Why I'm skeptical about unproven causes (and you should be too)

Since living in Oxford, one of the centers of the “effective altruism” movement, I’ve been spending a lot of time discussing the classic “effective altruism” topic—where it would be best to focus our time and money.

Some people here seem to think that the most important thing we should be focusing our time and money on are speculative projects, or projects that promise a very high impact, but involve a lot of uncertainty. One such very common example is “existential risk reduction”, or attempts to make a long-term far future for humans more likely, say by reducing the chance of things that would cause human extinction.

I do agree that the far future is the most important thing to consider, by far (see papers by Nick Bostrom and Nick Beckstead). And I do think we can influence the far future. I just don’t think we can do it in a reliable way. All we have are guesses about what the far future will be like and guesses about how we can affect it. All of these ideas are unproven, speculative projects, and I don’t think they deserve the main focus of our funding.

While I waffled in cause indecision for a while, I’m now going to resume donating to GiveWell’s top charities, except when I have an opportunity to use a donation to learn more about impact. Why? My case is that speculative causes, or any cause with high uncertainty (reducing nonhuman animal suffering, reducing existential risk, etc.) requires that we rely on our commonsense to evaluate them with naīve cost-effectiveness calculations, and this is (1) demonstrably unreliable with a bad track record, (2) plays right into common biases, and (3) doesn’t make sense based on how we ideally make decisions. While it’s unclear what long-term impact a donation to a GiveWell top charity will have, the near-term benefit is quite clear and worth investing in.

Focusing on Speculative Causes Requires Unreliable Commonsense

How can we reduce the chance of human extinction? It just makes sense that if we fund cultural exchange programs between the US and China, there will be more goodwill for the other within each country, and therefore the countries will be less likely to nuke each other. Since nuclear war would likely be very bad, it’s of high value to fund cultural exchange programs, right?

Let’s try another. The Machine Intelligence Research Institute (MIRI) thinks that someday artificial intelligent agents will become better than humans at making AIs. At this point, AI will build a smarter AI which will build an even smarter AI, and—FOOM! -- we have a superintelligence. It’s important that this superintelligence be programmed to be benevolent, or things will likely be very bad. And we can stop this bad event by funding MIRI to write more papers about AI, right?

Or how about this one? It seems like there will be challenges in the far future that will be very daunting, and if humanity handles them wrong, things will be very bad. But if people were better educated and had more resources, surely they’d be better at handling those problems, whatever they may be. Therefore we should focus on speeding up economic development, right?

These three examples are very common appeals to commonsense. But commonsense hasn’t worked very well in the domain of finding optimal causes.

Can you pick the winning social program?

Benjamin Todd makes this point well in “Social Interventions Gone Wrong”, where he provides a quiz with eight social programs and asks readers to guess whether they succeeded or failed.

I’ll wait for you to take the quiz first… doo doo doo… la la la...

Ok, welcome back. I don’t know how well you did, but success on this quiz is very rare, and this poses problems for commonsense. Sure, I’ll grant you that Scared Straight sounds pretty suspicious. But the Even Start Family Literacy Program? It just makes sense that providing education to boost literacy skills and promote parent-child literacy activities should boost literacy rates, right? Unfortunately, it was wrong. Wrong in a very counter-intuitive way. There wasn’t an effect.

GiveWell and Commonsense’s Track Record of Failure

Commonsense actually has a track record of failure. GiveWell has been talking about this for ages. Every time GiveWell has found an intervention hyped by commonsense notions of high-impact and they’ve looked at it further, they’ve ended up disappointed.

The first was the Fred Hollows Foundation. A lot of people had been repeating the figure that the Fred Hollows Foundation could cure blindness for $50. But GiveWell found that number suspect.

The second was VillageReach. GiveWell originally put them as their top charity and estimated them as saving a life for under $1000. But further investigation kept leading them to revise their estimate until ultimately they weren’t even sure if VillageReach had an impact at all.

