2020-08-10

EA ideas 4: utilitarianism

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Many ideas in effective altruism (EA) do not require a particular moral theory. However, while there is no common EA moral theory, much EA moral thinking leans consequentialist (i.e. morality is fundamentally about consequences), and often specifically utilitarian (i.e. wellbeing and/or preference fulfilment are the consequences we care about).

Utilitarian morality can be thought of as rigorous humanism, where by “humanism” I mean the general post-Enlightenment secular value system that emphasises caring about people, rather than upholding, say, religious rules or the honour of nations. Assume that the welfare of a conscious mind matters. Assume that our moral system should be impartial: that wellbeing/preferences should count the same regardless of who has them, and also in the sense of being indifferent of who’s perspective it is being wielded from (for example, a moral system that says to only value yourself would give you different advice than it gives me). The simplest conclusion you can draw from these assumptions is to consider welfare to be good and seek to increase it.

I will largely ignore differences between the different types of utilitarianism. Examples of divisions within utilitarianism include preference vs hedonic/classical utilitarianism (do we care about the total satisfied preferences, or the total wellbeing; how different are these?) and act vs rule utilitarianism (is the right act the one with the greatest good as its consequence, or the one that conforms to a rule which produces the greatest good as its consequences – and, once again, are they different?).


Utilitarianism is decisive

We want to do things that are “good”, so we have to define what we mean by it. But once we’ve done this, this concept of good is of no help unless it lets us make decisions on how to act. I will refer to the general property of a moral system being capable of making non-paradoxical decisions as decisiveness.

Decisiveness can fail if a moral system leads to contradiction. Imagine a deontological system with the rules “do not lie” and “do not take actions that result in someone dying”. Now consider the classic thought experiment of what such a deontologist would do if the Gestapo knocked on their door and asked if they’re hiding any Jews. A tangle of absolute rules almost ensures the existence of some case where they cannot all be satisfied, or where following them strictly will cause immense harm.

Decisiveness fails if our system allows circular preferences, since then you cannot make a consistent choice. Imagine you follow a moral system that says volunteering at a soup kitchen is better than helping old people across the street, collecting money for charity is better than soup kitchen volunteering, and helping old people across the street is better than collecting money. You arrive at the soup kitchen and decide to immediately walk out to go collect money. You stop collecting money to help an old person across the street. Halfway through, you abandon them and run off back to the soup kitchen.

Decisiveness fails if there are tradeoffs our system cannot make. Imagine highway engineers deciding whether to bulldoze an important forest ecosystem or a historical monument considered sacred. If your moral system cannot weigh environment against historical artefacts (and economic growth, and the time of commuters, and …), it is not decisive.

So for any two choices, a decisive moral system must be able to compare them, and the comparisons it makes cannot be circular preference. This implies a ranking: X is better than Y translates to X is before Y in the ranking list.

(If we allow circular preferences, we obviously can’t make a list, since the graph of “better-than” relations would include cycles. If there are tradeoffs we can’t make – X and Y such that X and Y are neither better than equal or worse than each other – we can generate a ranking list but not a unique one (in set theory terms, we have a partial order rather than a total order).)

Decisiveness also fails if our system can’t handle numbers. It is better to be happy for two minutes than one minute than fifty nine seconds. More generally, to practically any good we can either add or subtract a bit: one more happy thought, one less bit of pain.

Therefore a decisive moral system must rank all possible choices (or actions or world states or whatever), with no circular preferences, and with arbitrarily many notches between each ranking. It sounds like what we need is numbers: if we can assign a number to choices, then there must exist a non-circular ranking (you can always sort numbers), and there’s no problem with handling the quantitativeness of many moral questions.

There can’t be one axis to measure the value of pleasure, one to measure meaning, and another for art. Or there can – but at the most basic level of moral decision-making, we must be able to project everything onto the same scale, or else we’re doomed to have important moral questions where we can only shrug our shoulders. This leads to the idea of all moral questions being decidable by comparing how the alternatives measure up in terms of “utility”, the abstract unit of the basic value axis.

You might say that requiring this extreme level of decisiveness may sometimes be necessary in practice, but it’s not what morality is about; perhaps moral philosophy should concern itself with high-minded philosophical debates over the nature of goodness, not ranking the preferability of everything. Alright, have it your way. But since being able to rank tricky “ought”-questions is still important, we’ll make a new word for this discipline: fnergality. You can replace “morality” or “ethics” with “fnergality” in the previous argument and in the rest of this post, and the points will still stand.


What is utility?

So far, we have argued that a helpful moral system is decisive, and that this implies it needs a single utility scale for weighing all options.

I have not specified what utility is. Without this definition, utilitarianism is not decisive at all.

How you define utility will depend on which version of utilitarianism you endorse. The basic theme across all versions of utilitarianism is that utility is assigned without prejudice against arbitrary factors (like location, appearance, or being someone other than the one who is assigning utilities), and is related to ideas of welfare and preference.

A hedonic utilitarian might define the utility of a state of the world as total wellbeing minus total suffering across all sentient minds. A preference utilitarian might ascribe utility to each instance of a sentient mind having a preference fulfilled or denied, depending on the weight of the preference (not being killed is likely a deeper wish than hearing a funny joke), and the sentience of the preferrer (a human’s preference is generally more important than a cat’s). Both would likely want to maximise the total utility that exists over the entire future.

These definitions leave a lot of questions unanswered. For example, take the hedonic utilitarian definition. What is wellbeing? What is suffering? Exactly how many wellbeing units are being experienced per second by a particular jogger blissfully running through the early morning fog?

The fact that we can’t answer “4.7, ±0.5 depending on how runny their nose is” doesn’t mean utilitarianism is useless. First, we might say that an answer exists in principle, even if we can’t figure it out. For example, a hedonic utilitarian might say that there is some way to calculate the net wellbeing experienced by any sentient mind. Maybe it requires knowing every detail of their brain activity, or a complete theory of what consciousness is. But – critically – these are factual questions, not moral ones. There would be moral judgements involved in specifying exactly how to carry out this calculation, or how to interpret the theory of consciousness. There would also be disagreements, in the same way that preference and hedonic utilitarians disagree today (and it is a bad idea to specify one Ultimate Goodness Function and declare morality solved forever). But in theory and given enough knowledge, a hedonic utilitarian theory could be made precise.

Second, even if we can only approximate utilities, doing so is still an important part of difficult real-world decision-making.

For example, Quality- and Disability-Adjusted Life Years (QALYs and DALYs) try to put a number on the value of a year of life with some disease burden. Obviously it is not an easy judgement to make (usually the judgement is made by having a lot of people answer carefully designed questions on a survey), and the results are far more imprecise than the 3-significant-figure numbers in the table on page 17 here would suggest. However, the principle that we should ask people and do studies to try figure out how much they’re suffering, and then make the decisions that reduce suffering the most across all people, seems like the most fair and just way to make medical decisions.

Using QALYs may seem coldly numerical, but if you care about reducing suffering, not just as a lofty abstract statement but as a practical goal, you will care about every second. It can also be hard to accept QALY-based judgements, especially if they prefer others to people close to you. However, taking an impartial moral view, it is hard not to accept that the greatest good is better than a lesser good that includes you.

(Using opposition to QALYs as an example, Robin Hanson argues with his characteristic bluntness that people favour discretion over mathematical precision in their systems and principles “as a way to promote an informal favoritism from which they expect to benefit”. In addition to the ease of sounding just and wise while repeating vague platitudes, this may be a reason why the decisiveness and precision of utilitarianism become disadvantages on the PR side of things.)


Morality is everywhere

By achieving decisiveness, utilitarianism makes every choice a moral one.

One possible understanding of morality is that it splits actions into three planes. There are rules for what to do (“remember the sabbath day”). There are rules for what not to do (“thou shalt not kill, and if thy doest, thy goeth to hell”). And then there’s the earthly realm, of questions like whether to have sausages for dinner, which – thankfully – morality, god, and your local preacher have nothing to say about.

Utilitarianism says sausages are a moral issue. Not a very important one, true, but the happiness you get from eating them, your preferences one way or the other, and the increased risk of heart attack thirty years from now, can all be weighed under the same principles that determine how much effort we should spend on avoiding nuclear war. This is not an overreach: a moral theory is a way to answer “ought”-questions, and a good one should cover all of them.

This leads to a key strength of utilitarianism: it scales, and this matters, especially when you want to apply ethics to big uncertain things. But first, a slight detour.


Demandingness

A common objection to utilitarianism is that it is too demanding.

