There is a low cost to signing the petition, so no harm in doing so.
However a petition will have minimal upside too.
No MP will be surprised to know that some people are in favour of maintaining the 0.7%, but they will largely imagine those people to lefties who would never vote for a Conservative MP anyway.
Emails to your MP are more valuable because they help to bring you, an aid supporter, to life.
Thanks Matt. One of our team is in close contact with Oxfam. Thank you.
Thanks for your message sindirella.
Our approach came about as a result of conversations with people who know generally what works best in influencing lawmakers/lobbying, and specifically in the UK.
Agreed with alexrjl re opinion polls. Implementing a poll/survey is straightforward for us (I used to run a research team when I was a strategy consultant). The reason we’re not doing it is that our discussions with experts suggest that there is not much value in doing this.
Great question! We want to do this, but there are a few practicalities we are working through. Also I think your experience would be really valuable for us—I’ll ping you a message.
Thanks for the suggestion.
We reached out to that MP and several other MPs and parliamentarians in the days immediately after the announcement, and are also in conversation with several NGOs active in this space, and other groups.
Thanks for asking OHR. One idea is set out in the comment which starts “Thank you very much Will K and Id25 for asking how you can help.”
However a group of us have had our first meeting and in practice we have all been thinking through the connections and communities we belong to and working out ways to activate and work with them.
If anyone has the capacity to help, it would be great to have you involved. Ping me an email on sanjay_joshi { a t } hotmail.co.uk.
Thank you very much Will K and Id25 for asking how you can help.
Based on the conversations I’ve had with people thus far, I think the gap is for organisers/liaisers. I.e.
we will run some social media ads
most ads viewers will do nothing, some of the ad viewers will send an email to their MP (as requested), some will want to engage more
For those who want to engage more, we’ll need people to talk with them—these are the organisers/liaisers. We don’t know yet how many of these people will be needed.
At the time I wrote this post, I thought there might be a gap for analysis, but I suspect that gap might not exist after all.
If you are interested in helping out, please send me a message via the EA Forum or directly to sanjay_joshi { a t } hotmail.co.uk
Thanks very much!
Timeline—fairly urgent. There will be a bill going to parliament to change the law, and I don’t think anyone knows exactly when that will be, but it can’t be this side of Christmas (nothing works that quickly) and it will probably be before April (which is when the financial year starts). Given that they want it to go through and may anticipate opposition, I would guess late January.
Plan: which Tory MPs are relevant: for those which are bound to follow the whip (either because they always follow the whip, or because they are dead against international development) we don’t touch them—there’s no point. For those who are more on the fence, probably still little value, as the whip is probably fairly strong (I haven’t investigated that last claim very closely, so if anyone has opposing opinions I would be interested to hear them). For those who are against, but who might only abstain rather than rebel (which is what mostly happened when the Conservative party wanted the right to break international law), influencing them to rebel instead of abstain will help. The ask: I think we have two asks: (1) vote against reducing the 0.7% (2) An amendment to the bill so that if it does go ahead, it is written into the Bill that it should be temperary (which is what Rishi said anyway). Budget: as we’re using google/facebook ads (and not hiring people) there aren’t any “chunked-up” elements of spend—it’s all smoothly spendable. In other words, the more the merrier. If we have only a few thousand, we can use it. If we have a bit more or a lot more, we can use it.
Will the government win: I have discussed this with a few people and heard differing opinions. I don’t have a strong opinion on how likely this is.
Lessons from previous campaigns: I haven’t studied previous campaigns, but I’ve spoken to some NGOs working in this space and the thinking that they have outlined is pretty similar to the plan I set out above. So their implicit learning from previous campaigns is supportive
I don’t think they do. I seem to remember that this topic was debated some time back and GiveWell clarified their view that they don’t see it this way, but rather they just consider the immediate impact of saving a life as an intrinsic good. (although I would be more confident claiming that this is a fair representation of GiveWell’s views if I could find the place where they said this, and I can’t remember where it is, so apologies if I’m misremembering)
How I think of the impact of saving a life (by donating to the likes of AMF):
a life is saved, and the grief caused by that death is averted
the person whose life is saved lives the rest of their life
Total fertility rates reduce because of lower child mortality
In terms of total number of lives lived, the saving-lives effect and the reducing-fertility rates effect probably roughly cancel each other out in places were the current fertility is high (source: David Roodman on GiveWell blog)
So saving the life helps us, one life at a time, to transition to a world where people have fewer children and are able to invest more in each of them (and averts plenty of bereavement grief along the way)
I am glad you are seriously considering the implications of your philosophical beliefs—this is laudable. I very much hope you don’t conclude it’s bad to save children’s lives.