Third, there is deworming. Originally, deworming was announced as saving a year of healthy life (DALY) for every $3.41 spent. But when GiveWell dove into the spreadsheets that resulted in that number, they found five errors. When the dust settled, the $3.41 figure was found to actually be off by a factor of 100. It was revised to $326.43.

Why shouldn’t we expect this trend to not be the case in other areas where calculations are even looser and numbers are even less settled, like efforts devoted to speculative causes? Our only recourse is to fall back on interventions that are actually studied.

People are notoriously bad at predicting the (far) future

Cost-effectiveness estimates also frequently require making predictions about the future. Existential risk reduction, for example, requires making predictions about what will happen in the far future, and how your actions are likely to effect events hundreds of years down the road. Yet, experts are notoriously bad at making these kinds of predictions.

James Shanteau found in “Competence in Experts: The Role of Task Characteristics” (see also Kahneman and Klein’s “Conditions for Intuitive Expertise: A Failure to Disagree”) that experts perform well when thinking about static stimuli, thinking about things, and when there is feedback and objective analysis available. Furthermore, experts perform pretty badly when thinking about dynamic stimuli, thinking about behavior, and feedback and objective analysis are unavailable.

Predictions about existential risk reduction and the far future are firmly in the second category. So how can we trust our predictions about our impact on the far future? Our only recourse is to fall back on interventions that we can reliably predict, until we get better at prediction (or invest money in getting better at making predictions).

Even broad effects require specific attempts

One potential resolution to this problem is to argue for “broad effects” rather than “specific attempts”. Perhaps it’s difficult to know whether a particular intervention will go well or mistaken to focus entirely on Friendly AI, but surely if we improved incentives and norms in academic work to better advance human knowledge (meta-research), improved education, or advocated for effective altruism, the far future would be much better equipped to handle threats.

I agree that these broad effects would make the far future better and I agree that it’s possible to implement these broad effects and change the far future. The problem, however, is it can’t be done in an easy or well understood way. Any attempt to implement a broad effect would require a specific action that has an unknown expectation of success and unknown cost-effectiveness. It’s definitely beneficial to advocate for effective altruism, but could this be done in a cost-effective way? A way that’s more cost-effective at producing welfare than AMF? How would you know?

In order to accomplish these broad effects, you’d need specific organizations and interventions to channel your time and money into. And by picking these specific organizations and interventions, you’re losing the advantage of broad effects and tying yourself to particular things with poorly understood impact and no track record to evaluate.

Focusing on speculative causes plays into our biases

We’ve now known for quite a long time that people are not all that rational. Instead, human thinking fails in very predictable and systematic ways. Some of these ways make us less likely to take speculative causes seriously, such as ambiguity aversion, the absurdity heuristic, scope neglect, and overconfidence bias.

But there’s also a different side of the coin, with biases that might make people think badly about existential risk:

Optimism bias. People generally think things will turn out better than they actually will. This could lead people to think that their projects will have a higher impact than they actually will, which would lead to higher estimates of cost-effectiveness than is reasonable.

Control bias. People like to think they have more control over things than they actually do. This plausibly also includes control over the far future. Therefore, people are probably biased into thinking they have more control over the far future than they actually do, leading to higher estimates of ability to influence the future than is reasonable.

“Wow factor” bias. People seem attracted to more impressive claims. Saving a life for $2500 through a malaria bed net seems much more boring compared to the chance of saving the entire world by averting a global catastrophe. Within the effective altruist / LessWrong community, existential risk reduction is cool and high status, whereas averting global poverty is not. This might lead to more endorsement of existential risk reduction than is reasonable.

Conjunction fallacy. People have a problem assessing probability properly when there are many steps involved, each of which has a chance of not happening. Ten steps, each with an independent 90% success rate, has only a 35% chance of success. Focusing on the far future seems to involve that a lot of largely independent events happen the way that is predicted. This would mean people are worse at estimating their chances of helping the far future, creating higher cost-effectiveness estimates than is reasonable.

