Movement building and investing to give later

post by abergal · 2020-07-15T22:46:46.813Z · EA · GW · 3 comments

Contents

  Illustrative mathematical model
  What should we take away from this?
None
3 comments

There have been several [EA · GW] posts [EA · GW] recently [EA · GW] about investing financially to give later. I am overall uncertain about whether the marginal donor should invest, but I worry that existing analyses are missing some key movement-building effects that might be important. In particular, it seems plausible to me that:

If the above are true, we may want to invest only if we think our future money can be efficiently spent creating new longtermists. If we believe that spending can produce longtermists now, but won’t do so in the future, then we should instead be spending to produce more longtermists now instead.

Illustrative mathematical model

[Disclaimer: I am not an economist. Phil Trammell looked at this model and said that it does demonstrate my overall point, but also that the better way to do this would probably use control theory.]

[Update: Changes made after super helpful comment from Michael Dickens below.]

I created an extremely simplified model to try and illustrate the effects of spending on movement building vs. investing. In this model:

I’m not going to go into depth analyzing this model, but you can play with it here. The key observations are:

What should we take away from this?

This model above could be unrepresentative in many ways-- it’s not clear that movements can be modeled well as growing proportionally to their current size, we don’t have to spend money to convert people, etc. But it does gesture to some real aspects of the world:

As such, I think we should consider treating movement growth as a compounding resource that is useful in and of itself and is not fungible with money.

This doesn’t necessarily imply that the marginal dollar put towards movement building now is better than investing (and even in the simplified illustrative model a large fraction of total money should often go to investing). But I think we should take it into consideration when thinking about the effects of our donations.

3 comments

Comments sorted by top scores.

comment by MichaelDickens · 2020-07-18T01:24:53.126Z · EA(p) · GW(p)

I'm glad you wrote this! Movement-building is an important complement to financial investing, and can benefit the future in many of the same ways.

Maximizing the number of longtermists at time t may require periods of spending alternated with periods of investment.

I believe your model gives this result because of the constraint that you have to either spend or invest all of your salary in each period. If you allow spending greater than 0% or less than 100% of your salary, I believe you will get the result that you maximize the number of longtermists by spending some fixed proportion of your salary in each period. Alternating between periods is a way of approximating this.

I added related functionality to your script here: https://github.com/michaeldickens/public-scripts/blob/master/movement-building-model.py

Also, there is a bug in the invest function, money += (money + salary) * market_rate should be money = (money + salary) * market_rate.

comment by abergal · 2020-07-24T18:44:25.478Z · EA(p) · GW(p)

This is awesome, you're completely right and I'm totally updating my post with your model.

comment by alexherwix · 2020-07-24T19:05:27.162Z · EA(p) · GW(p)

Thank you for raising some additional considerations against giving later. I think this is really valuable for the ongoing discussion that seems to be strongly tilted in favor of investing and giving later.

Even beyond your argument for movement growth, there seem to be many other intuitive considerations where similar arguments could be made. For instance, you consider that "converting" longtermists is an activity that is not only related to money but also to time and room for growth.

You need time to convert dollars into results given that there are generally strong limitations to room for more funding that is tied to the current allocation of resources in the world. I would guess one could model this as some kind of game where at each time point t you can effectively invest x amount into cause y where x is a function of cumulative money spent on cause y. It could be plausible to model this as a gaussian function (i.e., a bell curve) where money invested in the beginning leads to strong growth in room for more funding in the next round and then declines again at some point when full saturation (i.e., all money that could reasonable be spent is spent) is approached. Interestingly, this is both an argument for giving now and giving later as there is limited room where money could be spent effectively.

Going beyond this "simple" view, it would also be interesting to model how problems grow over time as they are not addressed. The most obvious example is climate change. If somehow a US president in the 80s could have been convinced to shift policy towards renewables... the problem would have likely required much less resources overall. This indicates that the money required to be spent on problems is a function of the time at which it is discovered and how much resources are directed to it over time.

I am not a mathematician but if any of this is remotely plausible, I am not sure that the thinking so far has considered such complications (i.e., at least I haven't seen models that model these things but I also haven't been searching in depth) and at least my intuition tells me that integrating such consideration could radically tip the balance toward a strong preference for giving as early as reasonable and provide a good argument for investing into infrastructure that would help us identify and address problems effectively as they emerge.

This could be an interesting topic for a PhD with simulations chops. Or even a benchmarking platform where different agent strategies can compete against each other.[1]


  1. See Ketter, W., Peters, M., Collins, J., and Gupta, A. 2016. “COMPETITIVE BENCHMARKING: AN IS RESEARCH APPROACH TO ADDRESS WICKED PROBLEMS WITH BIG DATA AND ANALYTICS,” MIS Quarterly (40:4), p. 34. ↩︎