The Suffering Equation
Some groups of people suffer more than others — and there is a better way to understand why.
In The Beginning…
Although the view I put forward in this article is in no way religious, I suppose it does owe a debt to religion, in a sense. This is because it stems from a well-known critique of religious stories about suffering that involve God. The Scottish Enlightenment philosopher David Hume famously asked (regarding God):
Is he willing to prevent evil, but not able? then is he impotent. Is he able, but not willing? then is he malevolent. Is he both able and willing? whence then is evil? ¹
This question, which was perhaps initially an open question dating back as far as the Ancient Greek philosopher Epicurus, was eventually used as an argument against the existence of God (aka the modern “problem of evil.”)
But the existence of God is not the topic of this post. Rather, it is the concept that ability (which I further divide into aspects of power and knowledge) and benevolence / malevolence are relevant to evil that is of importance here. More specifically, it is the combination of these factors that matters, because (as Hume’s argument notes):
- A god that is merely good would want to eliminate all evil (i.e., suffering), but might not know how nor have the power to do so.
- A god that is very powerful would be able to eliminate suffering, but may not want to, and wouldn’t know how, even if so.
- A god that is wholly knowledgeable would know how to eliminate suffering, but may not want to, and wouldn’t be able to, even if so.
All three parts have to be combined in order to eliminate the evil, suffering, or really bad stuff from happening. Further, although this argument is targeting God, the same argument would apply if we are talking about superheroes, military organizations, social institutions, etc.
For example, a superhero that is all good, all powerful, and all knowing would be expected to eliminate evil just as well as God. And so to the extent that suffering or evil exists, we could also use Hume’s argument to cast doubt on the existence of a superhero with those three traits.
That this argument applies to any type of entity is good news, because I am going to use it — not to cast doubt on the existence of something, but to better understand why suffer.
Suffering Machines and Equations
Let’s explore this idea that evil or really bad stuff (I’ll call this ‘extreme suffering’, or just ‘suffering’ for short) is somehow dependent upon the triad of power, knowledge and malevolence / benevolence and see what relationships we can glean.
If someone is going to be mean, you also want them to be lazy and dumb
For now, let’s imagine that these attributes are dials on a mystical machine, a machine that takes settings on these dials as inputs, and then produces a certain amount of suffering (or protection from suffering) as a result.
It doesn’t really matter what the machine looks like, or how it works; we just know that it has three dials we can fiddle with:
“P” = Power, or ability/resources, which is something you can have more or less of (ranging from impotence to omnipotence).
“K” = Knowledge, or true, justified beliefs (ranging from omniscience, to simple ignorance, all the way to its opposite, negative knowledge (-K, aka being highly misinformed).
“M” = Malevolence, which can range from outright malevolence, to indifference, to its opposite, negative malevolence(-M, aka kindness).
With these three dials understood, now consider the output of this machine; it will either generate severe and unjust suffering (S), be neutral to such suffering (0), or prevent such suffering (-S). Let’s call this output “S” for Suffering.
Before we start fiddling with these dials (assuming we could), it would be good to know exactly how these inputs relate to the output (i.e., suffering). What function or equation describes these relationships?
Don’t worry — I’m not going to make you do any math. I originally had numeric value ranges determined for this equation, but the relationships and relative values matter more than the numbers, so you can put away your calculator. You’ll see why shortly, but I believe that the equation that best represents the relationships between the inputs and outputs of this theoretical machine is simply:
P x K x M = S
Let’s call this the Suffering Equation.
As an easy (and yet frightening) example, let’s consider the case where we have the highest values for all three.
High P x High K x High M = High S
This is really the worst possible scenario because extreme power and knowledge are present without any benevolence — in fact, they are paired with extreme malevolence. This is how society would be under a ruthless dictator, tyrant or monarchy with very harmful intentions for its subjects and unlimited power and knowledge.