Selection bias. When trying to find trends in history that are favorable for affecting the far future, some examples can be provided. However, this is because we usually hear about the interventions that end up working, whereas all the failed attempts to influence the far future are never heard of again. This creates a very skewed sample that can negatively bias our thinking about our success of influencing the far future.

It’s concerning there are numerous biases both weighted in favor and weighted against speculative causes, and this means we must tread carefully when assessing their merits. However, I would strongly expect biases to be even worse in favor of speculative causes rather than against them, because speculative causes lack the available feedback and objective evidence needed to help insulate against bias, whereas a focus on global health does not.

Focusing on speculative uses bad decision theory

Furthermore, not only is the case for speculative causes undermined by a bad track record and possible cognitive biases, but the underlying decision theory seems suspect in a way that’s difficult to place.

Would you play a lottery with no stated odds?

Imagine another thought experiment—you’re asked to play a lottery. You have to pay $2 to play, but you have a chance at winning $100. Do you play?

Of course, you don’t know, because you’re not given odds. Rationally, it makes sense to play any lottery where you expect to come out ahead more often than not. If the lottery is a coin flip, it makes sense to pay $2 to have a ⁵⁰⁄₅₀ shot to win $100, since you’d expect to win $50 on average, and come ahead $48 each time. With a sufficiently high reward, even a one in a million chance is worth it. Pay $2 for a 1/1M chance of winning $1B, and you’d expect to come out ahead by $998 each time.

But $2 for the chance to win $100, without knowing what the chance is? Even if you had some sort of bounds, like you knew the odds had to be at least ¹⁄₁₅₀ and at most ¹⁄₁₀, though you could be off by a little bit. Would you accept that bet?

Such a bet seems intuitively uninviting to me, yet this is the bet that speculative causes offer me.

“Conservative orders of magnitude” arguments

In response to these considerations, I’ve seen people endorsing speculative causes look at their calculations and remark that even if their estimate were off by 1000x, or three orders of magnitude, they still would be on solid ground for high impact, and there’s no way they’re actually off by three orders of magnitude. However, Nate Silver’s The Signal and the Noise: Why So Many Predictions Fail — but Some Don’t offers a cautionary tale:

Moody’s, for instance, went through a period of making ad hoc adjustments to its model in which it increased the default probability assigned to AAA-rated securities by 50 percent. That might seem like a very prudent attitude: surely a 50 percent buffer will suffice to account for any slack in one’s assumptions? It might have been fine had the potential for error in their forecasts been linear and arithmetic. But leverage, or investments financed by debt, can make the error in a forecast compound many times over, and introduces the potential of highly geometric and nonlinear mistakes.
Moody’s 50 percent adjustment was like applying sunscreen and claiming it protected you from a nuclear meltdown—wholly inadequate to the scale of the problem. It wasn’t just a possibility that their estimates of default risk could be 50 percent too low: they might just as easily have underestimated it by 500 percent or 5,000 percent. In practice, defaults were two hundred times more likely than the ratings agencies claimed, meaning that their model was off by a mere 20,000 percent.

Silver points out that when estimating how safe mortgage backed securities were, the difference between assuming defaults are perfectly uncorrelated and defaults are perfectly correlated is a difference of 160,000x in your risk estimate—or five orders of magnitude.

If these kinds of five-orders-of-magnitude errors are possible in a realm that has actual feedback and is moderately understood, how do we know the estimates for cost-effectiveness are safe for speculative causes that are poorly understood and offer no feedback? Again, our only recourse is to fall back on interventions that we can reliably predict, until we get better at prediction.

Value of information, exploring, and exploiting

Of course, there still is one important aspect of this problem that has not been discussed—value of information—or the idea that sometimes it’s worth doing something just to learn more about how the world works. This is important in effective altruism too, where we focus specifically on “giving to learn”, or using our resources to figure out more about the impact of various causes.