First of all, I find this funny. Which principle of meta-ethics is it, exactly, that guarantees your moral obligations won’t take more than the equivalent of a Sunday afternoon each week?

However, I can also see why consequentialist ethics can seem daunting. For someone who is used to thinking of ethics in terms of specific duties that must always be carried out, a theory that paints everything with some amount of moral importance and defines good in terms of maximising something vague and complicated can seem like too much of a burden. (I think this is behind the misinterpretation that utilitarianism says you have a duty to calculate that each action you take is the best one possible, which is neither utilitarian nor an effective way to achieve anything.)

Utilitarianism is a consequentialist moral theory. Demands and duties are not part of it. It settles for simply defining what is good.

(As it should. The definition is logically separate from the implications and the implementation. Good systems, concepts, and theories are generally narrow.)


Scaling ethics to the sea

There are many moral questions that are, in practice, settled. All else being equal, it is good to be kind, have fun, and help the needy.

To make an extended metaphor: we can imagine that there is an island of settled moral questions; ones that no one except psychopaths or philosophy professors would think to question.

This island of settled moral questions provides a useful test for moral systems. A moral system that doesn’t advocate kindness deserves to go in the rubbish. But though there is important intellectual work to be done in figuring out exactly what grounds this island (the geological layers it rests on, if you will), the real problem of morality in our world is how we extrapolate from this island to the surrounding sea.

In the shallows near the island you have all kinds of conventional dilemmas – for example, consider our highway engineers in the previous example weighing nature against art against economy. Go far enough in any direction and you will encounter all sorts of perverse thought experiment monsters dreamt up by philosophers, which try to tear apart your moral intuitions with analytically sharp claws and teeth.

You might think we can keep to the shallows. That is not an option. We increasingly need to make moral decisions about weird things, due to the increasing strangeness of the world: complex institutions, new technologies, and the sheer scale of there being over seven billion people around.

A moral system based on rules for everyday things is like a constant-sized knife: fine for cutting up big fish (should I murder someone?), but clumsy at dealing with very small fish (what to have for dinner?), and often powerless against gargantuan eldritch leviathans from the deep (existential risk? mind uploading? insect welfare?).

Utilitarianism scales both across sizes of questions and across different kinds of situations. This is because it isn’t based on rules, but on a concept (preference/wellbeing) that manages to turn up whenever there are morally important questions. This gives us something to aim for, no matter how big or small. It also makes us value preference/wellbeing wherever it turns up, whether in people we don’t like, the mind of a cow, or in aliens.


Utilitarianism and other kinds of ethics

Utilitarianism, and consequentialist ethics more broadly, lacks one property that is a common social (if not philosophical) use of morality.

Consider confronting a thief robbing a jewellery store. A deontological argument is “stealing is wrong; don’t do it”. A utilitarian argument would need to spell out the harms: “don’t steal, because you will cause suffering to the owner of the shop”. But the thief may well reply: “yes, but the wellbeing I gain from distributing the proceeds to my family is greater, so my act is right”. And now you’d have to point out that the costs to the shop workers who will lose their jobs if the shop goes bankrupt, plus more indirect costs like the effect on people’s trust in others or feelings of safety, outweigh these benefits – if they even do. Meanwhile the thief makes their escape.

By making moral questions depend heavily on facts about the world, utilitarianism does not admit smackdown moral arguments (you can always be wrong about the facts, after all). This is a feature, not a bug. Putting people in their place is sometimes a necessary task (as in the case of law enforcement), but in general it is the province of social status games, not morality.

Of course, nations need laws and people need principles. The insight of utilitarianism is that, important as these things are, their rightness is not axiomatic. There is a notion of good, founded on the reality of minds doing well and fulfilling their wishes, that cuts deeper than any arbitrary rule can. It is an uncomfortable thought that there are cases where you should break any absolute moral rule. But would it be better if there were rules for which we had to sacrifice anything?

Recall the example of the Gestapo asking if you’re hiding Jews in your house. Given an extreme enough case, whether or not a moral rule (e.g. “don’t lie”) should be followed does depend on the effects of an action.

At first glance, while utilitarianism captures the importance of happiness, selflessness, and impartiality, it doesn’t say anything about many other common moral topics. We talk about human rights, but consequentialism admits no rights. We talk about good people and bad people, but utilitarianism judges only consequences, not the people who bring them about. In utilitarian morality, good intentions alone count for nothing.

First, remember that utilitarianism is a set of axioms about the most fundamental definition of good is. Just like simple mathematical axioms can lead to incredible complexity and depth, if you follow utilitarian reasoning down to daily life, you get a lot of subtlety and complexity, including a lot of common-sense ethics.

For example, knowledge has no intrinsic value in utilitarianism. But having an accurate picture of what the world is like is so important for judging what is good that, in practice, you can basically regard accurate knowledge as a moral end in itself. (I think that unless you never intend to be responsible for others or take actions that significantly affect other people, when deciding whether to consider something true you should care only about its literal truth value, and not at all about whether it will make you feel good to believe it.)

To take another example: integrity, in the sense of being honest and keeping commitments, clearly matters. This is not obvious if you look at the core ideas of utilitarianism, in the same way that the Chinese Remainder Theorem is not obvious if you look at the axioms of arithmetic. That doesn’t somehow make it un-utilitarian; for some examples of arguments, see here.

See also this article for ideas on why strictly following rules can make sense even for strict consequentialists, given only the fact that human brains are fallible in predictable ways.

As a metaphor, consider scientists. They are (in some idealised hypothetical world) committed only to the pursuit of truth: they care about nothing except the extent to which their theories precisely explain the world. But the pursuit of this goal in the real world will be complicated, and involve things – say, wild conjectures, or following hunches – that might even seem to go against the end goal. In the same way, real-world utilitarianism is not a cartoon caricature of endlessly calculating consequences and compromising principles for “the greater good”, but instead a reminder of what really matters in the end: the wishes and wellbeing of minds. Rights, duties, justice, fairness, knowledge, and integrity are not the most basic elements of (utilitarian) morality, but that doesn’t make them unimportant.


Utilitarianism is horrible

Utilitarianism may have countless arguments on its side, but one fact remains: it can be pretty horrible.

Many thought experiments show this. The most famous is the trolley problem, where the utilitarian answer requires diverting a trolley from a track containing 5 people to one containing only a single person (an alternative telling is doctors killing a random patient to get the organs to save five others). Another is the mere addition paradox, also known as the repugnant conclusion: we should consider a few people living very good lives as a worse situation than many people living mediocre lives.

Of course, the real world is never as stark as philosophers’ thought experiments. But a moral system should still give an answer – the right one – to every moral dilemma.

Many alternatives to utilitarianism seem to fail at this step; they are not decisive. It is always easier to wallow in platitudes than to make a difficult choice.

If a moral system gives an answer we find intuitively unappealing, we need to either reject the moral system, or reject our intuitions. The latter is obviously dangerous: get carried away by abstract morals, and you might find yourself denying common-sense morals (the island in the previous metaphor). However, particularly when dealing with things that are big or weird, we should expect our moral intuitions to occasionally fail.

As an example, I think the repugnant conclusion is correct: for any quantity of people living extremely happy lives, there is some larger quantity of people living mediocre lives that would be a better state for the world to be in.

First, rejecting the repugnant conclusion means rejecting total utilitarianism: the principle that you sum up individual utilities to get total utility (for example, you might average utilities instead). Rejecting total utilitarianism implies weird things, like the additional moral worth of someone’s life depending on how many people are already in the world. Why should a happy life in a world with ten billion people be worth less than one in a world with a thousand people?

Alternatives also bring up their own issues. To take a simple example, if you value average happiness instead, eliminating everyone who is less happy than the average is a good idea (in the limit, every world of more than one person should be reduced to a world of one person).

Finally, there is a specific bias that explains why the repugnant conclusion seems so repugnant. Humans tend to show scope neglect. If our brains were built differently, and assigned due weight to the greater quantity of life in the “repugnant” choice, I think we’d find it the intuitive one.

However, population ethics is both notoriously tricky and a fairly new discipline, so there is always the chance there exists a better alternative population axiology than totalism.


Is utilitarianism complete and correct?

I’m not sure what evidence or reasoning would let us say that a moral system is complete and correct.

I do think the basic elements of utilitarianism are fairly solid. First, I showed above how requiring decisiveness leads to most of the utilitarian character of the theory (quantitativeness, the idea of utility). The reasons are similar to the ones for using expected value reasoning: if you don’t, you either can’t make some decisions, or introduce cases where you make stupid ones. Second, ideas of impartiality and universality seem like fundamental moral ideas. I’d be surprised if you could build a consistent, decisive, and humane moral theory without the ideas of quantified utility and impartiality.