Sorry if I misunderstood, but does this rest on the assumption that farmed animal welfare is net negative? More on this here: http://interestingthingsiveread.blogspot.com/2018/12/veganism-may-be-net-negative-but-we.html
I’ve tried using gather town, and it’s fine except for the minor detail that the tech often fails! Another platform called mingle space seems to have enough of the same good features, and seems to work more robustly.
If it’s not too self-serving for me to mention this, the mental health chatbot that I run is in need of volunteers: more info here https://forum.effectivealtruism.org/posts/yWGaezWTuPY6LcJ4f/tio-a-mental-health-chatbot
I also run SoGive, an organisation with an exciting mission to expand our analysis to a broad range of charities. We need help with updating our website, so coders, especially those with frontend experience, would be great!
Thanks very much Kris, I’m very pleased that you’re interested in this enough to write these comments.
And as you’re pointing out, I didn’t respond to your earlier point about talking about the evidence base for an entire approach, as opposed to (e.g.) an approach applied to a specific diagnosis.
The claim that the “evidence base for CBT” is stronger than the “evidence base for Rogerian therapy” came from psychologists/psychiatrists who were using a bit of a shorthand—i.e. I think they really mean something like “if we look at the evidence base for CBT as applied to X for lots of values of X, compared to the evidence base for Rogerian therapy as applied to X for lots of values of X, the evidence base for the latter is more likely to have gaps for lots of values of X, and more likely to have poorer quality evidence if it’s not totally missing”.
It’s worth noting that while the current assessment mechanism is the question described in Appendix 1f, this is, as alluded to, not the only question that could be asked, and it’s also possible for the bot to incorporate other standard assessment approaches (PHQ9, GAD7, or whatever) and adapt accordingly.
Having said that, I’d say that this on its own doesn’t feel revolutionary to me. What really does seem revolutionary is that, with the right scale, I might be able to say: This client said XYZ to me, if I had responded with ABC or DEF, which of those would have given me a better response, and be able to test something as granular as that and get a non-tiny sample size.
Thank you for your comment Kris.
I’m unclear why you are hesitant about the claim of the potential to revolutionise the psychology evidence base. I wonder if you perhaps inadvertently used a strawman of my argument by only reading the section which you quoted? This was not intended to support the claim about the bot’s potential to revolutionise the psychology evidence base.
Instead, it might be more helpful to refer to Appendix 2; I include a heavily abbreviated version here:
The source for much of this section is conversations with existing professional psychiatrists/psychologists.
Currently some psychological interventions are substantially better evidenced than others.
<SNIP>
Part of the aim of this project is to address this in two ways:
(1) Providing a uniform intervention that can be assessed at scale
<SNIP>
(2) Allowing an experimental/scientific approach which could provide an evidence base for therapists
<SNIP>
Crucially, TIO is fundamentally different from other mental health apps—it has a free-form conversational interface, similar to an actual conversation (unlike other apps which either don’t have any conversational interface at all, or have a fairly restricted/”guided” conversational capability). This means that TIO is uniquely well-positioned to achieve this goal.
To expand on item (2), the idea is that when I, as someone who speaks to people in a therapeutic capacity, choose to say one thing (as opposed to another thing) there is no granular evidence about that specific thing I said. This feels all the more salient when being trained or training others, and dissecting the specific things said in a training role play. These discussions largely operate in an evidence vacuum.
The professionals that I’ve spoken to thus far have not yet been able to point me to evidence as granular as this.
If you know of any such evidence, please do let me know—it might help me to spend less time on this project, and I would also find that evidence very useful.
Thank you very much for taking the time to have a look at this.
