That’s a normal part of life :). Any things that I decide to do in a future day, I’ll copy/paste to over there, but I usually won’t delete the items from the checklist for the day where I didn’t complete them (thereby creating a record of things I expected or hoped to do, but didn’t).
For instance, at https://github.com/vipulnaik/daily-updates/issues/54 I have two undone items.
There is some related stuff by Carl Shulman here: https://www.greaterwrong.com/posts/QSHwKqyY4GAXKi9tX/a-personal-history-of-involvement-with-effective-altruism#comment-h9YpvcjaLxpr4hd22 that largely agrees with what I said.
My understanding is that Against Malaria Foundation is a relatively small player in the space of ending malaria, and it’s not clear the funders who wish to make a significant dent in malaria would choose to donate to AMF.
One of the reasons GiveWell chose AMF is that there’s a clear marginal value of small donation amounts in AMF’s operational model—with a few extra million dollars they can finance bednet distribution in another region. It’s not necessarily that AMF itself is the most effective charity to donate to to end malaria—it’s just the one with the best proven cost-effectiveness for donors at the scale of a few million dollars. But it isn’t necessarily the best opportunity for somebody with much larger amounts of money who wants to end malaria.
In its ~15-year existence, the Global Fund says it has disbursed over $10 billion for malaria and states that 795 million insecticide-treated nets were funded (though it’s not clear if these were actually funded all through the 10 billion disbursed by the Global Fund). It looks like their annual malaria spend is a little under a billion. See https://www.theglobalfund.org/en/portfolio/ for more. The Global Fund gets a lot of its funding from governments; see https://timelines.issarice.com/wiki/Timeline_of_The_Global_Fund_to_Fight_AIDS%2C_Tuberculosis_and_Malaria for more on their history and programs.
The Gates Foundation spends ~$3.5 billion annually, of which $150-450 million every year is on malaria. See https://donations.vipulnaik.com/donor.php?donor=Bill+and+Melinda+Gates+Foundation#donorDonationAmountsBySubcauseAreaAndYear and https://donations.vipulnaik.com/donor.php?donor=Bill+and+Melinda+Gates+Foundation&cause_area_filter=Global+health%2Fmalaria#donorDonationAmountsByDoneeAndYear They’re again donating to organizations with larger backbones and longer histories (like PATH, which has over 10,000 people, and has been around since 1978), that can absorb large amounts of funding, and the Gates Foundation seems more cash-constrained than opportunity-constrained.
The main difference I can make out between the EA/GiveWell-sphere and the general global health community is that malaria interventions (specifically ITNs) get much more importance in the EA/GiveWell-sphere, whereas in the general global health spending space, AIDS gets more importance. I’ve written about this before: http://effective-altruism.com/ea/1f9/the_aidsmalaria_puzzle_bleg/
I tried looking in the IRS Form 990 dataset on Amazon S3, specifically searching the text files for forms published in 2017 and 2016.
I found no match for (case-insensitive) openai (other than one organization that was clearly different, its name had openair in it). Searching (case-insensitive) “open ai” gave matches that all had “open air” or “open aid” in them. So, it seems like either they have a really weird legal name or their Form 990 has not yet been released. Googling didn’t reveal any articles of incorporation or legal name.
In my experience, writing full-fledged, thoroughly researched material is pretty time-consuming, and if you push that out to the audience immediately, (1) you’ve sunk a lot of time and effort that the audience may not appreciate or care about, and (2) you might have too large an inferential gap with the audience for them to meaningfully engage.
The alternative I’ve been toying with is something like this: when I’m roughly halfway through an investigation, I publish a short post that describes my tentative conclusions, without fully rigorous backing, but with (a) clearly stated conclusions, and (b) enough citations and other signals that there’s decent research backing my process. Then I ask people what they think of the thesis, which parts they are interested in, and what they are skeptical of. Then after I finish the rest of the investigation I push a polished writeup only for those parts (for the rest, it’s just informal notes + general pointers).
For examples, see https://www.lesserwrong.com/posts/ghBZDavgywxXeqWSe/wikipedia-pageviews-still-in-decline and http://effective-altruism.com/ea/1f9/the_aidsmalaria_puzzle_bleg/ (both are just the first respective steps for their projects).
I feel like this both makes comments more valuable to me and gives more incentive to commenters to share their thoughts, but the jury is still out.
FWIW, my impression is that data on Wikipedia has gotten somewhat more accurate over time, due to the push for more citations, though I think much of this effect occurred before the decline started. I think the push for accuracy has traded off a lot against growth of content (both growth in number of pages and growth in amount of data on each page). These are crude impressions (I’ve read some relevant research but don’t have strong reason to believe that should be decisive in this evaluation) but I’m curious to hear what specific impressions you have that are contrary to this.
If you have more fine-grained data at your disposal on different topics and how much each has grown or shrunk in terms of number of pages, data available on each page, and accuracy, please share :).
In the case of Wikipedia, I think the aspects of quality that correlate most with explaining pageviews are readily proxied by quantity. Specifically, the main quality factors in people reading a Wikipedia page are (a) the existence of the page (!), (b) whether the page has the stuff they were looking for. I proxied the first by number of pages, and the second by length of the pages that already existed. Admittedly, there are a lot more subtleties to quality measurement (which I can go into in depth at some other point) some of which can have indirect, long-term effects on pageviews, but on most of these dimensions Wikipedia hasn’t declined in the last few years (though I think it has grown more slowly than it would with a less dysfunctional mod culture, and arguably too slowly to keep pace with the competition).
