Physics graduate and currently Data Analyst. Enthusiastic about Complexity Science
Overall, this seems like a weak criticism worded strongly. It looks like the opposition here is more to the moniker of Complexity Science and its false claims of novelty but not actually to the study of the phenomenon that fall within the Complexity Science umbrella. This is analogous to a critique of Machine Learning that reads “ML is just a rebranding of Statistics”. Although I agree that it is not novel and there is quite a bit of vagueness in the field, I disagree on the point that Complexity Science has not made progress.
I think the biggest utility of Complexity Science comes in breaking disciplinary silos. Rebranding things to Complexity Science, just brings all the ideas on systems from different disciplines together under one roof. If you are a student, you can learn all these phenomena in one course or degree. If you are a professor, you can work on anything that relates to Complex Systems phenomena if you are in a Complexity department. The flip side of it is, you might end up living in a world of hammers without nails—you would just have a bunch of tools without a strong domain knowledge in any of the systems that you are studying.
My take on Complexity Science is that it is a set of tools to be used in the right context. For your specific context, some or none of the tools of Complexity Science can be useful. Where Complexity Science falls apart for me is when it tries to lose all context and generalize to all systems. I think the OP here is trying to stay within context. The post is just saying we can build ABMs to approach some specific EA cause areas. So I am more or less onboard with this post.
On a final note, I am in agreement with your critique on abuse of Power Laws. There are too many people that just make a log-log plot, look at the line and exclaim “Power law!”. The Clauset-Shalizi-Newman paper you linked to is the citation classic here. For those who do network theory, instead of trying to prove your degree distribution is a power law, I would recommend doing Graphlet Analysis.
Thanks a lot for posting this! I also have the same feeling as finm in that I wanted to write something like this. But even if I had written it wouldn’t have been as extensive as this one is. Wonderfully done!
To add to the pool of resources that the post has already linked to:
You can meet other people interested in Complexity Science/Systems Thinking here: https://www.complexityweekend.com/ It is a wonderful community with a good mix of rookies and experts. So even if you are new to Complexity you should feel free to join in. I participated in their latest event and got a lot out of it (specifically on making ABMs with MESA)
If you want a simple (also free!) intro to Complexity, I would recommend Introduction to the Modeling and Analysis of Complex Systems by Hiroki Sayama. If you go to the Wikipedia page on Complex System, the picture in it that portrays several subfields of Complexity was taken from this book. It should be easy to follow if you just know high school math. I have personally only read the Networks part of it (since that is the subfield of Complexity I have mostly worked on) and it was good enough to get my feet wet.
The very vague definition of “Cause Area” is making it hard for me to think about meta EA. It feels like GPR is a cause area and so working on it would be direct impact work but I am not sure. Same goes for EA Movement building. Also, it starts getting trippy if we claim meta-EA is also a cause area!
Maybe we can clarify the definition for cause area within this meta EA framework?
Specifics matter. There can be no one discussion norm to get people to be nice to each other.
I think things like discussion norms are highly contextual. The platform in which the discussion is happening, the point being discussed, the people who are involved in the discussion are some of the many factors that could end up mattering. Given these factors, transporting discussion norms from one virtual place to another might not be the right way to think about it.
I think the “EA-like” discussion norm is a function of several things. In addition to the factors mentioned above, the concept of EA itself seems to ask for people to be uncertain and humble.
Consider the following thought experiment—say you took all the same people from the EA Forum and put them all in a Facebook group. Do you think the “EA-like” discussion norms currently here would be maintained? Or imagine putting them all in a forum, not about EA or Philosophy or Sciency stuff. What would happen?
Thanks for this wonderful article! I absolutely agree that it would be highly beneficial to have a community that is at the intersection of EA and Complexity. I recently participated in an event, where I actually found several other EAs interested in Complexity but unfortunately I couldn’t spend enough time to network with them further (I got involved in another project there).
I have also been thinking about how we may use the tools of Complexity to make EA better although I haven’t been able to concretely land on anything. Here are some vague thoughts I have. I am not entirely sure if any of these thoughts are worth pursuing so tug at these threads at your own peril!:
I wonder if there is a possibility of creating an Agent-Based Model to understand Global Catastrophic Risks although I am not entirely sure how to go about doing this. This talk by Luisa Rodriguez here might be a good place to start. She is not building an ABM (atleast going by what she said in that talk) but the way she talks about it made me feel like an ABM could help.
Complexity has some roots in Philosophy (A quick Google search took me here). I wonder how the philosophy of EA and that of Complexity would work together.
