One category that you didn't include are people that agree with the ideas and take action, but don't want to or are too busy to attend lots of EA meetups.
One category that you didn't include are people that agree with the ideas and take action, but don't want to or are too busy to attend lots of EA meetups.
Hi David, I think that is actually quite a big factor! I noticed in particular that there are people in our group we think are particularly well-suited for EA but don't have the time and/or energy to engage. These sorts of people agree with the ideas, are motivated to make an impact, and also have sufficient work ethic to do so, but can't make it to the meetings precisely because they're too busy making an impact. I personally think these people are the ones we want to expose to EA ideas, but it is difficult to engage them.
We have a couple different ideas for engaging these sorts of people more:
Executive summary: The author presents a working model of factors that attract or repel university students from Effective Altruism (EA) based on their experience as a co-organizer of an EA student group, aiming to provide insights for community builders to optimize their efforts.
Key points:
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I've been co-organizing an EA student group at Queen's University in Canada for about a year now. When I first joined Queen's Effective Altruism (QEA) on campus as a general member in January of 2023, the club was small (only ~5 active members and the only HEA was the sole organizer). The following semester, when my fellow co-organizer and I inherited the club, we were pleasantly surprised with the number of members joining QEA—we attracted 40 members over the semester, with an active member base of ~17. Everyone seemed pretty interested and engaged, and we managed to send a total of 8 members to various GCPs and EAGs. We noticed some attrition, but not enough to pass it off as anything more than what was expected (in fact, our club collected more members than we lost over the semester.
Then winter semester rolled around. All of the sudden, our active member base fell to ~10, albeit we absorbed some of the previous EA members into exec roles. Our attrition rates were also notably higher: By the end of the semester, some of our introductory fellowship meetings, which initially attracted around 17 attendees, dwindled to just one or two participants.
This difference seemed significant. Of course, there are many expected variables (winter semesters tend to involve higher attrition rates, the recruiting period is less bustling than the fall recruiting period, etc.). But, with the knowledge of the success we had in the fall, the contrast was stark and rather confusing, especially since we put in the same—if not more—effort into the club during the winter semester. Some of the members who we thought would stick, didn't, leading to even more confusion.
I've grown increasingly interested in attrition—namely, questions related to what qualities the people who reject/tend towards EA ideas possess, what factors contribute to attrition, and how to discern whether it reflects issues within our university club or is just noise.
As I've thought about this more, I've come to discern some patterns in attrition, which, I think, hint at the potential for a comprehensive model. Developing mental models has been incredibly helpful for me. When I notice confusion about something with no clear way out—no model to grip onto and make sense of the thing—I succumb to inertia. The same can be said about this model of attrition: The more I cultivated a model, a mental heuristic to employ when considering membership for QEA, the more clarity I've had about thinking about membership, and thus the more confident I felt about taking action according to it. This post aims to go over some of these trends/little things I've noticed that I think are helpful to be aware of as a university community builder.
Note: I've been uncertain about the usefulness of this discussion. Much of what I've noticed might seem self-evident, but maybe this is due to the illusion of transparency or the curse of knowledge fallacy. Regardless, I've concluded that I think there is at least no harm in posting this. At its best, I think this post could open up further discussion about attrition in EA broadly—not as a means of reducing it, as attrition is healthy and normal, but as a means of having a better prediction model.
It's also important to clarify that this model isn't meant for preemptively judging potential members. Instead, it aims to provide community builders with strategic insights—identifying where to intensify or ease recruitment efforts based on understanding likely member engagement. This approach isn't about convincing everyone of EA; rather, it's about efficiently selecting and connecting with individuals who are naturally inclined toward the principles of EA. Gaining even a basic understanding of who these individuals might be can better our focus and effectiveness as club leaders, ensuring our energies are well-positioned for optimizing our CB efforts.
*This is mostly based on conversations with QEA members of varying commitment levels—so all anecdotal, qualitative observations, with a small sample size.
Of course, there are overarching factors that largely do not lay in the control of community builders:
The factors I'm more interested in are those that come down to individual factors, like personality, interests, dispositions, etc. Here are some other factors and/or general considerations:
Please reach out if you have any thoughts on this :)
Solid piece. I like lists of things and I appreciate you taking the time to write one.
I sometimes wonder how to combine many qualitative impressions like this into a more robust picture. Some thoughts:
Hi Nathan, I'm one of the co-organizers along with Juliana and I've thought a lot about quantitatively measuring attrition rates and the types of people more interested in EA.
We found it hard—at least on the level of one club—to measure things like attrition rate for a few different reasons:
But as you mentioned, there may be a lot of value in sort-of qualitatively-quantitatively measuring attrition rates on the scale of CEA—instead of trying to find reasons as for why people are not staying on a group-level, the CEA Groups team could survey reasons for why group organizers think people leave, and perhaps use that to create helpful resources.