Tim Harford's Blog

Chris McKinlay is a good looking, smart student at UCLA, but he can’t seem to get a girlfriend. He’s a computing expert, so why not use his technology prowess to supercharge his search for a soulmate? He starts building an army of bots and unleashes them into the world of online dating. Chris’ search for love leads him to some unexpected places, and it might be teaching us all the wrong lessons about love. 

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Further reading

On Chris McKinlay, the two essential sources are Kevin Poulsen’s feature article for Wired Magazine March 2014 “Hack OkCupid, acquire love. This guy did” and Hannah Fry’s BBC podcast Uncharted: Love Bytes supplemented by How To Get a Date with Data This Valentine’s Day by Zoe Kleinman

The history of Compatibility Research Inc is told in Love In The Time of Algorithms by Dan Slater (also published under the title A Million First Dates) and a FiveThirtyEight short documentary.

The description of scientific matchmaking in the 1920s is from Matt Novak in Smithsonian Magazine.

Dan Gilbert’s research with Jane Ebert is “Decisions and revisions: The affective forecasting of changeable outcomes” Journal of Personality and Social Psychology 2002; he also describes the work in his TED talk.

OK Cupid’s research on placebo matching is Christian Rudder “We Experiment on Human Beings!” archived here.

Daniel Kahneman’s book is Thinking, Fast and Slow.

Research on the “sex recession” includes:

Finer LB, Philbin JM. Trends in ages at key reproductive transitions in the United States, 1951-2010. Womens Health Issues. 2014 May-Jun;24(3):e271-9. doi: 10.1016/j.whi.2014.02.002.

Twenge JM, Sherman RA, Wells BE. Sexual Inactivity During Young Adulthood Is More Common Among U.S. Millennials and iGen: Age, Period, and Cohort Effects on Having No Sexual Partners After Age 18. Arch Sex Behav. 2017 Feb;46(2):433-440. doi: 10.1007/s10508-016-0798-z.

Stephanie Stacey How We Fell Out of Love with Dating Apps Financial Times Dec 24 2024

The marathon, the algorithm and me

Twenty-five centuries ago, after the Greeks shattered the Persian army at Marathon, brave Pheidippides ran 26 miles to Athens with the news. Robert Browning’s poem tells the tale:

“Rejoice, we conquer!” Like wine thro’ clay

Joy in his blood bursting his heart, he died — the bliss!

With the death of Pheidippides began the legend of the marathon, a feat of running so arduous that the very attempt could kill you. I plan to run my first marathon in April in London, hoping to avoid his blissful fate. After all, I have an ally that he did not. Pheidippides, for all his valour, lacked a sports watch.

I was never a runner; my knees weren’t up to it, I’d tell myself. But one thing led to another and, after a couple of years at my local Parkrun, I bought an entry-level running watch, with no aim beyond pacing myself evenly. I didn’t realise that I was plugging my body into the exercise yard of the digital panopticon, with the watch’s app estimating everything from my heart-rate to my step count, and hazarding a guess at my body’s capacity to use oxygen, not to mention my “fitness age”. I had never dreamt such a small box of tricks could provide so many numbers, all claiming in some way to — and here I quote the watch manufacturer, Garmin — “support your efforts to improve and maintain your health”.

There is no denying the technological cleverness here. My watch uses a network of 24 satellites, time signals to within three billionths of a second and calculations adjusting for the irregular shape of the planet in order to pinpoint my location to within 5m. It adds an accelerometer, a device that detects changes in speed or direction using interleaved combs of conductive material etched on silicon that flex and touch as my wrist moves. A strip of flashing green lights on the underbelly of my watch monitors my heart rate by detecting how much light bounces off my wrist rather than being absorbed by the red blood swelling and shrinking my tiny capillaries.

It is all something of a miracle, but more interesting still is the panoply of behavioural nudges, everything from inviting me to share my runs on social networks to tracking my “streaks” of exercise. Last year, I began training for a 10k race, then a half-marathon (more than 21km), using the free coaching software bundled with my watch.