But now let’s look at a more positive scenario:
High P x High K x -M = -S
Here we again have maximum values for P and K, but now they are combined with a negative value for M, which means the suffering machine is being extremely kind rather than malevolent. Thus we see a corresponding negative value for S, as the machine would actually protect people from suffering rather than create suffering. In that case, there simply would be no severe, unjust suffering (the type of evil with which David Hume and countless philosophers / theologians have issues). What a happy world!
These examples illustrate why I’ve represented malevolence / benevolence in the suffering equation as a multiplying factor rather than simply being added or subtracted. It is not that an entity’s character or moral quality merely adds to or offsets scores for power and knowledge. Nor does a good or bad moral character just zero out the level of suffering.
Rather, power and knowledge get harnessed by the entity and used to either prevent or inflict suffering beyond what you would see if the entity was simply indifferent. The level of suffering thus actually gets amplified by the character of the being in question, which is what the equation illustrates.
There are also reasons why power and knowledge get multiplied instead of added / subtracted. Warren Buffet² humorously points out a reason in one of his “sermons” to MBA students:
We look for three things when we hire people. We look for intelligence, we look for initiative or energy, and we look for integrity. And if they don’t have the latter, the first two will kill you, because if you’re going to get someone without integrity, you want them lazy and dumb.
In other words, in the case of any malevolent entity, you’d want its power or knowledge (or both) to be as low as possible, or:
Zero P x Zero K x High M = Zero Impact to S
The entity described above is mean, but also powerless or ignorant (in this case both). Again, the multiplicative nature will accordingly yield the correct result in S; i.e., zero or close to zero, meaning that no level of suffering is generated (but neither is any suffering being prevented).
In fact, there may be many of these entities around today; any person who is mean to the core but has no power or knowledge fits this bill — they are mean but effectively harmless. Ill intentions get zeroed out (fortunately!) by a lack of power and knowledge.
However, this cuts both ways.
Consider the case where we dial up kindness by turning M negative, but the machine is again either completely powerless or extremely ignorant (or both). You could imagine if we all as a society mimicked this state. Our fates would too often depend on the whimsy of nature, despite everyone getting along famously and caring as much as possible for others. The first plague, virus, famine, natural disaster or hostile invasion would all-too-efficiently bring immense suffering to an ignorant or powerless — yet otherwise loving — population.
Zero P x Zero K x -M = Zero Impact To S
Again, the suffering equation reveals that you need at least some of both P and K to combine with kindness to get any level of protection from suffering. And if any one of those factors are truly missing, you will not gain much benefit by having a lot of the other two.
Real World Application
Thus far we’ve looked at extreme, theoretical cases for our suffering machine — these cases have illustrated some interesting relationships between the inputs and outputs that seem to make sense. It is time to apply this model to the world we observe around us, and it is here that the suffering equation gets both interesting and useful.
If we forget about mystical machines or spiritual beings that may or may not watch over us for a second, and consider
- Power or ‘P’ as simply the total amount of resources available to a society for deployment
- Knowledge or ‘K’ as that collective level of knowledge available in a society, and
- Malevolence or ‘M’ as a society’s malevolent disposition to apply both P and K in order to produce suffering (a reflection of its moral character)
we can glean some crucial insights about our social institutions as a whole (government, military, health care, education, welfare programs, etc.).
First of all, we can note that there is at least some severe, unjust suffering in our world. And so whatever else might be the case, our actual suffering equation is yielding an output of suffering that is less than ideal. This means, according to our equation, that our own collective social institutions lack the full combination of complete power, knowledge, and kindness (the same charge that Hume brings against God).
Before we dive deeper, I’ll pause to remind readers that scientists, health researchers, policy makers, and members of various global and national organizations work extremely hard to perform copious research, determine physical/social causes of suffering, and make their detailed information publicly available (some of which are cited below). This discussion is not meant to undermine or attempt to replace this valuable research.
Rather, what this model allows us to do is simply gain a fresh and I think a deeper understanding of these facts — if it is successful. This understanding allows us to develop a richer narrative when discussing why we suffer, and it does this by categorizing the many different, already known causal factors into a few metaphorical levers or dials whose interaction with each other are already known (as outlined in the suffering equation above).
