Article
Comment
Conspiracy theory
Death & life
4 min read

A Bayesian theory of death

The sinking of the superyacht displays the probability, and banality, of death.

George is a visiting fellow at the London School of Economics and an Anglican priest.

Rescue workers look at the plan of a yacht.
The search for the Bayesian.
Vigili del Fuoco.

On any statistical calculation, the probability of dying by drowning when your luxury yacht suddenly and inexplicably sinks at anchor in the Mediterranean has to be extremely low. 

So it’s the cruellest of ironies that tech tycoon Mike Lynch should so die, along with his daughter and five others, having devoted his commercial life to the application of such statistical probabilities. He had named his yacht Bayesian after the 18th-century theorem that introduced the idea that probability expresses a degree of belief in an event. 

That doesn’t expressly mean religious belief. But, intriguingly, it doesn’t exclude it either. According to Thomas Bayes, who published his theorem in 1763, the calculable degree of belief may be based on prior knowledge about an event, such as the results of previous experiments, or on personal beliefs about it. 

In essence, you don’t believe your yacht will capsize in the night and sink in seconds, because your experience tells you so. That belief can mathematically be included in the probability of it happening. 

We can transfer the method into religious praxis. Christian belief in the event of resurrection, for instance, can be calculated in the probability that the deaths of the Lynches and others aboard the Bayesian are not the end of their existence. 

It’s an intriguing legacy of Lynch’s work for theologians. But it’s the sheer lack of probability of the lethal event occurring at all that lends it its random banality. It’s that death visited those asleep on a yacht in the small hours that lends this news story such tireless legs, not just that these were super-rich masters and mistresses of the universe. 

There have been bitter observations on social media that the Bayesian’s victims have commanded limitlessly greater attention than the many thousands of refugees who die in small-boat crossings of the Mediterranean every year.  

This is a category mistake. And again, Bayesian theory can be deployed. Experience supports our belief that crossing the sea in overcrowded and unseaworthy vessels can all too often lead to tragically terminal events. The probability of death is plain. Again, it’s the sheer randomness of the Bayesian yacht event that sets it apart. 

If death can visit at any time, there can be no difference in the valuation of long or short lives. 

That randomness brings us back to the banality of sudden death among us, almost its ordinariness, something that just happens, often entirely out of the blue. The prayer book has the funeral words “in the midst of life we are in death”, meaning that death is our constant living companion. But that doesn’t quite cut it for me, because it tells us it’s there, but nothing of its true significance. 

The tenets of Christian faith are regularly said to be those of a death cult; that it’s a deep-seated fear of death that leads us to avoid it with assurances of eternal life. But it’s the sheer banality of death, as displayed in the randomness of the Bayesian event, that seems to knock down that idea. In its randomness, death looks ridiculous rather than evil. 

Conspiracy theories around the sinking of the Bayesian are a kind of denial of the reality of death too. We want there to be more to it than the utterly banal.

Author Hannah Arendt coined the phrase “the banality of evil” when covering the trial of Nazi holocaust architect Adolf Eichmann in Jerusalem. I’d want to suggest that it’s that same banality, that basic human ordinariness, that is the real nature of the supposed grim reaper, rather than his evil.   

None of this can comfort the Lynch family, who mourn the loss of a much-loved father and his young daughter, or the families of the others who lost their lives on the Bayesian. But it is meant to go some way towards an explanation of what we mean in Christian theology when we bandy about phrases such as “the defeat of death”. Because it’s not a wicked serpent that’s been defeated, more of a pointless clown. 

There is something especially painful about the death of the young, such as that of 18-year-old Hannah Lynch on the Bayesian that night, a young woman on the threshold of life. And – God knows – the even younger lives we’ve read about being taken lately. 

But the concept of banality may lead us to another tenet of faith: The completeness of every life. If death can visit at any time, there can be no difference in the valuation of long or short lives.  

A poem, often ascribed to a former dean of St Paul’s cathedral, begins with the line: “Death is nothing at all.” That’s wrong, as an idea. Death is as significant an event as birth. But its defeat is in keeping it in its place. 

The dignity in simplicity with which football manager Sven-Göran Eriksson greeted his final illness is a masterclass in this tactic for life. Death isn’t to be negotiated, it’s just there. 

In the end, death isn’t a Bayesian probability, it’s a certainty, for all of us. The difference, in Bayesian theory, must be the belief we bring to our personal calculations of the probability of the event.   

Explainer
Comment
Economics
Leading
Politics
Wisdom
5 min read

When someone makes a claim, ask yourself these questions

How stories, statistics, and studies exploit our biases.

Alex is a professor of finance, and an expert in the use and misuse of data and evidence.

A member of an audience makes a point while gesturing.
On the other hand...
Antenna on Unsplash.

“Check the facts.”  

“Examine the evidence.”  

“Correlation is not causation.”  

We’ve heard these phrases enough times that they should be in our DNA. If true, misinformation would never get out of the starting block. But countless examples abound of misinformation spreading like wildfire. 

