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Why predicting suicide is a difficult and complex challenge
In the wake of suicides by high-profile and much-beloved celebrities Kate Spade and Anthony Bourdain, psychologists and psychiatrists say that suicide is too complex and indeterminate for humans to predict.
Who is going to die by suicide? This terrible mystery of human behavior takes on particular poignance in the wake of suicides by high-profile and much-beloved celebrities Kate Spade and Anthony Bourdain. It is only natural that people want to know why such tragedies occur. Those closest to those who take their lives are often tormented, wondering if there is something they could have – or should have – known to prevent their loved one’s suicide.
As a scientist who has focused on this question for the past decade, I should have a pretty good idea of who is and isn’t going to die by suicide. But the sad truth is, I don’t. The sadder truth is, neither do any other suicide experts, psychiatrists or physicians. The sum of the research on suicide shows that it does not matter how long we’ve known someone or how much we know about them. In my research, my colleagues and I have shown that we can only predict who is going to die by suicide slightly more accurately than random guessing.
The need for answers
The fact that suicide is so hard to predict unfortunately took about 50 years for most scientists to appreciate. About the same time that this recognition became widespread a few years ago, a new hope emerged: a form of artificial intelligence called machine learning. As several research groups have demonstrated in recent years, machine learning may be able to predict who is going to attempt or die by suicide with up to 90 percent accuracy.
To understand why this is, and why we humans won’t ever be able to accurately predict suicide on our own, one needs to take a step back and understand a little more about the nature of human cognition, suicide and machine learning.
As humans, we love explanations that have two qualities. First, explanations should be simple, meaning that they involve one or a small number of things. For example, depression is a simple explanation for suicide.
Second, explanations should be determinate, meaning that there is one set explanation that accounts for all or most of something. For example, the idea that depression causes most suicides is a determinate explanation. This simple and determinate explanatory style is highly intuitive and very efficient. It’s great for helping us to survive, procreate, and get through our days.
But this style of thinking is terrible for helping us understand nature. This is because nature is not simple and determinate. In recent decades, scientists have come to recognize that nearly everything – from physics to biology to human behavior – is complex and indeterminate. In other words, a very large number of things combined in a complex way are needed to explain most things, and there’s no set recipe for most physical, biological or behavioral phenomena.
I know that this latter idea of indeterminacy is especially counterintuitive, so let me provide a straightforward example of it. The math equation X plus Y equals 1 is indeterminate. As humans, we instinctively try to find one solution to this equation (e.g., X equals 1, Y equals 0). But there is no set recipe for solving this equation; there are nearly infinite solutions to this equation. Importantly, however, this does not mean that “anything goes.” There are also near infinite values for X and Y that do not solve this equation. This indeterminate middle ground between “one solution” and “anything goes” is difficult for most humans to grasp, but it’s how much of nature works.
The sum of our scientific evidence indicates that, just like most other things in nature, the causes and predictors of suicide are complex and indeterminate. Hundreds, and maybe thousands, of things are relevant to suicide, but nothing predicts suicide much more accurately than random guessing. For example, depression is often considered to be an extremely important predictor of suicide. But about 2 percent of severely depressed people eventually die by suicide, which is only slightly higher than the 1.6 percent of people from the general United States population who eventually die by suicide. Such a pattern is consistent with complexity because it suggests that we must put a lot of factors together to account for suicide.
Empathy will always matter
So how should we put all of these factors together? One intuitive solution is to add many of these factors together. But even when summing hundreds of factors, this doesn’t work – prediction is still only slightly more accurate than random guessing.
A much better solution would be to somehow find an optimized combination of tens or even hundreds of factors. How can we do this? One promising answer is machine learning. In short, machine learning programs can process a large amount of data and learn an optimal combination of factors for a given task. For example, most existing machine learning studies have used data from electronic health records, spanning hundreds of factors related to mental health diagnoses, physical health problems, medications, demographics and hospital visit patterns. Results from several groups in recent years have shown that this approach can consistently predict future suicide attempts and death with 80-90 percent accuracy. Multiple groups are currently working on applying these algorithms to actual clinical practice.
One important thing to keep in mind is that there isn’t, and never will be, a single algorithm or recipe for suicide prediction. This is because suicide is indeterminate, much like the X plus Y equals 1 equation. There are likely near-infinite algorithms that could predict suicide with 80-90 percent accuracy, as a number of studies have shown. Research has already demonstrated that no particular factors are necessary for a good algorithm, and many different types of algorithms can produce accurate prediction. But again, this indeterminacy also means that there are near-infinite bad algorithms, too.