Though this skeleton may be solid, the real mess lies with defining utility.

Do we care about preferences or wellbeing? It seems that if we define either in a broad enough way to be reasonable, the ideas start to converge. Is this a sign that we’re on the right track because the two main variants of utilitarianism talk about a similar thing, or that we’re on the wrong track and neither concept means much at all?

Wellbeing as pleasure leaves out most of what people actually value. Sometimes people prefer to feel sadness; we have to include this. How? Notice the word I used – “prefer”. It seems like this broad-enough “wellbeing” concept might just mean “what people prefer”. But try defining the idea of preference. Ideal preferences should be sincere and based on perfect information – after all, if you hear information that changes your preference, it’s your estimate of the consequences that changed, not the morally right action. So when we talk about preference, we need complete information, which means trying to answer the question “given perfect information about what you will experience (or even the entire state of the universe, depending on what preferences count) in option A and in option B, which do you prefer?” Now how is this judgement made? Might there be something – wellbeing, call it – which is what a preferrer always prefers?

Capturing any wellbeing/preference concept is difficult. Some things are very simple: a healthy life is preferable to death, for example, and given the remaining horribleness in the real world (e.g. sixty million people dying each year) a lot of our important moral decisions are about the simple cases. Even the problem of assigning QALY values to disease burdens has proven tractable, if not easy or uncontroversial. But solving the biggest problems is only the start.

An important empirical fact about human values is that they’re complex. Any simple utopia is a dystopia. Maybe the simplest way to construct a dystopia is to imagine a utopia and remove one subtle thing we care about (e.g. variety, choice, or challenge).

On one hand, we have strong theoretical reasons why we need to reduce everything to utilities to make moral decisions. On the other, we have the empirical fact that what counts as utility to people is very complex and subtle.

I think the basic framework of utilitarian ideas gives us a method, in the way that the ruler and compass gave the Greeks a method to begin toying with maths. Thinking quantitatively about how all minds everywhere are doing is probably a good way to start our species’ serious exploration of weird and/or big moral questions. However, modern utilitarianism may be an approximation, like Newton’s theory of gravity (except with a lot more ambiguity in its definitions), and the equivalent of general relativity may be centuries away. It also seems certain that most of the richness of the topic still eludes us.


Indirect arguments: what people think, and the history of ethics

In addition to the theoretical arguments above, we can try to weigh utilitarianism indirectly.

First, we can see what people think (we are talking about morality after all – if everyone hates it, that’s cause for concern). On one hand, out of friends with who I’ve talked about these topics with (the median example being an undergraduate STEM student), basically everyone favours some form of utilitarianism. On the other hand, a survey of almost a thousand philosophers found only a quarter accepting or leaning towards consequentialist ethics (slightly lower than the number of deontologists, and less than the largest group of a third of respondents who chose “other”). (However, two thirds endorse the utilitarian choice in the trolley problem, compared to only 8% saying not to switch (the rest were undecided).) My assumption is that a poll of everyone would find a significant majority against utilitarianism, but I think this would be largely because of the negative connotations of the word.

Second, we can look at history. A large part of what we consider moral progress can be summarised as a move to more utilitarian morality.

I am not an expert in the history of ethics (though I’d very much like to hear from one), but the general trend from rule- and duty-based historical morality to welfare-oriented modern morality seems clear. Consider perhaps the standard argument in favour of gay marriage: it’s good for some people and it hurts no one, so why not? Arguments do not get much more utilitarian. (Though of course, other arguments can be made with different starting points, for example a natural right to various freedoms.) In contrast the common counter-argument – that it violates the law of nature or god or at least social convention – is rooted in decidedly non-utilitarian principles. Whereas previously social disapproval was a sufficient reason to deny people happiness, today we assume a heavy, even insurmountable, burden of proof of any custom or rule that increases suffering on net.

A second trend in moral attitudes is often summarised as an “expanding moral circle”: granting moral significance to more and more entities. The view that only particular people of particular races, genders, or nationalities count as moral patients has come to be seen as wrong, and the expansion of moral patienthood to non-humans is already underway.

A concern for anything capable of experiencing welfare is built into utilitarianism. Utilitarianism also ensures that this process will not blow up to absurdities: rather than blindly granting rights to every ant, utilitarianism allows for the fact that the welfare of some entities deserves greater weight, and assures us there’s no need to worry about rocks.

It would be a mistake to say that our moral progress has been driven by explicit utilitarianism. Abolitionists, feminists, and civil rights activists had diverse moral philosophies, and the deontological language of rights and duties has played a big role. But consider carefully why today we value the rights and duties that we do, rather than those of past eras, and I think you’ll find that the most concise way to summarise the difference is that we place more value on welfare and preferences. In short, we are more utilitarian.

Two of the great utilitarian philosophers were Jeremy Bentham and John Stuart Mill, who died in the early and late 1800s respectively (today we have Peter Singer). On the basis of his utilitarian ethics, Bentham advocated for the abolition of slavery and capital punishment, gender equality, decriminalising homosexuality (an essay so radical at its time that it went unpublished for over a hundred years after Bentham’s death), and is especially known as one of the first defenders of animal rights. Mill also argued against slavery, and is especially known as an early advocate of women’s rights. Both were also important all-around liberals.

Nineteenth century utilitarians were good at holding moral views that were ahead of their time. I would not be surprised if the same were true today.


2020-07-26

EA ideas 3: uncertainty

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Moral uncertainty is uncertainty over the definition of good. For example, you might broadly accept utilitarianism, but still have some credence in deontological principles occasionally being more right.

Moral uncertainty is different from epistemic uncertainty (uncertainty about our knowledge, its sources, and uncertainty over our degree of uncertainty about these things). In practice these often mix – uncertainty over an action can easily involve both moral and epistemic uncertainty – but since is-ought confusions are a common trap in any discussion, it is good to keep these ideas firmly separate.


Dealing with moral uncertainty

Thinking about moral uncertainty quickly gets us into deep philosophical waters.

How do we decide which action to take? One approach is called “My Favourite Theory” (MFT), which is to act entirely in accordance to the moral theory you think is most likely to be correct. There are a number of counterarguments, many of which involve around problems of how we draw boundaries between theories: if you have 0.1 credence in each of 8 consequentialist theories and 0.2 credence in a deontological theory, should you really be a strict deontologist? (More fundamentally: say we have some credence in a family of moral systems with a continuous range of variants – say, differing by arbitrarily small differences in the weights assigned to various forms of happiness – does MFT require we reject this family of theories in favour of ones that vary only discretely, since in the former case the probability of a particular variant being correct is infinitesimal?). For a defence of MFT, see this paper.

If we reject MFT, when making decisions we have to somehow make comparisons between the recommendations of different moral systems. Some regard this as non-sensical; others write theses on how to do it (some of the same ground is covered in a much shorter space here; this paper also discusses the same concerns with MFT that I mentioned in the last paragraph, and problems with switching to “My Favourite Option” – acting according to the option that is most likely to be correct, summed over all moral theories you have credence in).

Another less specific idea is the parliamentary model. Imagine that all moral theories you have some credence in send delegates to a parliament, who can then negotiate, bargain, and vote their way to a conclusion. We can imagine delegates for a low-credence theory generally being overruled, but, on the issues most important to that theory, being able to bargain their way to changing the result.

(In a nice touch of subtlety, the authors take care to specify that though the parliament acts according to a typical 50%-to-pass principle, the delegates act as if they believe that the percent of votes for an action is the probability that it will happen, removing the perverse incentives generated by an arbitrary threshold.)

As an example of other sorts of meta-ethical considerations, Robin Hanson compares the process of fitting a moral theory to our moral intuitions to fitting a curve (the theory) to a set of data points (our moral intuitions). He argues that there’s enough uncertainty over these intuitions that we should take heed of a basic principle of curve-fitting: keep it simple, or otherwise you will overfit, and your curve will veer off in one direction or another when you try to extrapolate.


Mixed moral and epistemic uncertainty

Cause X

We are probably committing a moral atrocity without being aware of it.

This is argued here. The first argument is that past societies have been unaware of serious moral problems and we don’t have strong enough reasons to believe ourselves exempt from this rule. The second is that there are many sources of potential moral catastrophe – there are very many ways of being wrong about ethics or being wrong about key facts – so though we can’t point to any specific likely failure mode with huge consequences, the probability that at least one exists isn’t low.