(1) For links to the bot, I recommend having a look at the end of Appendix 1a, where I provide links to the bot, but also explain that people who aren’t feeling low tend not to behave like real users, so it might be easier to look at one of the videos/recordings that we’ve made, which show some fictional conversations which are more realistic.
(2) Re retention, we have deliberately avoided measuring this, because we haven’t thought through whether that would count as being creepy with users’ data. We’ve also inherited some caution from my Samaritans experience, where we worry about “dependency” (i.e. people reusing the service so often that it almost becomes an addiction). So we have deliberately not tried to encourage reuse, nor measured how often it happens. We do however know that at least some users mention that they will bookmark the site and come back and reuse it. Given the lack of data, the model is pretty cautious in its assumptions—only 1.5% of users are assumed to reuse the site; everyone else is assumed to use it only once. Also, those users are not assumed to have a better experience, which is also conservative.
I believe your comments about hypotheticals and “this will be the next facebook” are based on a misunderstanding. This model is not based on the “hypothetical” scenario of people using the bot, it’s based on the scenario of people using the bot *in the same way the previous 10,000+ users have used the bot*. Thus far we have sourced users through a combination of free and paid-for Google ads, and, as described in Appendix 4a, the assumptions in the model are based on this past experience, adjusted for our expectations of how this will change in the future. The model gives no credit to the other ways that we might source users in the future (e.g. maybe we will aim for better retention, maybe we will source users from other referrals) -- those would be hypothetical scenarios, and since I had no data to base those off, I didn’t model them.
(3) I see that there is some confusion about the model, so I’ve added some links in the model to appendix 4a, so that it’s easier for people viewing the model to know where to look to find the explanations.
To respond to the specific points, the worst case scenario does *not* assume that the effect lasts 0.5 years. The worst case scenario assumes that the effect lasts a fraction of day (i.e. a matter of hours) for exactly 99.9% of users. For the remaining 0.1% of users, they are assumed to like it enough to reuse it for about a couple of weeks and then lose interest.
I very much appreciate you taking the time to have a look and provide comments. So sorry for the misunderstandings, let’s hope I’ve now made the model clear enough that future readers are able to follow it better.
Thank you to Alex for writing this piece, which I think is really helpful.
I am a Founder and Director of SoGive. We support donors to achieve more impact, and we influence c£1m per annum, the majority of which is from a very small number of major donors.
In this comment, I will say that I think the thrust of Alex’s concerns are valid and still stand, to my mind. But first:
I want to take my hat off to the guys at Giving Green.
My first tentative forays into getting SoGive going were as early as 2015 and the official start date was 2017, so it’s taken a long time to get to where we are. By contrast Giving Green has achieved a much higher profile than we have, and they’ve achieved it quickly. I would also say that Giving Green’s analytical capabilities are ahead of where we were in 2016. Furthermore, the team is still only working on Giving Green in their spare time, so their progress is impressive.
While achieving traction quickly is great, I question whether Giving Green has achieved their traction too quickly.
For the first several years of our existence, SoGive’s recommendations were solely borrowed from other better-resourced organisations like GiveWell, and we’re only now in the process of updating our website to reflect our own analysis.
And of course just because SoGive is doing things one way, it doesn’t mean that that way is right. But there are reasons for our cautious approach.
I believe it is premature for Giving Green to put equal emphasis on recommendations where there is an EA consensus (like CATF) and recommendations where Giving Green is going out on a limb (like TSM).
I have had a small number of conversations with the Giving Green team now, and I think they are good guys who could create a good analytical organisation given time.
And on some of the points that Dan made in this thread, I have sympathies with his position. For example, on Climeworks, he made the point that “you are betting on the technology, not the company”. Contra Alex, I think this is a reasonable argument in favour of the claim that one of the Metaculus forecasts is not analytically helpful. (although doesn’t support Dan’s claim that both are irrelevant)
Having said that, the majority of Alex’s concerns still stand, to my mind.
Furthermore, I have read some of the Giving Green analysis, and believe that Alex’s list of concerns would be longer, if only there were time to do a more detailed review.
I’m conscious that reading much of this thread may feel punishing for the Giving Green team. However I really am positive about the long-term potential for this project.