I wonder if we can deal with flow-through effects better if we had a Complex Systems view. Is this a network shaped problem?
But these are all mostly at a ‘wondering-if’ stage and one would definitely need help from cleverer people to actually start some concrete work. So having a community around EA & Complexity would be highly beneficial.
Is a recording of this event available?
Thanks for linking to the podcast! I hadn’t listened to this one before and ended up listening to the whole thing and learnt quite a bit.
I just wonder if Ben actually had some other means in mind other than evidence and reasoning though. Do we happen to know what he might be referencing here? I recognize it could just be him being humble and feeling that future generations could come up with something better (like awesome crystal balls :-p). But just in case if something else is actually already there other than evidence and reason I find it really important to know.
I both agree and disagree with you.
I agree that the ambiguity in whether giving in a hits-based way or evidence-based way is better, is an important aspect of current EA understanding. In fact, I think this could be a potential 4th point (I mentioned a third one earlier) to add to the definition desiderata: The definition should hint at the uncertainty that is in current EA understanding.
I also agree that my definition doesn’t bring out this ambiguity. I am afraid it might even be doing the opposite! The general consensus is that both experimental & theoretical parts of the natural sciences are equally important and must be done. But I guess EAs are actually unsure if the evidence-based giving & careful reasoning-based giving (hits based) should both be done or if we would be doing more good by just focussing on one. I should possibly read up more on this. (I would appreciate it if any of you can DM me any resources you have found on this) I just assumed EAs believed both must be done. My bad!
I don’t see how Will’s definition allows for debating said ambiguity though. As I mentioned in my earlier comment, I don’t think that the definition distinguishes between the two schools of thought enough. As a consequence, I also don’t think it shows the ambiguity between them. I believe a conflict(aka ambiguity) requires at least two things but the definition actually doesn’t convincingly show there are two things in the first place, in my opinion.
Thanks for bringing up Will’s post! I have now updated the question’s description to link to that.
I actually like Will’s definition more. The reason is two-fold:
Will’s definition adds a bit more mystery which makes me curious to actually work out what all the words mean. In fact, I would add this to the list of “principal desiderata for the definition” the post mentions: The definition should encourage people to think about EA a bit deeply. It should be a good starting point for research.
Will’s definition is not radically different from what is already there—the post says “little more rigorous”—which makes the cost of changing to this definition lesser. (One of the costs of changing something as fundamental as the definition could be giving the perception to the community that somehow there has been a significant change in the foundations of EA when hasn’t been any—we are just trying to better reflect what is actually done in EA).
One critique I have of Will’s alternative is that the proposed definition isn’t quite distinguishing the two schools of thought. To explain my thinking here is a bit more visual representation. Let () represent a bucket:
Will’s definition and existing definition makes things feel like - (Evidence, Careful reasoning) - it is just one bucket
But it should really feel like - (Evidence), (Careful reasoning) - two separate buckets
Apologies if that is too nitpicky but I don’t think it is. I think the distinctness of Evidence and Careful reasoning needs to come out. I guess rephrasing it this way would be better: Effective altruism attempts to improve the world by the use of experimental evidence and/or theoretical reasoning to work out how to maximize the good with a given unit of resources, tentatively understanding ‘the good’ in impartial welfarist terms.This rephrasing is inspired by the fact that many of the natural sciences split into two—theory and experiment (like Theoretical Physics and Experimental Physics). We are saying EA is also that way which I think it is. I think this also adds to the Science-aligned point that Will mentions. (I have edited this to say that I don’t think this definition is a good one. See my next comment below)
The point about “working through what it really means” is very interesting. (more on this below) But when I read, “high-quality evidence and careful reasoning”, it doesn’t really engage the curious part of my brain to work out what that really means. All of those are words I have already heard and it feels like standard phrasing. When one isn’t encouraged to actually work through that definition, it does feel like it is excluding high variance strategies. I am not sure if you feel this way but “high-quality evidence” to my brain just says empirical evidence. Maybe that is why I am sensing this exclusion of high variance strategies.
You are probably right. But I am worried if that is really a good strategy? By not openly saying that we do things we are uncertain about we could end up coming off as a know-it-all who has it all figured out with evidence! There were some discussions along these lines in another recent post. Maybe having a definition that kind of gives a subtle nod to hits-based giving could help with that?