So let’s go deeper now by looking at some basic facts, and see how the suffering equation can lend us a fresh perspective.
The World Health Organization (WHO) states that
“Life expectancy varies by 34 years between countries: In low-income countries, the average life expectancy is 62 years, while in high-income countries, it is 81 years. A child born in Sierra Leone can expect to live for 50 years while a child born in Japan can expect to live 84 years.” ³
Life expectancy varies greatly across nations. Further, we see variances in life expectancy even within nations across certain sub-groups. The WHO points out several such examples within countries or even within a particular city:
· In Bolivia, the rate of infant mortality for babies born to women with no education is greater than 100 per 1000 live births, while the rate of infant mortality for babies born to mothers with at least secondary education is less than 40 per 1000.
· Life expectancy at birth among indigenous Australians is substantially lower (59.4 years for males and 64.8 years for females) than it is for non-indigenous Australians (76.6 years and 82.0 years, respectively).
· Life expectancy at birth for men in Glasgow, Scotland varies by neighbourhood. In Calton the average life expectancy is 54 years, which is 28 years less than the life expectancy of men in Lenzie, just a few kilometres away.
· The prevalence of long-term disabilities among European men aged 80+ years is 58.8% among the lower educated versus 40.2% among the higher educated. ⁴
These statistics reveal what are called “health inequities” — differences in health outcomes between and even within nations.
Remember that the suffering equation says that a nation’s level of suffering (S) would vary according to its power (P), knowledge (K) and malevolence (M). When we take GDP as a rough approximation for a nation’s P, and life expectancy as an indicator for S (a crude proxy just for illustration purposes), we can both predict and explain why, as the WHO states, the average life expectancy for low-income nations (at 62) would differ so drastically from the high-income nations (at 81). This is the real-life implication of power differentials between countries, holding the other factors relatively constant.
This relationship between power and the level of suffering (again, taking life expectancy as an indicator) is vividly exemplified by what is known as the Preston curve.
The Preston curves shown above illustrate that the more GDP a nation has, the higher its life expectancy, with diminishing marginal returns after about $3,200 GDP/capita. The increase in GDP is thus the equivalent of turning up the power dial on our machine: as P increases, we see less suffering, with a diminishing marginal return of this investment at a certain point.
The Preston curve gives us a real-time view as to what effectively happens when we turn the power dial up across nations: suffering decreases (up to a certain point). Also, by looking at Preston curves through time, we see that the Preston curve has been shifting up over the years. This, too, can be predicted / explained with our model. Scientific advancements and general increases in medical-related knowledge have been disseminated across the globe as the human race has learned what works and what doesn’t.
This means that for any particular level of power or resources, the suffering drops; our time, money and resources become more effective over time as our knowledge increases. So regardless of how much resources are available, an increase in K (knowledge) makes the outcome higher across the range — effectively shifting the Preston curve up.
So what? The Good and the Possibly Not-Good
It might be hard to gauge levels of suffering in an accurate way. I’ve used life expectancy as a proxy for levels of suffering just to illustrate the potential of the model. And although life expectancy is indeed an important metric used to measure health inequities (at the very least), a different metric could in theory be suggested, one that might reveal different patterns.
But for now, let’s continue to use life expectancy and push this analysis further to see what kinds of things the suffering equation can reveal. The fact that in the Preston curve we consistently see a drop in suffering (S) as GDP (P) increases, as well as a general shifting up across the curves through time (interpreted as an increase in K, or knowledge), has some important implications.
For the people out there who have no hope for humanity, the suffering equation indicates they are wrong
First of all, for the Preston curve to hold in the first place, it must mean that our third factor, malevolence, is both consistently negative (indicating kindness or benevolence) across countries regardless of wealth. If M wasn’t negative, increases in P or power across countries would not result in the observed decrease in S, the very phenomenon that creates the curve itself.
The conclusion drawn from the suffering equation here is that people on average are at least somewhat and consistently benevolent across countries.
I’ll let that sink in for a second.