In addition to an ongoing moral catastrophe, it could be that we are overlooking an opportunity to achieve a lot of good for cheap. In either case there would be a cause, dubbed Cause X, which would be a completely unknown but extremely important way of improving the world.

(In either case, the cause would likely involve both moral and epistemic failure: we’ve both failed to think carefully enough about ethics to see what it implies, and failed to spot important facts about the world.)

“Overlooked moral problem” immediately invites everyone to imagine their pet cause. That is not what Cause X is about. Imagine a world where every cause you support triumphed. What would still be wrong about this world? Some starting points for answering this are presented here.

If you say “nothing”, consider MacAskill’s anecdote in the previous link: Aristotle was smart and spent his life thinking about ethics, but still thought slavery made sense.


Types of epistemic uncertainty

I use the term "epistemic uncertainty" because the concept is broader than just uncertainty over facts. For example, our brains are flawed in predictable ways, and dealing with this is different from dealing with being wrong or having incomplete information about a specific fact.

Flawed brains

A basic cause for uncertainty is that human brains make mistakes. Especially important are biases, which consistently make our thinking wrong in the same way. This is a big and important topic; the classic book is Kahneman’s Thinking, Fast and Slow, but if you prefer sprawling and arcane chains of blog posts, you’ll find plenty here. I will only briefly mention some examples.

The most important bias to avoid when thinking about EA may be scope neglect. In short, people don’t automatically multiply. It is the image of a starving child that counts in your brain, and your brain gives this image the same weight whether the number you see on the page has three zeros or six after it. Trying to reason about any big problem without being very mindful of scope neglect is like trying to captain a ship that has no bottom: you will sink before you move anywhere.

Many biases are difficult to counter, but occasionally someone thinks of a clever trick. Status quo bias is a preference for keeping things as they are. It can often be spotted through the reversal test. For example, say you argue that we shouldn’t lengthen human lifespans further. Ask yourself: should we then decrease life expectancy? If you think that we should have neither more nor less of something, you should also have a good reason for why it just so happens that we have an optimum amount already. What are the chances that the best possible lifespan for humans also happens to be the highest one that present technology can achieve?


Crucial considerations

A crucial consideration is something that flips (or otherwise radically changes) the value of achieving a general goal.

For example, imagine your goal is to end raising cows for meat, because you want to prevent suffering. Now say there’s a fancy new brain-scanner that lets you determine that even though the cow ends up getting chucked into a meat grinder, on average the cow’s happiness is above the threshold for when non-existence is preferable to existence (assume this is a well-defined concept in your moral system). Your morals are the same as before, but now they’re telling you to raise more cows for meat.

An example of a chain of crucial considerations is whether or not we should develop some breakthrough but potentially dangerous technology, like AI or synthetic biology. We might think that the economic and personal benefits make it worth the expense, but a potential crucial consideration is the danger of accidents or misuse. There might be another crucial consideration that it’s better to have the technology developed internationally and in the open, rather than have advances made by rogue states.

There are probably many crucial considerations that are either unknown or unacknowledged, especially in areas that we haven’t thought about for very long.


Cluelessness

The idea of cluelessness is that we are extremely uncertain about the impact of every action. For example, making a car stop as you cross the street might affect a conception later that day, and might make the difference between the birth of a future Gandhi or Hitler later on. (Note that many non-consequentialist moral systems seem even more prone to cluelessness worries – William MacAskill points this out in this paper, and argues for it more informally here.)

I’m not sure I fully understand the concerns. I’m especially confused about what the practical consequences of cluelessness should be on our decision-making. Even if we’re mostly clueless about the consequences of our actions, we should base them on the small amount of information we do have. However, at the very least it’s worth keeping in mind just how big uncertainty over consequences can be, and there are a bunch of philosophy paper topics here.

For more on cluelessness, see for example:


Reality is underpowered

Imagine we resolve all of our uncertainties over moral philosophy, iron out the philosophical questions posed by cluelessness, confidently identify Cause X, avoid biases, find all crucial considerations, and all that remains is the relatively down-to-earth work of figuring out which interventions are most effective. You might think this is simple: run a bunch of randomised controlled trials (RCTs) on different interventions, publish the papers, and maybe wait for a meta-analysis to combine the results of all relevant papers before concluding that the matter is solved.

Unfortunately, it’s often the case that reality is underpowered (in the statistical sense): we can’t run the experiments or collect the data that we’d need to answer our questions.

To take an extreme example, there are many different factors that affect a country’s development. To really settle the issue, we might make groups of, say, a dozen countries each, give them different amounts of the development factors (holding everything else fairly constant), watch them develop over 100 years, run a statistical analysis of the outcomes, and then draw conclusions about how much the factors matter. But try finding hundreds of identical countries with persuadable national leaders (and at least one country must have a science ethics board that lets this study go forwards).

To make a metaphor with a different sort of power: the answers to our questions (on what effects are the most important in driving some phenomenon, or which intervention is the most effective) exist, sharp and clear, but the telescopes with which we try to see them aren’t good enough. The best we can do is interpret the smudges we do see, inferring as much as we can without the brute force of an RCT.

This is an obvious point, but an important one to keep in mind to temper the rush to say we can answer everything if only we run the right study.


Conclusions?

All this uncertainty might seem to imply two conclusions. I support one of them but not the other.

The first conclusion is that the goal of doing good is complicated and difficult (as is the subgoal of having accurate beliefs about the world). This is true, and important to remember. It is tempting to forget analysis and fall back on feelings of righteousness, or to switch to easier questions like “what feels right?” or “what does society say is right?”

The second conclusion is that this uncertainty means we should try less. This is wrong. Uncertainties may rightly redirect efforts towards more research, and reducing key uncertainties is probably one of the best things we can do, but there’s no reason why they should make us reduce our efforts.

Uncertainty and confusion are properties of minds, not reality; they exist on the map, not the territory. To every well-formed question there is an answer. We need only find it.

 

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2020-07-25

EA ideas 2: expected value and risk neutrality

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The expected value (EV) of an event / choice / random variable is the sum, over all possible outcomes, of {value of outcome} times {probability of that outcome} (if all outcomes are equally likely, it is the average; if they’re not, it’s the probability-weighted average).

In general, a rational agent makes decisions that maximise the expected value of the things they care about. However, EV reasoning involves more subtleties than its mathematical simplicity suggests, in both the real world and in thought experiments.

Is a 50% chance of 1000€ exactly as good as a certain gain of 500€ (that is, are we risk-neutral?), or a 50% chance of 2000€ with a 50% chance of a 1000€ loss instead?

Not necessarily. A bunch of research (and common sense) says people put decreasing value on an additional unit of money: the thousandth euro is worth more than the ten-thousandth. For example, average happiness scales roughly logarithmically with per-capita GDP. The thing to maximise in a monetary tradeoff is not the money, but the value you place on money; with a logarithmic relationship, the diminishing returns mean that more certain bets are better than naive EV-of-money reasoning implies. A related reason is that people weight losses more than gains, which makes the third case look worse than the first even if you don’t assume a logarithmic money->value function.

However, a (selfish) rational agent will still maximise EV in such decisions – not of money, but of what they get from it.

(If you’re not selfish and live in a world where money can be transferred easily, the marginal benefit curve of efficiently targeted donations is essentially flat for a very long time – a single person will hit quickly diminishing returns after getting some amount of money, but there are enough poor people in the world that enormous resources are needed before you need to worry about everyone reaching the point of very low marginal benefit from more money. To fix the old saying, albeit with some hit to its catchiness: “money can buy happiness only (roughly) logarithmically for yourself, but (almost) linearly in the world at large, given efficient targeting”.)

In some cases, we don’t need to worry about wonky thing->value functions. Imagine the three scenarios above, but instead of euros we have lives. Each life has the same value; there’s no reasonable argument for the thousandth life being worth less than the first. Simple EV reasoning is the right tool.


Why expected value?

This conclusion easily invites a certain hesitation. Any decision involving hundreds of lives is a momentous one; how can we be sure of exactly the right way to value these decisions, even in simplified thought experiments? What’s so great about EV?

A strong argument is that maximising EV is the strategy that leads to the greatest good over many decisions. In a single decision, a risky but EV-maximising choice can backfire – you might take a 50-50 bet of saving 1000 lives and lose, in which case you’ll have done much worse than picking an option of certainly saving 400. However, it’s a mathematical fact that given enough such choices, the actual average value will tend towards the EV. So maximising EV is what results in the most value in the long run.