All of this research shows that suicide is unfortunately too complex and indeterminate for humans to predict. Neither I nor anyone else can accurately predict who is going to die by suicide or truly explain why a particular person died by suicide (this includes the suicide decedents themselves). Machine learning can do a much better job of approximating the complexity of suicide, but even it falls far short. Although it can accurately predict who will eventually die by suicide, it cannot yet tell us when someone will die by suicide. This “when” dimension of prediction is critical, and we are likely still many years away from accounting for it.
In the meantime, what can we humans do? While we don’t have the ability to know whether someone is going to die by suicide or not, we do have the ability to be supportive and caring. If you believe that someone may be struggling, talk with them and let them know about resources such as the US National Suicide Prevention Lifeline (1-800-273-8255).
If this article has raised issues for you or if you’re concerned about someone you know, call Lifeline on 13 11 14.
Why mega-eruptions like the ones that covered North America in ash are the least of your worries.
- The supervolcano under Yellowstone produced three massive eruptions over the past few million years.
- Each eruption covered much of what is now the western United States in an ash layer several feet deep.
- The last eruption was 640,000 years ago, but that doesn't mean the next eruption is overdue.
The end of the world as we know it
Panoramic view of Yellowstone National Park
Image: Heinrich Berann for the National Park Service – public domain
Of the many freak ways to shuffle off this mortal coil – lightning strikes, shark bites, falling pianos – here's one you can safely scratch off your worry list: an outbreak of the Yellowstone supervolcano.
As the map below shows, previous eruptions at Yellowstone were so massive that the ash fall covered most of what is now the western United States. A similar event today would not only claim countless lives directly, but also create enough subsidiary disruption to kill off global civilisation as we know it. A relatively recent eruption of the Toba supervolcano in Indonesia may have come close to killing off the human species (see further below).
However, just because a scenario is grim does not mean that it is likely (insert topical political joke here). In this case, the doom mongers claiming an eruption is 'overdue' are wrong. Yellowstone is not a library book or an oil change. Just because the previous mega-eruption happened long ago doesn't mean the next one is imminent.
Ash beds of North America
Ash beds deposited by major volcanic eruptions in North America.
Image: USGS – public domain
This map shows the location of the Yellowstone plateau and the ash beds deposited by its three most recent major outbreaks, plus two other eruptions – one similarly massive, the other the most recent one in North America.
The Huckleberry Ridge eruption occurred 2.1 million years ago. It ejected 2,450 km3 (588 cubic miles) of material, making it the largest known eruption in Yellowstone's history and in fact the largest eruption in North America in the past few million years.
This is the oldest of the three most recent caldera-forming eruptions of the Yellowstone hotspot. It created the Island Park Caldera, which lies partially in Yellowstone National Park, Wyoming and westward into Idaho. Ash from this eruption covered an area from southern California to North Dakota, and southern Idaho to northern Texas.
About 1.3 million years ago, the Mesa Falls eruption ejected 280 km3 (67 cubic miles) of material and created the Henry's Fork Caldera, located in Idaho, west of Yellowstone.
It was the smallest of the three major Yellowstone eruptions, both in terms of material ejected and area covered: 'only' most of present-day Wyoming, Colorado, Kansas and Nebraska, and about half of South Dakota.
The Lava Creek eruption was the most recent major eruption of Yellowstone: about 640,000 years ago. It was the second-largest eruption in North America in the past few million years, creating the Yellowstone Caldera.
It ejected only about 1,000 km3 (240 cubic miles) of material, i.e. less than half of the Huckleberry Ridge eruption. However, its debris is spread out over a significantly wider area: basically, Huckleberry Ridge plus larger slices of both Canada and Mexico, plus most of Texas, Louisiana, Arkansas, and Missouri.
This eruption occurred about 760,000 years ago. It was centered on southern California, where it created the Long Valley Caldera, and spewed out 580 km3 (139 cubic miles) of material. This makes it North America's third-largest eruption of the past few million years.
The material ejected by this eruption is known as the Bishop ash bed, and covers the central and western parts of the Lava Creek ash bed.
Mount St Helens
The eruption of Mount St Helens in 1980 was the deadliest and most destructive volcanic event in U.S. history: it created a mile-wide crater, killed 57 people and created economic damage in the neighborhood of $1 billion.
