this much passive time allowed us to work very quickly once we had a formal meeting.

Comments


No comments on this post yet.
Be the first to respond.
Curated and popular this week
Garrison
 ·  · 7m read
 · 
This is the full text of a post from "The Obsolete Newsletter," a Substack that I write about the intersection of capitalism, geopolitics, and artificial intelligence. I’m a freelance journalist and the author of a forthcoming book called Obsolete: Power, Profit, and the Race to build Machine Superintelligence. Consider subscribing to stay up to date with my work. Wow. The Wall Street Journal just reported that, "a consortium of investors led by Elon Musk is offering $97.4 billion to buy the nonprofit that controls OpenAI." Technically, they can't actually do that, so I'm going to assume that Musk is trying to buy all of the nonprofit's assets, which include governing control over OpenAI's for-profit, as well as all the profits above the company's profit caps. OpenAI CEO Sam Altman already tweeted, "no thank you but we will buy twitter for $9.74 billion if you want." (Musk, for his part, replied with just the word: "Swindler.") Even if Altman were willing, it's not clear if this bid could even go through. It can probably best be understood as an attempt to throw a wrench in OpenAI's ongoing plan to restructure fully into a for-profit company. To complete the transition, OpenAI needs to compensate its nonprofit for the fair market value of what it is giving up. In October, The Information reported that OpenAI was planning to give the nonprofit at least 25 percent of the new company, at the time, worth $37.5 billion. But in late January, the Financial Times reported that the nonprofit might only receive around $30 billion, "but a final price is yet to be determined." That's still a lot of money, but many experts I've spoken with think it drastically undervalues what the nonprofit is giving up. Musk has sued to block OpenAI's conversion, arguing that he would be irreparably harmed if it went through. But while Musk's suit seems unlikely to succeed, his latest gambit might significantly drive up the price OpenAI has to pay. (My guess is that Altman will still ma
 ·  · 5m read
 · 
When we built a calculator to help meat-eaters offset the animal welfare impact of their diet through donations (like carbon offsets), we didn't expect it to become one of our most effective tools for engaging new donors. In this post we explain how it works, why it seems particularly promising for increasing support for farmed animal charities, and what you can do to support this work if you think it’s worthwhile. In the comments I’ll also share our answers to some frequently asked questions and concerns some people have when thinking about the idea of an ‘animal welfare offset’. Background FarmKind is a donation platform whose mission is to support the animal movement by raising funds from the general public for some of the most effective charities working to fix factory farming. When we built our platform, we directionally estimated how much a donation to each of our recommended charities helps animals, to show users.  This also made it possible for us to calculate how much someone would need to donate to do as much good for farmed animals as their diet harms them – like carbon offsetting, but for animal welfare. So we built it. What we didn’t expect was how much something we built as a side project would capture peoples’ imaginations!  What it is and what it isn’t What it is:  * An engaging tool for bringing to life the idea that there are still ways to help farmed animals even if you’re unable/unwilling to go vegetarian/vegan. * A way to help people get a rough sense of how much they might want to give to do an amount of good that’s commensurate with the harm to farmed animals caused by their diet What it isn’t:  * A perfectly accurate crystal ball to determine how much a given individual would need to donate to exactly offset their diet. See the caveats here to understand why you shouldn’t take this (or any other charity impact estimate) literally. All models are wrong but some are useful. * A flashy piece of software (yet!). It was built as
Omnizoid
 ·  · 9m read
 · 
Crossposted from my blog which many people are saying you should check out!    Imagine that you came across an injured deer on the road. She was in immense pain, perhaps having been mauled by a bear or seriously injured in some other way. Two things are obvious: 1. If you could greatly help her at small cost, you should do so. 2. Her suffering is bad. In such a case, it would be callous to say that the deer’s suffering doesn’t matter because it’s natural. Things can both be natural and bad—malaria certainly is. Crucially, I think in this case we’d see something deeply wrong with a person who thinks that it’s not their problem in any way, that helping the deer is of no value. Intuitively, we recognize that wild animals matter! But if we recognize that wild animals matter, then we have a problem. Because the amount of suffering in nature is absolutely staggering. Richard Dawkins put it well: > The total amount of suffering per year in the natural world is beyond all decent contemplation. During the minute that it takes me to compose this sentence, thousands of animals are being eaten alive, many others are running for their lives, whimpering with fear, others are slowly being devoured from within by rasping parasites, thousands of all kinds are dying of starvation, thirst, and disease. It must be so. If there ever is a time of plenty, this very fact will automatically lead to an increase in the population until the natural state of starvation and misery is restored. In fact, this is a considerable underestimate. Brian Tomasik a while ago estimated the number of wild animals in existence. While there are about 10^10 humans, wild animals are far more numerous. There are around 10 times that many birds, between 10 and 100 times as many mammals, and up to 10,000 times as many both of reptiles and amphibians. Beyond that lie the fish who are shockingly numerous! There are likely around a quadrillion fish—at least thousands, and potentially hundreds of thousands o