You might argue that we’re not often met with dozens of similar momentous decisions. Say that we’re reasonably confident the same choice will never pop up again, and certainly not many times; doesn’t the above argument no longer apply? Take a slightly broader view though, and consider which strategy gets you the most value across all decisions you make (of which there will realistically be many, even if no single decision occurs twice): the answer is still EV maximisation. We could go on to construct crazier thought experiments – toy universes in which only one decision ever occurs, for example – and then the argument really begins to break down (though you might try to save it by some wild scheme of imagining many hypothetical agents faced with the same choice and consider a Kantian / rule-utilitarian principle of deciding by answering the question of which strategy would be right if it were the one adopted across all countless hypothetical instances of this decision).

There are other arguments too. Imagine 1000 people are about to die of a disease, and you have to decide between a cure that will certainly cure 400 versus an experimental one that will either cure everyone or save no-one. Imagine you are one of these people. In the first scenario, you have a 40% chance of living; in the second, a 50% chance. Which would you prefer?

On a more mathematical level, von Neumann (an all-around polymath) and Morgenstern (co-founder of game theory with von Neumann) have proved that under fairly basic assumptions of what is rational behaviour, a rational agent acts as if they’re maximising the EV of some preference function.


Problems with EV

Diabolical philosophers have managed to dream up many challenges for EV reasoning. For example, imagine there’s two dollars on the table. You toss a coin; if it’s heads you take the money on the table, if it’s tails the money on the table doubles and you toss again. You have a 1/2 chance of winning 2 dollars, 1/4 chance of winning 4, 1/8 chance of winning 8, and so on, for a total EV of 1/2 x 2 + 1/4 x 4 + … = 1 + 1 + … . The sequence diverges to infinity.

Imagine a choice: one game of the “St. Petersburg lottery” described above, or a million dollars. You’d be crazy not to pick the latter.

Is this a challenge to the principle of maximising EV? Not in our universe. We know that whatever casino we’re playing at can’t have an infinite amount of money, so we’re wise to intuitively reject the St. Petersburg lottery. (This section on Wikipedia has a very nice demonstration of why, even if the casino is backed by Bill Gates’s net worth, the EV of the St. Petersburg game is less than $40.)

The St. Petersburg lottery isn’t the weirdest EV paradox by half, though. In the Pasadena game, the EV is undefined (see the link for a definition, analysis, and an argument that such scenarios are points against EV-only decision-making). Nick Bostrom writes about the problems of consequentialist ethics in an infinite universe (or a universe that has a finite probability of being infinite) here.

There’s also the classic: Pascal’s wager, the idea that even if the probability of god existing is extremely low, the benefits (an eternity in heaven) are great enough that you should seek to believe in god and live a life of Christian virtue.

Unlike even Bostrom’s infinite ethics, Pascal’s wager is straightforwardly silly. We have no reason to privilege the hypothesis of a Christian god over the hypothesis – equally probable given the evidence we have – that there’s a god who punishes us exactly for what the Christian god rewards us for, or that god is a chicken and condemns all chicken-eaters to an eternity of hell. So even if you accept the mathematically dubious multiplication of infinities, Pascal’s wager doesn’t let you make an informed decision one way or another.

However, the general format of Pascal’s wager – big values multiplied by small probabilities – is the cause of much of EV-related craziness, and dealing with such situations is a good example of how naive EV reasoning can go wrong. The more general case is often referred to as Pascal’s mugging, and exemplified by the scenario (see link) where a mugger threatens to torture an astronomical amount of people unless you give them a small amount of money.


Tempering EV extremeness with Bayesian updating

Something similar to Pascal’s mugging easily happens if you calculate EVs by multiplying together very rough guesses involving small probabilities and huge outcomes.

The best and most general approach to these sorts of issues is laid out here.

The key insight is to remember two things. First, every estimate is a probability distribution: if you measure a nail or estimate the effectiveness of a charity, the result isn’t just your best-guess value, but also the uncertainty surrounding it. Second, Bayesian updating is how you change your estimates when given new evidence (and hence you should pay attention to your prior: the estimate you have before getting the new information).

Using some maths detailed here, it can be shown that if your prior and measurement both follow normal distributions, then your new (Bayesian) estimate will be another normal distribution, with a mean (=expected value) that is an average of the prior and measurement means, weighted by the inverse variance of the two distributions. (Note that the link does it with log-normal distributions, but the result is the same; just switch between variables and their logarithms.)

Here’s an interactive graph that lets you visualise this.

The results are pretty intuitive. Let’s say our prior for the effectiveness of some intervention has a mean of zero. If we take a measurement with low variance, our updated estimate probability distribution will shift most of the way towards our new measurement, and its variance will decrease (it will become narrower):

Red is the probability distribution of our prior estimate. Green is our measurement. Black is our new belief, after a Bayesian update of our prior with the measurement. Dotted lines show the EV (=average, since the distributions are symmetrical) for each probability distribution. You can imagine the x-axis as either a linear or log scale.

If the same measurement has greater variance, our estimates shift less:


And if we have a very imprecise measurement – for example, we’ve multiplied a bunch of rough guesses together – the estimate barely shifts even if the estimate is high:


Of course, we can argue about what our priors should be – perhaps, for many of the hypothetical scenarios with potentially massive benefits (for instance concerning potential space colonisation in the future), the variance of our prior should be very large, in which case even highly uncertain guesses will shift our best-guess EV a lot. But the overall point still stands: if you go to your calculator, punch in some numbers, and conclude you’ve discovered something massively more important than anything else, it’s time to think very carefully about how much you can really conclude.

Overall, I think this is a good example of how a bit of maths can knock off quite a few teeth from a philosophical problem.

(Here’s a link to a wider look at pitfalls of overly simple EV reasoning with a different framing, by the same author as this earlier link. And here is another exploration of the special considerations involved with low-probability, high-stakes risks.)


Risk neutrality

An implication of EV maximisation as a decision framework is risk neutrality: when you’ve measured things in units of what you actually care about (e.g. converting money to the value it has for you as discussed above), you should be neutral about the choice between 10% chance of 10 value units and 100% chance of 1, and you really should prefer a 10% chance of 11 “value units” over a 100% chance of 1 “value unit”, or a 50-50 bet between losing 10 and gaining 20 over a certain gain of 14.

This is not an intuitive conclusion, but I think we can be fairly confident in its correctness. Not only do we have robust theoretical reasons for using EV, but we can point to specific bugs in our brains that makes us balk at risk-neutrality: biases like scope neglect, which makes humans underestimate the difference between big and small effects, or loss aversion, which makes losses more salient than gains, or a preference for certainty.

$$$%%IF YOU SEE DOLLAR SIGNS IN THE NEXT SECTION, EQUATION RENDERING VIA MATHJAX IS NOT WORKING IN YOUR BROWSER$$$

Stochastic dominance (an aside)

Risk neutrality is not necessarily specific to EV maximisation. There’s a far more lenient, though also far more incomplete, principle of rational decision making that goes under the clumsy name of “stochastic dominance”: given options $$A$$ and $$B$$, if the probability of a payoff of $$X$$ or greater is more under option $$A$$ than option $$B$$ for all values of $$X$$, then $$A$$ “stochastically dominates” option B and should be preferred. It’s very hard to argue against stochastic dominance.

Consider a risky and a safe bet; to be precise, call them option $$A$$, with a small probability $$p$$ of a large payoff $$L$$, and option $$B$$, with a certain small payoff $$S$$. Assume that $$pL > S$$, so EV maximising says to take option $$A$$. However, we don’t have stochastic dominance: the probability of getting a small amount of value $$v$$ ($$v < S$$) is greater with $$B$$ than $$A$$, whereas the probability of getting a large amount of value ($$S < v < L$$) is greater with option $$A$$.

The insight of this paper (summarised here) is that if we care about the total amount of value in the universe, are sufficiently uncertain about this total amount, and make some assumptions about its distribution, then stochastic dominance alone implies a high level of risk neutrality.

The argument goes as follows: we have some estimate of the probability distribution $$U$$ of value that might exist in the universe. We care about the entire universe, not just the local effects of our decision, so what we consider is $$A + U$$ and $$B + U$$ rather than $$A$$ and $$B$$. Now consider an amount of value $$v$$. The probability that $$A + U$$ exceeds $$v$$ is the probability that $$U > v$$, plus the probability that $$(v - L) < U < v$$ and $$A$$ pays off $$L$$ (we called this probability $$p$$ earlier). The probability that $$B + U$$ exceeds $$v$$ is the probability that $$U > v - S$$.