Over 12 weeks of training, my virtual coach would send me off on several runs a week, gradually sharpening the pace and increasing the distance, mixing things up with easier runs or fierce sprint intervals. From time to time, I’d get a short article or a canned video message and, after every run, an upbeat verdict: “Great job!” or “Room to grow.” A coloured dial, purportedly indicating my coach’s confidence, but actually the output of some unknown algorithm, told me how likely I was to achieve my goal on race day.

Without a doubt this coaching programme worked; it prompted me to exercise regularly, and I became faster and fitter. But the longer I used it, the more questions arose in my mind.

There is something about the fitness watch that feels unnervingly familiar after two decades of smartphones and social media. An amazing technology flipping from unimaginable to indispensable almost overnight; the endless tracking, nudging, sharing; the datafication of something that previously had eluded measurement; and a sense of mystery about where all this data is going and how it is being used. On top of all that is something new and visceral: a device worn on my skin, measuring blood, breath, speed and sleep.

Is the fitness watch really to be trusted with my fitness? And can it teach me a lesson about the way so many parts of my life have been transformed into numbers, rewards and targets?

*

Automated fitness tracking began before I was born. In the 1960s, worried that their Japanese compatriots were becoming sedentary, a doctor named Iwao Ohya and an engineer named Jiro Kato developed a simple step-counter. They called it the manpokei or “10,000 stepmeter”. There are various origin stories for the figure of 10,000 and all of them acknowledge that there is no scientific logic for the threshold. That didn’t stop the idea catching on in a big way in the 21st century, when smartphones and fitness trackers began to number our steps and tut disappointedly whenever we missed their arbitrary target.

These tuts make a difference: Katy Milkman, a professor at the Wharton School and author of How To Change, showed me step-tracking data from an unpublished study. Her study subjects walked a variety of distances, but the data displayed a huge spike just beyond 10,000 daily steps, evidence of the powerful urge to satisfy the fitness tracker’s meaningless target.

Still, motivation is motivation. “There is a widespread perception that fitness trackers don’t work, which is incorrect,” says Carol Maher, a professor of population and digital health at the University of South Australia, who has conducted many studies into the effects of fitness tracking. “When you put all the evidence together, it’s very clear that they do help people walk more and take more steps. It’s a modest change but even modest changes are very beneficial.”

Maher and a team of researchers conducted a wide-ranging review of different studies of fitness trackers, covering 164,000 participants. They found all the effects that one might hope for: people tend to be more active, walk more, lose fat, lose weight and gain fitness.

This should not be a surprise. Fitness trackers set us simple goals, record our progress and share our achievements with our friends. All of these behaviour nudges are calculated to prod us into action.

Milkman sent me a short reading list of relevant studies, along with a rapid-fire summary. “Reminders change behaviour,” she told me. “Bite-sized, short-term goals change behaviour and round-number goals are particularly helpful. Self-monitoring changes behaviour. Symbolic rewards like badges change behaviour. Social accountability, such as sharing your exercise, changes behaviour.”

Both Milkman and Maher are convinced that fitness trackers help, and so am I. But help who? And to do what? It’s one thing to coax a couch potato to get up and go for a walk; it’s another to guide an ageing writer to his first marathon. Yet I had put my watch in charge of reaching this all encompassing goal.

*

At the heart of the matter is a piece of human behaviour identified by Milkman in a study conducted with behavioural scientists Linda Chang, Erika Kirgios and Sendhil Mullainathan. The researchers asked a simple question: “Do we decide differently when some dimensions of a choice are quantified and others are not?”

The answer emerged loud and clear from a series of experiments: yes, we do. Whenever experimental subjects were offered a choice between two options, they would tend to favour whichever option looked better on numerical measures and overlook qualities that were expressed as graphical elements, letter grades, star symbols or in words (“moderate”, “excellent”, “highly likely”). This was true whether the choice was between hotels, job applicants, conference locations, public works projects, restaurants or charitable causes. Numbers loomed large. What was quantified, got attention.

This matters because fitness trackers purport to excel at quantifying some things and do not pretend even to quantify others. If quantification fixation applies, we would expect to see such trackers systematically pushing people towards the quantified behaviour at the expense of other things.

