Crossposted from Charity Entrepreneurship's Blog here

In my work as a research analyst for __Charity Entrepreneurship__, I have been assessing possible animal welfare campaigns. The first thing I found is that finding the correct welfare asks for corporate or government campaigns is really hard. The scarcity of information in animal advocacy relative to other cause areas means that accurate decisions are harder to make. Establishing reliable metrics to assess ideas is essential to avoid wasting time. One way of doing this is to think about how to approximate the endline metric we are trying to maximise: counterfactual impact.

**Counterfactual Impact **

When trying to make the world a better place, what we really want to measure is the counterfactual impact. Counterfactual impact is the increase in utility that your actions actually cause in the world. If you are choosing to do X action, the actual impact that can be attributed to your action is the difference in utility between the world where you do X and the world where you do not. This is very difficult to measure, as your actions have flow-through effects, which have flow-through effects, and so on. Every action you take ripples out through the butterfly effect to change all of history. The way we account for this is by discounting these flow-through effects based on uncertainty until effects with an unknown direction are discounted to 0. Peter Hurford has written __a more fleshed-out discussion__ of different ways to handle these flow-through effects.

Given that counterfactual impact is what we are pursuing, I have considered two possible strategies for selecting welfare asks: Maximizing the value of counterfactual speed-up and maximizing the welfare increase.

**Maximizing the Welfare Increase (WI) vs Maximizing the Value of Counterfactual Speed Up (CSU)**

The simplest method could be considered as an instant-gratification method. For this method of decision making, we just choose the biggest welfare increase available to achieve as much welfare in the present as possible. If we use this method then, irrespective of what other organisations are doing, we should select the largest welfare increase available to us. If dissolved oxygen for fish is the largest welfare improvement possible, we should select it, even if we know that it is the next ask for which the rest of the animal movement will campaign. This may only speed the process up by a year, but it will minimise fish suffering in the present.

Counterfactual speed-up is the second strategy. To select asks using this methodology, we need to know one additional piece of information: the time until other actors currently in the field would have chosen this ask. This timeline for welfare improvements allows us to calculate how much sooner our campaign caused the welfare improvement to occur.

For example, imagine we are in 1975, considering a cage-free ask in the United States that would take 10 years to be successful. Knowing what we do now, we could see that conducting a corporate campaign at that time would speed up the timeline for cage-free by ~30 years. If we ignore the possibility of recidivism, by acting in the 1975s we only increase the number of cage-free years by 30. Using this model, the impact we have for chickens is 30 multiplied by the magnitude of the welfare increase caused by cage-free farming. Using this factor in our decision making, we now have some time sensitivity.

If the largest welfare improvements are many years away, both decision-making strategies will dictate the same asks. However, we can quickly see where these two strategies will diverge. Imagine we are only examining two possible welfare asks: one that is a moderate improvement that will not otherwise happen for some time, and another that is large but will occur shortly anyway. If we choose the moderate improvement, it will occur many years sooner than it otherwise would have. If we campaign for the large improvement, it will only occur a few years sooner than it would have without our involvement. This is suboptimal from the counterfactual speed-up perspective, as a moderate ask advanced by many years will speed up welfare more, and thus we will be responsible for more impact. The benefit of this model is that it is relatively simple, as only the expected value of the welfare improvement needs to be determined.

A short examination of these two strategies would lead us to believe that counterfactual speed-up is always the superior model. The time-sensitivity of this way of making decisions should increase the counterfactual impact of your actions. However, it is still just a heuristic for counterfactual impact, not a direct measure. This is not just in terms of nebulous flow-through effects, but also the effect of speed-up on other interventions. If we achieve a welfare ask sooner, other charities can conduct a different campaign in place of this one; and where that second campaign would have been conducted, another; and so on.

Thus, the two main effects of conducting a welfare ask now are:

1) The speed-up of the welfare ask selected

2) The speed-up of all subsequent welfare asks

Imagine what would happen in the timeline where we conduct the year cage-free campaign in 1975. Cage-free improvements for chickens now occur 30 years earlier. Assuming organisations follow the same timeline as before, when they would have had to conduct cage-free campaigns they can now campaign for broiler chicken or fish welfare. In fact, through a single campaign in 1975, you speed up every welfare ask that will occur until the end of animal agriculture.

As long as your use of funding does not slow down the timeline for subsequent campaigns, the impact of your cage-free ask could be 30 years of cage-free chickens and 10 years of broiler and fish welfare improvements. Therefore, if you only measure the counterfactual speed-up for your actions on cage-free, you underestimate your actual impact. This consideration led me to create a very simple model for the charity sector to examine how the two strategies mentioned above compare when trying to maximize counterfactual impact.