Is the first probability greater? This depends on the shape of the distribution of $$U$$ (to be precise, we’re asking whether $$P(U > v) + p P(v - L < U < v) > P(U > v - S)$$, which clearly depends on $$U$$). If you do a bunch of maths (which is present in the paper linked above; I haven’t looked through it), it turns out that this is true for all $$v$$ – and hence we have stochastic dominance of $$A$$ over $$B$$ – if the distribution of $$U$$ is wide enough and has a fat tail (i.e. trails off slowly as $$v$$ increases).

What’s especially neat is that this automatically excludes Pascal’s mugging. The smaller the probability $$p$$ of our payoff is, the more stringent the criteria get: we need a wider and wider distribution of $$U$$ before $$A$$ stochastically dominates $$B$$, and at some point even the most stringent Pascalian must admit $$U$$ can’t plausibly have that wide of a distribution.

It’s far from clear what $$U$$’s shape is, and hence how strong this reasoning is (see the links above for that). However, it is a good example of how easily benign background assumptions introduce risk neutrality into the problem of rational choice.


Implications of risk neutrality: hits-based giving

What does risk neutrality imply about real-world altruism? In short, that we should be willing to take risks.

A good overview of these considerations is given in this article. The key point:

[W]e suspect that, in fact, much of the best philanthropy is likely to fail.

For example, GiveWell thinks that Deworm the World Initiative probably has low impact, but still recommends them as one of their top charities because there’s a chance of massive impacts.

Hits-based giving comes with its own share of problems. As the article linked above notes, it can provide a cover for arrogance and make it harder to be open about decision-making. However, just as high-risk high-reward projects make up a disproportionate share of successes in scientific research and entrepreneurship, we shouldn’t be surprised if the bulk of returns on charity comes from a small number of risky bets.

 

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EA ideas 1: rigour and opportunity in charity

2.2k words (8 minutes)

Effective altruism (EA) is about trying to carefully reason how to do the most good. On the practical side, EA has inspired the donation of hundreds of millions of dollars to impactful charities, and lead to many new organisations focused on important causes. On the theoretical side, it has lead to rigorous and precise thought on ethics and how to apply it in the real world.

The intellectual work that has come out of EA is valuable, especially in two ways.

First, much EA work is exceptional in the breadth and weight of the matters it considers. It is interdisciplinary, including everything from meta-ethics to interpreting studies on the effectiveness of vaccination programs in developing countries. Because of its motivation – finding and exploring the most important problems – it zeros in on the weightiest issues in any particular area. EA work is a goldmine of interesting writing, particularly if you find yourself drawn in a discipline-agnostic way to all the biggest questions.

Second, EA often has a scientific precision of argument that is often missing from discussions on abstract things (e.g. meta-ethics) or emotionally charged issues (e.g. saving lives).

This post explains the motivations behind EA, and has a table of contents for this post series.


Altruism, impartial welfarist good, and cause neutrality

I will have more to say in a later post about specific philosophical issues in defining what is moral. For now I will hope that the idea of an impartial welfare-oriented definition of good is sufficiently defensible that I will not be mauled to death by moral philosophers before that post (though if it doesn’t happen by then, it will certainly happen afterwards).

Impartial (in the sense of considering everyone fairly, and giving the same answer regardless of who’s doing the judging) and welfare-oriented (in the sense of valuing happiness, meaning, fulfilment of preferences, and the absence of suffering) good is an intuitive and fairly unobjectionable idea. Yet if we take it as a goal, it points towards a different idea of charity than the current norm.

Most charities are single-issue charities. This generally makes sense: better to have one organisation be really good at distributing malaria nets and one really good at advocating for taking nuclear weapons off high alert, than to have one organisation doing a mediocre job at both (malaria net delivery via ICBM?).

But the siloing of causes often goes further. If the effectiveness of an intervention is considered, it is often after choosing a cause area. To weigh cause areas against each other, to judge the needs of African children against, say, factory farmed pigs, seems like a faux pas at best, and a sin at worst (for a particularly incendiary tirade on the topic, see this article).

However, if we hold ourselves to an impartial welfarist idea of good, this judgement must be made. An artist might choose what to paint based on how they want to express themselves or on a sudden flash of inspiration. A would-be altruist refusing to weigh causes against each other and instead selecting them on the basis of passion or inspiration is acting like our artist. In the artist’s case it doesn’t matter, but the altruist, in doing so, implicitly values their own choice and/or self-expression over the good that their actions might do. This is not altruism by our definition of good.

Of course, people differ in their knowledge and talents, and these tend to align with inspiration. In the real world, it may well be that your greater ability, drive, and/or knowledge in one area outweighs the greater efficiency at which results convert to goodness in some other area. We will also see arguments for not placing all our bets on the same cause, and explore the enormous uncertainties that come in trying to compare causes. But the idea of cause-neutrality – that causes are comparable, and that making these comparisons is an important part of the job of any would-be altruist – remains.


Effectiveness

Focusing on the idea of impartial welfarist good also makes it clear that, in trying to do good, we should focus on the good our actions result in. This may seem like an obvious statement, but it is not true of much charitable work.

For example, we tend to emphasise the sacrifices of the donor over the benefits of the recipients. Consider old tales of people like Francis of Assisi. Their claim to virtue (and sainthood) comes from giving away all their possessions, but the question of how much good this did to the beggars doesn’t come up. This attitude continues in the many modern charity evaluators that focus on metrics like percentage of money spent on overhead costs. Paying big salaries to recruit the best management and administration may genuinely be a cost-effective way of increasing the total good done, but it conflicts with our stereotype of self-sacrificing do-gooders. Of course, there is virtue in selfless sacrifice, but we should remember that the goal of charity is to make recipients better off, not to rank donors.

As with many things humans do, acts of charity often aren't based on rational calculation. Some consider this a good thing: altruistic acts should come from hearts, not spreadsheets. This is wrong – if you care about impartial welfarist good.

It is a fact about our world that good charity is hard, and that charities have vast differences in cost-effectiveness. When one charity results in ten or a hundred times more healthy years of life per dollar spent than another, boring details of statistical effectiveness become important moral facts. (This is true not just of charities, but most kinds of projects that might impact many people – government policy, activism, and so on.)

When the difference in effectiveness between different interventions is often greater than the difference to doing nothing at all, and when these differences are often measured in lives, effectiveness considerations are critical in any attempt to do good.

There is a role for simple, comforting altruism, but this role isn’t making big decisions over how to benefit others. These decisions deserve more than goodwill. They deserve to be made right.


Opportunity

Debates over charitable giving often centre on questions of moral duty and obligation (a good example is Famine, Affluence, and Morality, Peter Singer’s classic paper that laid some of the foundations of what later became EA).

Another framing is to think of it as an opportunity. To someone who cares about impartial welfarist good, altruistic acts are not a burden but an opportunity to achieve valuable things. In particular, there are many reasons to think that we (as in developed-world humans of the early 21st century) have an exceptionally large opportunity to do good.

First, our values are better than those of people in preceding eras. This statement implies many philosophically contentious points, but for the time being I will not defend them, instead appealing to what I hope to be a common sense conviction that human morality isn’t nearly relative enough that it is impossible to differentiate modern secular humanist values from values that support war, slavery, and boundaries on personhood that exclude most people.

(Of course, this statement also suggests that our current moral views are far from perfect too. This is important, very likely true, and will be discussed at length in future posts. The fact that this is increasingly recognised is hopefully a hint that we are at least on the right track.)

Second, we have more resources than people in previous eras. There is also large variation in global income, meaning that if you happen to live in a rich country, you can help many others for cheap. A 2-adult, 1-child UK household with a total income of £30,000 is in the top 10% of the world income distribution and 7 times richer than the median global household.

Third, knowledge on what is effective has increased and technology make it easier to apply this knowledge. Today GiveWell’s thorough charity research can multiply the impact of giving. Twenty years ago, there was no GiveWell. Two hundred years ago, donation guidance, if it existed, might have consisted of the church telling you to donate to them so they can convert people and push their social values.

Fourthly, we may have an unprecedented ability to affect where civilisation is headed (for thoughts on this topic, see for example this link). The steepness of technical advancement increases the variance of possible future outcomes: in the next few decades we might nuke each other or engineer a pandemic – or we can set ourselves on a trajectory towards becoming a sustainable civilisation with billions of happy inhabitants that lasts until the stars burn down. Past eras didn’t have similar power, and if the future goes well humanity will no longer be as vulnerable to catastrophe as we are today, so people living roughly today might have exceptional leverage.