I think this is actually really important and is not a victim to any of my previous arguments, because we’re not talking about impact, but rather learning value. Perhaps one could look to an “explore-exploit model”, or the idea that we achieve the best outcome when we spend a lot of time exploring first (learning more about how to achieve better outcomes) before exploiting (focusing resources on achieving the best outcome we can). Therefore, whenever we have an opportunity to “explore” further or learn more about what causes have high impact, we should take it.

Learning in practice

Unfortunately, in practice, I think these opportunities are very rare. Many organizations that I think are “promising” and worth funding further to see what their impact looks like do not have sufficiently good self-measurement in place to actually assess their impact or sufficient transparency to provide that information, therefore making it difficult to actually learn from them. And on the other side of things, many very promising opportunities to learn more are already fully funded. One must be careful to ensure that it’s actually one’s marginal dollar that is getting marginal information.

The typical donor

Additionally, I don’t think the typical donor is in a very good position to assess where there is high value of information or have the time and knowledge to act upon this information once it is acquired. I think there’s a good argument for people in the “effective altruist” movement to perhaps make small investments in EA organizations and encourage transparency and good measurement in their operations to see if they’re successfully doing what they claim (or potentially create an EA startup themselves to see if it would work, though this carries large risks of further splitting the resources of the movement).

But even that would take a very savvy and involved effective altruist to pull off. Assessing the value of information on more massive investments like large-scale research or innovation efforts would be significantly more difficult, beyond the talent and resources of nearly all effective altruists, and are probably left to full-time foundations or subject-matter experts.

GiveWell’s top charities also have high value of information

As Luke Muehlhauser mentions in “Start Under the Streetlight, Then Push Into the Shadows”, lots of lessons can be learned only by focusing on the easiest causes first, even if we have strong theoretical reasons to expect that they won’t end up being the highest impact causes once we have more complete knowledge.

We can use global health cost-effectiveness considerations as practice for slowly and carefully moving into the more complex and less understood domains. There even are some very natural transitions, such as beginning to look at “flow through effects” of reducing disease in the third-world and beginning to look at how more esoteric things affect the disease burden, like climate change. Therefore, even additional funding for GiveWell’s top charities has high value of information. And notably, GiveWell is beginning this “push” through GiveWell Labs.

Conclusion

The bottom line is that sometimes things look too good to be true. Therefore, I should expect that the actual impact of speculative causes that make large promises, upon a thorough investigation, will be much lower. And this has been true in other domains. People are notoriously bad at estimating the effects of causes in both the developed world and developing world, and those are the causes that are near to us, provide us with feedback, and are easy to predict. Yet, from the Even Start Family Literacy Program to deworming estimates, our commonsense has failed us.

Add to that the fact that we should expect ourselves to perform even worse at predicting the far future. Add to that optimism bias, control bias, “wow factor” bias, and the conjunction fallacy, which make it difficult for us to think realistically about speculative causes. And then add to that considerations in decision theory, and whether we would bet on a lottery with no stated odds.

When all is said and done, I’m very skeptical of speculative projects. Therefore, I think we should be focused on exploring and exploiting. We should do whatever we can to fund projects aimed at learning more, when those are available, but be careful to make sure they actually have learning value. And when exploring isn’t available, we should exploit what opportunities we have and fund proven interventions.

But don’t confuse these two concepts and fund causes intended for learning because of their actual impact value. I’m skeptical about these causes actually being high impact, though I’m open to the idea that they might be and look forward to funding them in the future when they become better proven.

I’d like to thank Nick Beckstead, Joey Savoie, Xio Kikauka, Carl Shulman, Ryan Carey, Tom Ash, Pablo Stafforini, Eliezer Yudkowsky, and Ben Hoskin for providing feedback on this essay, even if some of them might strongly disagree with its conclusion.

Why I’m skeptical about unproven causes (and you should be too)