Great point. As somebody who has been in the crosshairs of Wikipedia mods (see ANI) my bias would push me to agree :). However, despite what I see as problems with Wikipedia mod culture, it remains true that Wikipedia has grown quite a bit, both in number of articles and length of already existing articles, over the time period when pageviews declined. I suspect the culture is probably a factor in that it represents an opportunity cost: a better culture might have led to an (even) better Wikipedia that would not have declined in pageviews so much, but I don’t think the mod culture led to a quality decline per se. In other words, I don’t think the mechanism:counterproductive mod culture → quality decline → pageview declineis feasible.
Great points. As I noted in the post, search and social media are the two most likely proximal mechanisms of causation for the part of the decline that’s real. But neither may represent the “ultimate” cause: the growth of alternate content sources, or better marketing by them, or changes in user habits, might be what’s driving the changes in social media and search traffic patterns (in the sense that the reason Google’s showing different results, or Facebook is making some content easier to share, is itself driven by some combination of what’s out there and what users want).
The main challenge with search engine ranking data is that (a) the APIs forbid downloading the data en masse across many search terms, and (b) getting historical data is difficult. Some SEO companies offer historical data, but based on research Issa and I did last year, we’d have to pay a decent amount to even be able to see if the data they have is helpful to us, and it may very well not be.
The problem with Google Trends is that (a) it does a lot of normalization (it normalizes search volume relative to total search volume at the time), which makes it tricky to interpret data over time, and (b) it’s hard to download data en masse. Also, a lot of Google Trends results are just amusingly weird, e.g. https://trends.google.com/trends/explore?date=all&q=Facebook (see https://www.facebook.com/vipulnaik.r/posts/10208985033078964 for more discussion)-- are we really to believe that interest in Facebook spiked in October 2012, and that it has returned in 2017 (after a 5-year decline) to what it used to be back in 2009? Google Trends is just yet another messy data series that I would have to acquire expertise in the nuances of, not a reliable beacon of truth against which Wikipedia data can be compared.
The one external data source I have been able to collect with reasonable reliability is Facebook share counts. At the end of each month, I record Facebook share counts for a number of Wikipedia pages by hitting the Facebook API (a process that takes several days because of Facebook’s rate limiting). Based on this I now have decent time series of cumulative Facebook share counts, such as https://wikipediaviews.org/displayviewsformultiplemonths.php?tag=Colors&allmonths=allmonths-api&language=en&drilldown=cumulative-facebook-shares If I do a more detailed analysis, this data will be important for evaluating the social media hypothesis.
How interested are you in seeing an exploration of the search engine ranking and increased use of social media hypotheses?
This is already an issue: https://github.com/Discordius/Lesswrong2/issues/168
The Wikimedia Foundation has not ignored the decline. For instance, they discuss the overall trends in detail in their quarterly readership metrics reports, the latest of which is at https://commons.wikimedia.org/wiki/File:Wikimedia_Foundation_Readers_metrics_Q4_2016-17_(Apr-Jun_2017).pdf The main difference between what they cover and what I intend to cover are (a) they only cover overall rather than per-page pageviews, (b) they focus more on year-over-year comparisons than long-run trends, (c) related to (b), they don’t discuss the long-run causes. However, these reports are a great way of catching up on incremental overall traffic level updates as well as any analytics or measurement discrepancies that might be driving weird numbers.The challenge of raising more funds with declining traffic has also been noted in fundraiser discussions, such as at https://en.wikipedia.org/wiki/Wikipedia:Wikipedia_Signpost/2015-10-14/News_and_notes which has the quote:
Better performing banners are required to raise a higher budget with declining traffic. We’ll continue testing new banners into the next quarter and sharing highlights as we go.
They still show up in the total comment count :).
For all the talk about the “decline” of LessWrong, total pageviews and sessions to LessWrong have stayed 5-10 times higher than those to the Effective Altruism Forum (the EAF numbers are documented in my post).
The 2017 SSC Survey had 5500 respondents. Presumably this survey was more widely visible and available than mine (which was one link in the middle of a long link list).
Varies heavily by context. Typical alternatives:
(a) Google’s own answers for simple questions.
(b) Transactional websites for search terms that denote possible purchase intent, or other websites that are action-oriented (e.g., Yelp reviews).
(c) More “user-friendly” explanation sites (e.g., for medical terminology, a website that explains it in a more friendly style, or WikiHow)
(d) Subject-specific references (some overlap with (c), but could also include domain Wikias, or other wikis)
(e) When the search term is trending because of a recent news item, then links to the news item (even if the search query itself does not specify the associated news)
Interesting. I suspect that even among verbal elites, there are further splits in the type of consumption. Some people are heavy on reading books since they want a full, cohesive story of what’s happening, whereas others consume information in smaller bits, building pieces of knowledge across different domains. The latter would probably use Wikipedia more.
Similarly, some people like opinion-rich material whereas others want factual summaries more. The factual summary camp probably uses Wikipedia more.
However, I don’t know if there are easy ways of segmenting users, i.e., I don’t know if there are websites or communities that are much more dominated by users who prefer longer content, or users who prefer factual summaries.