Common EA cause areas

The cause areas most frequently seen as important, and most specific to EA relative to what other charities focus on, are:

  • Global poverty, because the developing world is big, poor, and has many tractable problems with well-researched solutions.
  • Animal welfare, because it is largely ignored, and potentially huge in scope (depending on how much animal lives are valued).
  • Existential risk: focusing on avoiding human extinction or other irrevocable civilisational collapses, because new technologies (AI and biotech in particular) make them scarily plausible. (Sometimes this is motivated even more strongly by long-termism: specifically caring about the overwhelming number of happy future lives that may come to exist over the long-term future if we don't mess things up).

These are far from the only cause areas discussed in EA. Many EA-affiliated people argue either against some of the above, for the overwhelming importance of one relative to the other, or for entirely different causes.


Effective altruism in practice

In practice, EA can seem weird and theoretical.

The main reason for EA weirdness is that it casts a wide net. Everyone agrees that international peacekeeping is an important project, and also a serious one: it doesn’t get much more serious than world leaders intervening to get men with big guns to have big talks about their big disputes. On the other hand, the colonisation of space is important, but seems to have very little gravitas indeed; it’s something out of a science fiction novel. However, just as it’s a brute fact about the world that there are lots of violent people with big guns, it’s also a brute fact that space is big; both of these facts should be taken seriously when considering the long-run future. There might be a clear line between sci-fi and current affairs in a bookshop, but reality doesn't care about genre.

More generally, it’s important to keep in mind that every moral advance started out as a weird idea (for example, it was once considered crazy to suggest that women should get to vote).

Parts of EA are very theoretical. This, too, is by design. Future posts will show many cases where which way we resolve a very abstract issue has a big impact on what the right practical action is – and in many of these cases it is unclear what the right resolution is. Finding out clearly matters.

If EA seems too theoretical or mathematical to you, consider two points. First, whatever the field, doing complex things in the real world tends to involve (or be built on) theoretical heavy lifting. Second, most charity efforts don’t pay much attention to theoretical issues; EA is at very least a helpful counterweight, and likely to uncover missed opportunities.

Whenever the goal is to do good, it is easy to be overwhelmed by feelings of righteousness and forget theoretical scruples. Unfortunately we don’t live in the simple world where what feels right is the same as what is right.

The core of effective altruism is not any particular moral theory or cause area, but a conviction that doing good is both important and difficult, and hence worthy of thought.


This post series:

  1. Rigour and opportunity in charity: this post.
  2. Expected value and risk neutrality: a rational agent maximises the expected value of what it cares about. Expected value reasoning is not free of problems, but, outside extreme thought experiments and applied carefully, it clears most of them, including "Pascal's mugging" (high-stakes, low-probability situations). Expected value reasoning implies risk neutrality. The most effective charity may often be a risky one, and gains from giving may be dominated by a few risky bets.
  3. Uncertainty: we are uncertain about both what is right and what is true (being mindful of the difference is often important). Moral uncertainty raises the question of how we should act when we have credence in more than one moral theory. Uncertainty about truth has many sources, including ones broader than uncertainty about specific facts, such as our biases or the difficulty of confirming some facts. These uncertainties suggest we are unaware of huge problems and opportunities.
  4. Utilitarianism: while not a necessary part of EA thinking, utilitarianism is the most successful description of the core of human ethics so far. In principle (if not practice, due to the complexity of defining utility), it is capable of deciding every moral question, an important property for a moral system. Our moral progress over the past few centuries can be summarised as a transition to more utilitarian morality.


(More coming)

2020-05-09

Short reviews: fiction

Cryptonomicon (Neal Stephenson)


Cryptonomicon is a hard novel to summarise. It is about World War II code-breakers and 1990s tech entrepreneurs, but also manages to concern itself with most other things as well.

I first read Cryptonomicon over two years ago. However, it is a massive book, and since it happens in the same universe as The Baroque Cycle, I assumed reading it again would reveal many new things. I was not wrong.

Neal Stephenson has a humorously extravagant (baroque?) writing style that is always entertaining to read, but in Cryptonomicon it is taken to an extreme. Stephenson turns mundane activities like writing a business plan, eating cereal, taking a car ride in the Philippines, and visiting a dentist into lengthy but hilarious tangents. Do they contribute to the plot? Who cares!

As this is a Neal Stephenson novel, certain vices will also be present. A printed version of the book, dropped from a bomber, would punch a hole through the deck of a Japanese warship. The plot meanders to an extent that puts most rivers to shame. And some things are just plain weird.

But overall, Cryptonomicon makes for a great read for anyone with the time to spare, and an interest in codebreaking, history, war, mathematics, the Internet, the financial industry, or technology.


Exhalation (Ted Chiang)


“Exhalation”, this short story collection’s titular work, is the greatest short story I have ever read. (You may read it online for free – and legally, as far as I can tell – here). The careful setup builds to a beautiful and intuitive analogy that make the philosophical points at the end hit hard.

Based on the strengths of “Exhalation” (the short story), I bought Exhalation (the short story collection). None of the other stories surpass “Exhalation”, though they are mostly good and sometimes excellent.

Reading a Ted Chiang story is like watching an eerily intricate machine in action, or listening to a Bach fugue: the feeling is one of orderliness and precision combined with an almost casual ease. The premise of each story is fundamentally a thought experiment; a “what-if” question knocks down one domino and the story follows its consequences all the way down the chain. Nothing is wasted or in excess, and the beats of the pacing come like metronome beats. In the best of the stories, these beats are almost undetectable at first, gradually building up into dawning revalation as the pieces fall together and the story reaches its climax.

Aside from “Exhalation”, there are two stories that stand out.

“The Truth of Fact, the Truth of Feeling” is a thoughtful exploration of the effect of the medium on what is seen as true (a topic that Neil Postman would feel right at home with). The story cleverly parallels the story of a person in an African village being introduced to literacy in the past, and a person in the future grappling with the consequences of technology that records everything people see. In a world of cautionary tales about technology stealing our identities, destroying our communities, or letting dinosaurs loose in the park, Chiang’s take on this issue is surprisingly forward-looking.

In “Omphalos” (an Ancient Greek word for “navel”, as in the expression “navel of the world”), the what-if question is: what if creationism were true, but humanity was a side-effect rather than the pinnacle of creation? The story is told in the form of prayers to god. Chiang takes the reader on a tour of what the scientific facts of this world look like: old trees with no growth rings in the middle, mummified people without navels, and so on, until finally a physics discovery, while confirming without doubt the existence of miracles, also leads inevitably to the conclusion that we are not the purpose of god’s creation. All this takes place in parallel with the emotional arc of the central character, which is told in a sympathetic and realistic manner.


Summerland (Hannu Rajaniemi)


The year is 1938. The Spanish Civil War rages on, Europe braces for war, Queen Victoria reigns from the afterlife, and the Soviets are merging souls into a godlike overmind, starting with Lenin’s.

In the alternative universe of Summerland, Marconi discovered more than he bargained for when working with radio transmission, and soon enough ectotanks and other supernatural weaponry were being deployed in World War I. Since then much of early-1900s spiritualism has been proven right.

Most significant is Summerland, an afterlife where souls can lodge themselves (provided they have a ticket) and even interact to a limited extent with the living.

In terms of plot, Summerland is a fairly straightforward spy novel. This is executed well (though my judgement may not be representative of those who know more about spy novels), but the premise is what makes Summerland special.

(Rajaniemi is best known for his far-future science fiction trilogy, which starts with The Quantum Thief; this is also recommended.)


The Curse of Chalion (Lois McMaster Bujold)


At the time of writing, the “Reception” section of the Wikipedia page for this book tells me nothing but “The book has received a number of reviews”.

This rather underwhelming (though doubtlessly accurate) statement does not do the book justice. The Curse of Chalion shines not through outstanding excellence in one respect, but rather by bringing a variety of good elements together: characters that feel like real people, an atmospheric setting, and above all a hard-to-pin-down tastefulness where nothing is in excess.

If I had to critique something, some of the turning points in the plot are rather deus ex machina. However, overall the book is a great example of fantasy built on literary merits rather than genre props, and makes for a very enjoyable story to get lost in.

(The introduction of the Wikipedia article, however, is little but a list of all the awards the book has won.)


Unsong (Scott Alexander)


In the beginning God created the heavens and the Earth. For a while, everything was fine. Then Thamiel, the left hand of God, appears in the centre of the Earth and corrupts a third of the angelic host. A war begins between the angels and demons, in which the demons gain the upper hand. Their victory is averted only when the mathematically talented archangel Uriel initiates his backup plan: switching the world from running on divine light to running on mathematical laws. Angels and demons both are reduced to mere metaphors, and the world is saved.