**Model **

To explore this, I created __a model of the charity sector__ where there is currently only one charity working on welfare improvements. There are ten years until animal agriculture ends, and ten possible asks. Once each welfare improvement has been achieved, it is never reversed and will last the remainder of the 10 years until animals are no longer farmed. The magnitude of possible welfare asks that charities can choose from are between 1000 to -100, and vary randomly within that range. This is not a perfect model of the magnitude of difference in actual welfare asks or its distribution. A more accurate model would probably have very few asks that have very cost-effective improvements, with many more closer to 0. I included the possibility of negative asks because our understanding of animal welfare may lead us to accidentally decrease the welfare of animals.

Given this is the space charities are working in, I examined how deciding our ask based on counterfactual speed-up and welfare increase affects the counterfactual impact of our actions. In the real world, we do not have perfect information about the magnitude of welfare asks or the timelines of organizations currently in the field. It is possible that some ways of making decisions perform better in lower-information environments, but I thought examining the ideal situation could still provide some insight.

In the first case, I examined a world in which a Random Altruist charity is already works on welfare asks. This charity chooses randomly from the set of welfare asks; this could be similar to (although probably worse than) choosing asks based on salience and emotional impact. In this case, I modeled a sample of 30 random welfare ask ranges and selections. I found that entering the space with the welfare increase method leads to more optimal outcomes ~17% of the time. The counterfactual speed-up approach leads to more optimal outcomes ~60% of the time. The two models were equally good ~23% of the time. Therefore, if we did not have perfect information and so cannot select asks which result in highest utility then maximizing counterfactual speed-up is a superior strategy. No surprise there.

However, I then examined how varying the strategy of the existing charity would change the outcome. If the existing charity follows the simple welfare increase approach, the results change. Now following the simple welfare increase strategy is the optimal outcome 100% of the time! This would be the same if the actor in the space is trying maximize counterfactual speed-up, as the best way to do this when alone is to choose the biggest welfare asks.

**Conclusion**

This outcome seems to suggest that the optimal decision-making strategy in imperfect information environment changes based on the strategy of the other charities in the sector. It seems that the better other organizations are at differentiating between strong and weak welfare asks, the better the welfare increase strategy becomes. However, if asks are currently selected closer to random then counterfactual speed-up is a much better heuristic.

I am unsure how accurate existing organizations would have to be at maximising welfare for the welfare increase strategy to win out. A more complex model may be able to capture this by altering the accuracy of welfare selection. It could also vary the amount and accuracy of the information for the three possible actors in the system.

I think the ranking of the two approaches could depend substantially on the distribution of magnitudes of welfare asks and the number of asks.

For example, consider a distribution which is constant in magnitude, except a few rare and very large outliers. Suppose specifically it's always positive, and constant except for

exactly onelarge positive outlier. In this case, the optimal solution is to ensure the outlier comes as early as possible, so you choose the outlier first, and then choose any other asks after that. The welfare increase method does this, so it willalwaysbe optimal (but might tie with counterfactual speed-up). On the other hand, if the number of asks is high enough, the counterfactual speed-up approach will often choose the last ask the Random Altruist charity would have chosen so it could speed it up, which would be suboptimal.To illustrate, consider the following sequence of asks (and their present value) that Random Altruist charity would have chosen:

1, 10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1

That's one 1, one 10 and then ten 1s.

Choosing the 10 has a value of 10 according to counterfactual speed-up, since it advances it by one year.

Choosing the very last 1 in the sequence has a value of 11 according to counterfactual speed-up, since it advances it by 11 years, but it wouldn't have made a real difference if you had chosen the 2nd 1 instead (the two sequences would be indistinguishable by welfare), and even choosing the very 1st 1 would have been better, since it would make the 10 come one year earlier.

What this might suggest in general to me is that if most asks aren't very impactful or have similar impact, but there are some much more impactful outliers, we should use the welfare increase approach. This seems kind of intuitive if you thought most animal welfare charities aren't focused on farmed animals at all or the most numerous and worst treated ones (I'm not sure this is actually the case, most animal charity goes to shelters according to ACE, but I don't know if that counts as animal welfare asks). (EDIT: I suppose if there's still quite a lot of spread among the outliers, then the counterfactual speed-up approach could be better.) Of course, we could just ignore those charities, but once we do, we might be in a situation similar to the one you described as:

Also, did you happen to estimate (via Monte Carlo) the expected value of each approach?