This is because our internal, often subconscious, biases cause us to accept incorrect statements at face value. Nobel Laureate Daniel Kahneman refers to our rational, slow thought process – which has mastered the above three phrases – as System 2, and our impulsive, fast thought process – distorted by our biases – as System 1. In the cold light of day, we know that we shouldn’t take claims at face value, but when our System 1 is in overdrive, the red mist of anger clouds our vision. 

Confirmation bias 

One culprit is confirmation bias – the temptation to accept evidence uncritically if it confirms what we’d like to be true, and to reject a claim out of hand if it clashes with our worldview. Importantly, these biases can be subtle; they’re not limited to topics such as immigration or gun control where emotions run high. It’s widely claimed that breastfeeding increases child IQ, even though correlation is not causation because parental factors drive both. But, because many of us would trust natural breastmilk over the artificial formula of a giant corporation, we lap this claim up. 

Confirmation bias is hard to shake. In a study, three neuroscientists took students with liberal political views and hooked them up to a functional magnetic resonance imaging scanner. The researchers read out statements the participants previously said they agreed with, then gave contradictory evidence and measured the students’ brain activity. There was no effect when non-political claims were challenged, but countering political positions triggered their amygdala. That’s the same part of the brain that’s activated when a tiger attacks you, inducing a ‘fight-or-flight’ response. The amygdala drives our System 1, and drowns out the prefrontal cortex which operates our System 2. 

Confirmation bias looms large for issues where we have a pre-existing opinion. But for many topics, we have no prior view. If there’s nothing to confirm, there’s no confirmation bias, so we’d hope we can approach these issues with a clear head. 

Black-and-white thinking 

Unfortunately, another bias can kick in: black-and-white thinking. This bias means that we view the world in binary terms. Something is either always good or always bad, with no shades of grey. 

To pen a bestseller, Atkins didn’t need to be right. He just needed to be extreme. 

The bestselling weight-loss book in history, Dr Atkins’ New Diet Revolution, benefited from this bias. Before Atkins, people may not have had strong views on whether carbs were good or bad. But as long as they think it has to be one or the other, with no middle ground, they’ll latch onto a one-way recommendation. That’s what the Atkins diet did. It had one rule: Avoid all carbs. Not just refined sugar, not just simple carbs, but all carbs. You can decide whether to eat something by looking at the “Carbohydrate” line on the nutrition label, without worrying whether the carbs are complex or simple, natural or processed. This simple rule played into black-and-white thinking and made it easy to follow. 

To pen a bestseller, Atkins didn’t need to be right. He just needed to be extreme. 

Overcoming Our biases 

So, what do we do about it? The first step is to recognize our own biases. If a statement sparks our emotions and we’re raring to share or trash it, or if it’s extreme and gives a one-size-fit-all prescription, we need to proceed with caution. 

The second step is to ask questions, particularly if it’s a claim we’re eager to accept. One is to “consider the opposite”. If a study had reached the opposite conclusion, what holes would you poke in it? Then, ask yourself whether these concerns still apply even though it gives you the results you want. 

Take the plethora of studies claiming that sustainability improves company performance. What if a paper had found that sustainability worsens performance? Sustainability supporters would throw up a host of objections. First, how did the researchers actually measure sustainability? Was it a company’s sustainability claims rather than its actual delivery? Second, how large a sample did they analyse? If it was a handful of firms over just one year, the underperformance could be due to randomness; there’s not enough data to draw strong conclusions. Third, is it causation or just correlation? Perhaps high sustainability doesn’t cause low performance, but something else, such as heavy regulation, drives both. Now that you’ve opened your eyes to potential problems, ask yourselves if they plague the study you’re eager to trumpet. 

A second question is to “consider the authors”. Think about who wrote the study and what their incentives are to make the claim that they did. Many reports are produced by organizations whose goal is advocacy rather than scientific inquiry. Ask “would the authors have published the paper if it had found the opposite result?” — if not, they may have cherry-picked their data or methodology. 

In addition to bias, another key attribute is the authors’ expertise in conducting scientific research. Leading CEOs and investors have substantial experience, and there’s nobody more qualified to write an account of the companies they’ve run or the investments they’ve made. However, some move beyond telling war stories to proclaiming a universal set of rules for success – but without scientific research we don’t know whether these principles work in general. A simple question is “If the same study was written by the same authors, with the same credentials, but found the opposite results, would you still believe it?” 

Today, anyone can make a claim, start a conspiracy theory or post a statistic. If people want it to be true it will go viral. But we have the tools to combat it. We know how to show discernment, ask questions and conduct due diligence if we don’t like a finding. The trick is to tame our biases and exercise the same scrutiny when we see something we’re raring to accept. 

 

This article is adapted from May Contain Lies: How Stories, Statistics, and Studies Exploit Our Biases – and What We Can Do About It
(Penguin Random House, 2024)
Reproduced by kind permission of the author.

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