Saved, that is, until humans get very good at harnessing those laws and send Apollo 8 on a trip around the moon in 1968. Unfortunately all space beyond the moon is simply an illusion to make the universe seem consistent with the physics that now reigns on Earth. Instead of looping around the moon, Apollo 8 crashes into the edge of the world, damaging the delicate celestial machinery that Uriel put into place to maintain his conversion.

Various glitches start to show up in the working of the world. Angels and demons begin returning: Uriel reappears in a hurricane in the Mexican Gulf, from where he plays the role of an overworked sysadmin issuing a constant stream of patches to prevent physics from crashing, while demons spring up from Lake Baikal and start invading Russia.

The backstory of Unsong, told in various short excerpts throughout the book, continues with a very clever account of how the world reacts to this turn of events. Cold War politicking continues; for example, at one point Henry Kissinger successfully convinces President Nixon to ally with Hell in order to keep the Russians in check.

The main plot line begins in 2017. In this universe, kabbalah works. In particular, it makes possible the discovery of Names of God – words which have magical powers, but whose distribution is controlled by strict copyright laws. The main character, Aaron Smith-Teller, is a gifted kabbalist, but works a low-paid job helping a company find Names: he reads potential Names off a computer screen all day long, and if he finds a Name, gives it over to the company. The process cannot be automated because computers lack a soul and hence can’t detect which words are Names, necessitating this sort of low-skill work.

Unsong is remarkable not just for its crazy premise, but for the consistency and ruthlessness of its internal logic (which characters do not fail to exploit). Imagine you stumble across a Name that grants souls to inanimate objects. What do you do? That’s obvious: use it on a computer, have it start searching for new Names at superhuman speed, sell the Names for profit, buy more computers, and continue in this vein until you have magic powers beyond your dreams and can take over the world. If the Bible is literally true, what is the overriding moral priority? Simple: end the existence of hell; countless people suffering eternal torture for vague reasons cannot be part of a just universe.

The central question that many of Unsong’s characters grapple with is the problem of theodicy: why would a good god create a world with so much evil? This question does not have direct relevance to our own world, but it leads to other interesting questions (as well as giving the author a chance to flaunt their ingenuity; the book actually has a plausible answer). Together with characters who are often both idealistic and ruthless – I’m particularly fond of Jalaketu West, AKA “The Comet King” – this makes the book a good exploration of many moral themes.

Be warned, though: Unsong is about a universe where words, rather than equations, are the building blocks of reality. This leads to a lot of perverse verbal ingenuity, including more puns than can possibly be healthy. If you don’t want to read about characters who protest at the World’s Fair by waving signs saying “No it isn’t!”, or how atheists also include a leviathan in their mythology by calling the whole world a giant fluke, stay away.

Unsong was published online, chapter by chapter. This means two things, one bad and one good. First, it is a bit less polished than a published novel might be. Second, you can read it for free online.

Short reviews: non-fiction

The Feynman Lectures on Physics (Richard Feynman)


The Feynman Lectures on Physics (FLOP) is an incredible resource on basic physics. Feynman has an inimitable style: he is always clear, never the slightest bit pretentious, and has an eerie ability to cut through tangles of models, assumptions, and equations to get at the fundamental point. Often you can feel Feynman’s infectious enthusiasm through the page.

There are some issues with trying to learn physics from FLOP. There are no exercises, so you cannot test your understanding very easily.

Another reason is that the easy flow and elegant arguments make it less structured. If a typical textbook is like taking an official tour through a city, methodically exploring everything there is to see, the general feel of FLOP is more of chasing after a boundlessly enthusiastic tour guide as he zips from place to place using various shortcuts, leaving you with the nagging feeling that, while it was certainly very fun, you might not be able to retrace the route afterwards.

Feynman has a remarkable ability to introduce just enough background to pull off some proof or argument. This makes for some brilliant arguments that are fun to follow, but, particularly when it comes to mathematical tricks, left me with the feeling that if I didn’t have more background than Feynman introduces, I would be lost.

(Given a solid understanding of calculus and complex numbers, there are no great leaps required to follow the mathematics in volume 1. Volume 2 deals mainly with electromagnetism, which relies on vector calculus; at the time I was reading it, I didn’t have a solid grasp on that and this made parts difficult to follow. I still haven’t had a chance to read through everything in the second half of volume 2, and have read nothing from volume 3 and so cannot comment on it.)

Overall, FLOP is a brilliant resource. Perhaps it works best as a reference volume; there are many arguments that I do not remember off the top of my head, but which I remember are presented with extreme clarity in FLOP. Of course, without reading through at least once, how will you know what’s in it?


The Character of Physical Law (Richard Feynman)


The Character of Physical Law is based on another series of lectures Feynman that gave. It attempts to squeeze out maximum understanding and reflection about what physics is about from a minimum of abstruse maths.

It succeeds.

The focus is not on what the laws themselves are, but rather on the common themes in many of them: conservation principles, symmetry, and, of course, maths. The combination of clear explanation and reflection without pretence or overstretched philosophy is unbeatable.

If you read one popular physics book, make it this one. It is as close to the heart of physics as you can get without heavy mathematics.

If you are serious about physics, you will of course have to dive into the maths. But read this book anyways.


Origin Story: A Big History of Everything (David Christian)


I have rarely agreed with the purpose of a book as much as I do with the purpose of Origin Story.

The idea is that an origin story explaining where the world came from and what humanity’s place in it is has been a foundational part of most human cultures in history. Ironically, just as our civilisation is now figuring out the real answers to these questions, a collective understanding of our “origin story” is missing. This is the gap that Origin Story – and the field of big history in general – aims to plug.


The Great Leveler: Violence and the History of Inequality (Walter Scheidel)


Inequality is a trendy topic. Coherent insights into its history and how to quantify it are notably less trendy.

The Great Leveler provides both in spades. Optimism is in somewhat shorter supply. Scheidel identifies “Four Horsemen of Leveling” that have historically driven large decreases in inequality: total war, violent revolution, state collapse, and pandemics. If, as Scheidel cautions, welfare democracies probably won’t buck this trend, it looks like coronavirus is our only chance.


The Strategy of Conflict (Thomas Schelling)


You are in a car, driving directly towards another car. You will soon crash. The rules of the game are simple: the first one to swerve loses. How do you win? You close your eyes, throw away the steering wheel - basically, anything that both removes your ability to act and credibly signals this to your opponent.

The Strategy of Conflict is all about delightfully - and sometimes scarily - counterintuitive problems in game theory, in particular conflict of the nuclear sort. The general theme is that reducing your ability to make choices and committing to irrational acts can be the most powerful tools at your disposal. If you can commit to something in advance, regardless of whether it is in your rational interest to do it when the time comes, you can change the payoffs for your opponent, and hence possibly change what they calculate their best action to be.


The Doomsday Machine (Daniel Ellsberg)


My brief notes on this book snowballed into a full review, which you can find here. If you’re getting tired of the coronavirus pandemic, why not put things into perspective by reading about nuclear war?


Founders at Work (Jessica Livingston)


Founders at Work is a collection of interviews with startup founders. The book doesn’t try to be anything fancy, or make any deep conclusions about how the technology industry works. Its main value – and this is not a trivial thing – is as a source of “virtual experience” that you can download into your brain. Reading dozens of founders reflecting on their experiences with the guidance of a knowledgeable interviewer is the second-best thing to having that experience yourself.
Perhaps the two most basic and recurring themes are:
  1. In a (good) startup, everything is as barebones, minimalist, and plain as possible. The working place might be the stereotypical garage, someone’s apartment, or there might not even be one. Money is saved in endlessly creative ways. At most, you occasionally might have to dress up or pretend to have a normal office to impress investors. This theme is summarised by a story told in the introduction: some people tried to figure out how to make a sports car go faster, and eventually realised the key was to remove everything that makes it look like it goes fast.
  2. In the early stages, no one has any idea what they’re doing


Security Engineering (Ross Anderson)


The lecturer for my current software & security engineering course is publishing the third edition of his security engineering textbook online chapter by chapter (“like Dickens’ novels”, as he describes it). The textbook is extremely readable, and many of the case studies are both illuminating and funny. Read it here.

Be warned that most of the chapters will disappear from the website for several years after the book is published. However, they will return afterwards, and the same page linked above also has all the chapters from the second edition, which has already passed this period and is free online forever.