Yet by Yellowstone standards, it was tiny: Mount St Helens only ejected 0.25 km3 (0.06 cubic miles) of material, most of the ash settling in a relatively narrow band across Washington State and Idaho. By comparison, the Lava Creek eruption left a large swathe of North America in up to two metres of debris.
The difference between quakes and faults
The volume of dense rock equivalent (DRE) ejected by the Huckleberry Ridge event dwarfs all other North American eruptions. It is itself overshadowed by the DRE ejected at the most recent eruption at Toba (present-day Indonesia). This was one of the largest known eruptions ever and a relatively recent one: only 75,000 years ago. It is thought to have caused a global volcanic winter which lasted up to a decade and may be responsible for the bottleneck in human evolution: around that time, the total human population suddenly and drastically plummeted to between 1,000 and 10,000 breeding pairs.
Image: USGS – public domain
So, what are the chances of something that massive happening anytime soon? The aforementioned mongers of doom often claim that major eruptions occur at intervals of 600,000 years and point out that the last one was 640,000 years ago. Except that (a) the first interval was about 200,000 years longer, (b) two intervals is not a lot to base a prediction on, and (c) those intervals don't really mean anything anyway. Not in the case of volcanic eruptions, at least.
Earthquakes can be 'overdue' because the stress on fault lines is built up consistently over long periods, which means quakes can be predicted with a relative degree of accuracy. But this is not how volcanoes behave. They do not accumulate magma at constant rates. And the subterranean pressure that causes the magma to erupt does not follow a schedule.
What's more, previous super-eruptions do not necessarily imply future ones. Scientists are not convinced that there ever will be another big eruption at Yellowstone. Smaller eruptions, however, are much likelier. Since the Lava Creek eruption, there have been about 30 smaller outbreaks at Yellowstone, the last lava flow being about 70,000 years ago.
As for the immediate future (give or take a century): the magma chamber beneath Yellowstone is only 5 percent to 15 percent molten. Most scientists agree that is as un-alarming as it sounds. And that its statistically more relevant to worry about death by lightning, shark, or piano.
Strange Maps #1041
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Measuring a person's movements and poses, smart clothes could be used for athletic training, rehabilitation, or health-monitoring.
In recent years there have been exciting breakthroughs in wearable technologies, like smartwatches that can monitor your breathing and blood oxygen levels.
But what about a wearable that can detect how you move as you do a physical activity or play a sport, and could potentially even offer feedback on how to improve your technique?
And, as a major bonus, what if the wearable were something you'd actually already be wearing, like a shirt of a pair of socks?
That's the idea behind a new set of MIT-designed clothing that use special fibers to sense a person's movement via touch. Among other things, the researchers showed that their clothes can actually determine things like if someone is sitting, walking, or doing particular poses.
The group from MIT's Computer Science and Artificial Intelligence Lab (CSAIL) says that their clothes could be used for athletic training and rehabilitation. With patients' permission, they could even help passively monitor the health of residents in assisted-care facilities and determine if, for example, someone has fallen or is unconscious.
The researchers have developed a range of prototypes, from socks and gloves to a full vest. The team's "tactile electronics" use a mix of more typical textile fibers alongside a small amount of custom-made functional fibers that sense pressure from the person wearing the garment.
According to CSAIL graduate student Yiyue Luo, a key advantage of the team's design is that, unlike many existing wearable electronics, theirs can be incorporated into traditional large-scale clothing production. The machine-knitted tactile textiles are soft, stretchable, breathable, and can take a wide range of forms.
"Traditionally it's been hard to develop a mass-production wearable that provides high-accuracy data across a large number of sensors," says Luo, lead author on a new paper about the project that is appearing in this month's edition of Nature Electronics. "When you manufacture lots of sensor arrays, some of them will not work and some of them will work worse than others, so we developed a self-correcting mechanism that uses a self-supervised machine learning algorithm to recognize and adjust when certain sensors in the design are off-base."
The team's clothes have a range of capabilities. Their socks predict motion by looking at how different sequences of tactile footprints correlate to different poses as the user transitions from one pose to another. The full-sized vest can also detect the wearers' pose, activity, and the texture of the contacted surfaces.
The authors imagine a coach using the sensor to analyze people's postures and give suggestions on improvement. It could also be used by an experienced athlete to record their posture so that beginners can learn from them. In the long term, they even imagine that robots could be trained to learn how to do different activities using data from the wearables.
"Imagine robots that are no longer tactilely blind, and that have 'skins' that can provide tactile sensing just like we have as humans," says corresponding author Wan Shou, a postdoc at CSAIL. "Clothing with high-resolution tactile sensing opens up a lot of exciting new application areas for researchers to explore in the years to come."