What’s more, for the Preston curve to be moving up through time, it means that knowledge has generally been both advancing, and is being disseminated across countries regardless of wealth (otherwise we’d see no shift upward in the poorer nation’s life expectancy, for example). This is a good thing — although perhaps not perfect, good practices based on solid scientific knowledge have been able to spread where they are needed. And for countries that lag on this curve, this might be the first place to investigate.
These are promising, and I think profound, results. For the people out there who have no hope for humanity, the suffering equation indicates they are wrong.
But before we get too optimistic, let us not forget that there is more to the story than just global averages or data examined from a bird’s eye view.
We can also see from the statistics above that our societal institutions are not always equally effective for all members of a population. There are great discrepancies within some countries regarding various sub-population health levels and life expectancy. This too can be provided an interpretation using the suffering equation — and this interpretation may not be so flattering.
Since we are taking GDP as a proxy for P where P is simply the power that is available for deployment to affect S, we know that P is (in principle) the same for every member of society. After all, what is available to one person is also available in theory to another member to leverage for the same benefit. The same holds for knowledge — a society’s level of knowledge does not change depending on who is asking.
Making more money will not solve problems associated with immoral or malevolent policies or practices
From this we should, at least as a first line of inquiry, be suspicious and investigate whether the sub-group’s variation in suffering (S) is due to a selective difference in benevolence of the nation’s government and institutions (for if power and knowledge truly are the same in a given society, then any difference in S must be due to differences in malevolence/benevolence.)
This makes sense as well. When a nation does not ensure similar levels of S to all but instead favors people of certain geographical areas, skin colors, levels of education, or levels of income (or all of the above!) — it is at least very possible that a fundamental deficiency or inconsistency in benevolence is at fault.
For example, the nation may simply be unwilling to publicly fund some or many of the programs or institutions that would direct resources where they are needed. And if that is the case, increasing GDP or power further — although it may still continue to lower overall levels of suffering or S— will not alleviate the discrepancy in suffering between various sub-groups; for that, you’d need to ensure your policies and institutions are more consistently benevolent.
This is where the suffering equation pays dividends — it can tell us where the fundamental problem is to be found, and help us shape further actions accordingly. If benevolence or the moral character of the society is the fundamental problem, then further economic growth or research may not help.
Let’s step back for a minute
None of the conclusions I’ve drawn here change the facts, or the inferences about the immediate or proximate causes, or the policies that the WHO and others would like to enact to increase life expectancy or reduce suffering. These remain extremely important and relevant.
The suffering equation merely offers a new perspective or diagnostic ability regarding where the fundamental or ultimate problem resides and where it doesn’t (and hence what we need to do to help). Perhaps some of the conclusions above have been drawn a little hastily and need to be further refined or qualified. Perhaps we would also need a better measure or proxy for levels of suffering. I consider this article to be the start of such a discussion rather than the final word. And this is a discussion worth having, in my opinion, based on the power of the suffering equation illustrated above.
In later posts, I’ll discuss how this model can be applied to selected topics such as the government, economics, religion, animal welfare and the environment. I’ll also highlight other aspects of suffering as part of the human condition. In the meantime, I challenge readers to find other ways to apply the suffering equation when looking at the world around them. I’d love to hear further insights!
¹ Hume, David. (2008). Principal Writings on Religion including Dialogues Concerning Natural Religion and The Natural History of Religion. (J. C. Gaskin, Ed.) New York: Oxford University Press (p.100)
² John. (2017, January 4). Warren Buffett looks for these 3 traits in people when he hires them. Retrieved from Markets Insider: http://markets.businessinsider.com/news/stocks/what-warren-buffett-looks-for-in-candidates-2017-1-1001644066
³ Health Inequities. (n.d.). Retrieved from World Health Organization: http://www.who.int/features/factfiles/health_inequities/en/
⁴ Social Determinants of Health. (2017, 09 29). Retrieved from World Health Organization: http://www.who.int/social_determinants/thecommission/finalreport/key_concepts/en/