Your point about ‘working through the definition’ actually gave me an idea: What if we rephrased to “high-quality evidence and/or careful reasoning”. That non-standard phrasing of ‘and/or’ sows some curiosity to actually work things out, doesn’t it? I am making the assumption that the phrase “high-quality evidence” is empirical evidence (as I already said) and the phrase “careful reasoning” includes Expected Value thinking, making Fermi estimates and all the other reasoning tools that EAs use. Also, this small phrasing change is not that radically different from what we already have so the cost of changing shouldn’t be that high. Of course the question is, is it actually that much more effective than what we have. Would love to hear thoughts on that and of course other suggestions for a better definition...
For evaluating the definition of EA we would only want people who don’t know much about EA. So we would need a focus group of EA newcomers and ask them what the definition means to them. Does that sound right?
Consider this—say the EA figured out the number of people the problem could affect negatively (i.e) the scale. Then even if there is a small probability that the EA could make a difference shouldn’t they have just taken it? Also even if the EA couldn’t avert the crisis despite their best attempts they still get career capital, right?
Another point to consider—IMHO, EA ideas have a certain dynamic of going against the grain. It challenged the established practices of charitable giving that existed for a long time. So an EA might be inspired by this and indeed go against the established central bank theory to work on a more neglected idea. In fact, there is at least some anecdotal evidence to believe that not enough people critique the Fed. So it is quite neglected.
″… I believe personal features (like fit and comparative advantages) would likely trump other considerations...” That is a very interesting point. Sometimes I do have a very similar feeling—the other 3 criteria are there mostly just so one doesn’t base one’s decision fully on personal fit but consider other things too. At the end of the day, I guess the personal fit consideration ends up weighing a lot more for a lot of people. Would love to hear from someone in 80k hours if this is wrong...
Editing to add this:
I wonder if there is a survey somewhere out there that asked people how much do they weigh each of the 4 factors. That might validate this speculation...
Thanks for linking to that OpenPhil page! It is really interesting. In fact, one of the pages that page links to talks about ABMs that rory_greig mentioned in his comment.
As someone interested in Complexity Science I find the ABM point very appealing. For those of you with a further interest in this, I would highly recommend this paper by Richard Bookstaber as a place to start. He also wrote a book on this topic and was also one of the people to foreshadow the crisis.
Also if you are interested in Complexity Science but never got a chance to interact with people from the field/learn more about it, I would recommend signing up for this event.
Sorry for digging up this old post. But it was mentioned in the Jan 2021 EA forum Prize report published today and that is how I got here.
This comment assumes that Cause Prioritization (CP) is a cause area that requires people with width(worked across different cause areas) rather than depth(worked on a single cause area) of knowledge. That is, they need to know something about several cause areas instead of deeply understanding one of them. Would love to hear from CP researchers or others who would disagree.
Maybe CP is an excellent path for some people in mid/late career. I think there could be some people in the middle of their career who have width rather than depth of knowledge. I might be wrong but it feels like the current advice for mid-career folks from 80k hours (See this 80k hours podcast episode discussion for example) seems to focus on people with skill depth alone. Further, I also think 80k hours may actually be creating people who have skill width by encouraging people to experiment with working on different cause areas until they find the best personal fit. What if we could tell them—“Experimented a lot? Have a lot of width? Try CP!”
I also feel like it would be difficult for people in their early career to rationalize working on CP. Personally, as someone in their early career, I feel like I don’t fully understand even one of the cause areas of interest to EAs properly. How can I then hope to understand multiples of them, find those not yet unknown and on top of it prioritize them all!? Now, there is good reason to believe EA is a relatively young movement (majority age between 25-34) and since young people can’t rationalize working on CP, we are seeing relatively lesser research on this.
Maybe as EAs grow older eventually CP research will gain steam. Maybe their depth could also give them some width. At a later stage, current EAs working on a specific cause area could feel, “Having done specialized work all these years, I am beginning to see some ways I can generalize this stuff. Maybe this generalization is the next big impactful thing I can do” and then get into CP. Maybe some EAs already realized this and have even planned their career so that they can do CP at a later stage. So this whole thing could just be a matter of time. But that doesn’t mean we should not worry—what if at the stage when EAs want to generalize we don’t have the structures in place for them to pursue it?
May I suggest that you also name people who strongly identify with the ideas of some of these organizations? For instance, 64,620 hourists; Glomars; Dr.Phils; The InCredibles (CrediblyGood);
Also if FHI is Bostrom’s squad then they should rename their currently boringly named “Research Areas” page to Squad Goals.
Happy April Fools! :-)
Hi tamgent! Thanks for the suggestion. I have edited the post to add my thoughts on relevance to EA. I am no expert at cause prioritization, so I have tried my best to make an argument. Would love to hear your thoughts.