The paper was co-written by MIT professors Antonio Torralba, Wojciech Matusik, and Tomás Palacios, alongside PhD students Yunzhu Li, Pratyusha Sharma, and Beichen Li; postdoc Kui Wu; and research engineer Michael Foshey.
The work was partially funded by Toyota Research Institute.
How imagining the worst case scenario can help calm anxiety.
- Stoicism is the philosophy that nothing about the world is good or bad in itself, and that we have control over both our judgments and our reactions to things.
- It is hardest to control our reactions to the things that come unexpectedly.
- By meditating every day on the "worst case scenario," we can take the sting out of the worst that life can throw our way.
Are you a worrier? Do you imagine nightmare scenarios and then get worked up and anxious about them? Does your mind get caught in a horrible spiral of catastrophizing over even the smallest of things? Worrying, particularly imagining the worst case scenario, seems to be a natural part of being human and comes easily to a lot of us. It's awful, perhaps even dangerous, when we do it.
But, there might just be an ancient wisdom that can help. It involves reframing this attitude for the better, and it comes from Stoicism. It's called "premeditation," and it could be the most useful trick we can learn.
Broadly speaking, Stoicism is the philosophy of choosing your judgments. Stoics believe that there is nothing about the universe that can be called good or bad, valuable or valueless, in itself. It's we who add these values to things. As Shakespeare's Hamlet says, "There is nothing either good or bad, but thinking makes it so." Our minds color the things we encounter as being "good" or "bad," and given that we control our minds, we therefore have control over all of our negative feelings.
Put another way, Stoicism maintains that there's a gap between our experience of an event and our judgment of it. For instance, if someone calls you a smelly goat, you have an opportunity, however small and hard it might be, to pause and ask yourself, "How will I judge this?" What's more, you can even ask, "How will I respond?" We have power over which thoughts we entertain and the final say on our actions. Today, Stoicism has influenced and finds modern expression in the hugely effective "cognitive behavioral therapy."
Helping you practice StoicismCredit: Robyn Beck via Getty Images
One of the principal fathers of ancient Stoicism was the Roman statesmen, Seneca, who argued that the unexpected and unforeseen blows of life are the hardest to take control over. The shock of a misfortune can strip away the power we have to choose our reaction. For instance, being burglarized feels so horrible because we had felt so safe at home. A stomach ache, out of the blue, is harder than a stitch thirty minutes into a run. A sudden bang makes us jump, but a firework makes us smile. Fell swoops hurt more than known hardships.
What could possibly go wrong?
So, how can we resolve this? Seneca suggests a Stoic technique called "premeditatio malorum" or "premeditation." At the start of every day, we ought to take time to indulge our anxious and catastrophizing mind. We should "rehearse in the mind: exile, torture, war, shipwreck." We should meditate on the worst things that could happen: your partner will leave you, your boss will fire you, your house will burn down. Maybe, even, you'll die.
This might sound depressing, but the important thing is that we do not stop there.
Stoicism has influenced and finds modern expression in the hugely effective "cognitive behavioral therapy."
The Stoic also rehearses how they will react to these things as they come up. For instance, another Stoic (and Roman Emperor) Marcus Aurelius asks us to imagine all the mean, rude, selfish, and boorish people we'll come across today. Then, in our heads, we script how we'll respond when we meet them. We can shrug off their meanness, smile at their rudeness, and refuse to be "implicated in what is degrading." Thus prepared, we take control again of our reactions and behavior.
The Stoics cast themselves into the darkest and most desperate of conditions but then realize that they can and will endure. With premeditation, the Stoic is prepared and has the mental vigor necessary to take the blow on the chin and say, "Yep, l can deal with this."
Catastrophizing as a method of mental inoculation
Seneca wrote: "In times of peace, the soldier carries out maneuvers." This is also true of premeditation, which acts as the war room or training ground. The agonizing cut of the unexpected is blunted by preparedness. We can prepare the mind for whatever trials may come, in just the same way we can prepare the body for some endurance activity. The world can throw nothing as bad as that which our minds have already imagined.
Stoicism teaches us to embrace our worrying mind but to embrace it as a kind of inoculation. With a frown over breakfast, try to spend five minutes of your day deliberately catastrophizing. Get your anti-anxiety battle plan ready and then face the world.
A study on charity finds that reminding people how nice it feels to give yields better results than appealing to altruism.