An early hint of this came in 2016, when the results of a study of weight loss in 470 people were published. All these people were trying to lose weight, all of them were prescribed a low-calorie diet and all of them were encouraged to exercise. Only half of them, however, were given fitness trackers. To the barely concealed glee of journalists, who love a counter-intuitive finding, the results of the study showed that, after two years, the people who had lost more weight were the ones without the fitness trackers.

Subsequent, larger studies strongly suggest that fitness trackers do not usually hinder weight loss, but the surprising and disheartening finding is an example in miniature of the quantification-fixation problem.

In this case, both groups were equally active, but those using a fitness tracker were getting automatic, effortless validation of their effort, which they could then use to justify more indulgent eating. The lead researcher, John Jakicic, speculated at the time: “People would say, ‘Oh, I exercised a lot today, now I can eat more.’ And they might eat more than they otherwise would have.” Calorie counting is joyless, easily fudged — and not automated by the watch.

We’re all familiar with the tendency to be virtuous in one aspect of our behaviour, then let ourselves off the hook somewhere else — choosing a healthy salad, then using it as permission to order dessert. Psychologists call this behaviour “self licensing” and fitness trackers encourage it by supplying us with asymmetric data. We are told how much we moved, but not what we ate. We get stark feedback on heart rate and step count, but the tracker looks the other way if we order french fries and a glass of beer.

Here’s another instructive example of the way quantification can lead us astray. In a small experiment conducted by Rob Copeland of Sheffield Hallam University, some volunteers were asked to hit the timeworn target of 10,000 steps a day, while others were told instead to take three brisk walks a day, each of about 10 minutes. One of these exercise regimes requires a wearable computer; the other, nothing more than a pair of shoes. Three brisk walks aren’t close to 10,000 steps; in total they are more like 3,000 — not that anyone is counting.

When Copeland studied fitness-tracking data from all the volunteers, he found that those who had done the human-centred exercise of a few short walks had actually done almost a third more “moderate to vigorous” physical activity than the ones grinding out a step count for the algorithm, and found the task less of a chore.

Even on the narrow grounds of cardiovascular activity, the unquantified walk beats the quantified one — and that is before we take into account the benefits of a chat with a friend or the feeling of the wind in your hair. The fitness tracker will handle quantity all day long. But the quality of a walk? That’s up to us to defend.

Our digital devices are quantification machines. Try to count 10,000 steps as you go about your day and you’ll drive yourself mad, but your watch will do it for you without you even noticing. But what gets counted isn’t always what counts.

A brutal callisthenics session in the gym may leave me feeling that I’ve given everything, but the watch sees only my heart rate and is unimpressed. My Taiji practice is a form of gentle exercise that I greatly value, but as far as my watch is concerned I’m not really exercising at all. None of this would matter much if quantification fixation didn’t exist, but it does. It is human nature to take the watch and the activities it quantifies more seriously than they deserve.

*

Over the past 18 months, my virtual coach has paced me to my longest runs and my fastest runs, prodded me to pull on my running shoes and head out the door even when I didn’t feel like it, and broadened the (admittedly narrow) horizons of my training routines. But it has also nudged me into some decisions I regret.

Last winter, I went out running when some of the roads were covered in sheet ice. I avoided mishaps by gingerly picking my way over the obstacles, only to find the algorithm grumbling that I had not run fast enough.

A month ahead of my first flat 10k race, I picked up a minor injury. A coach would have told me to rest and heal, but I worried that the algorithm’s “adaptive” training plan would be derailed if I didn’t keep going. (Many of these training plans call themselves “adaptive” but I have yet to find one that explains how this adaptation works.)

In the end I resolved the tension between my need for recuperation and my desire for a personal best by going to my local park three weeks before race day, gritting my teeth and running the PB I’d been aiming for. Then I switched off the training plan so that I could heal. I doubt I would have achieved the PB without the watch — but I also would never have behaved so oddly.

There’s a word for losing sleep because you’re worried about being judged by your sleep tracker: “orthosomnia”. I’m lucky enough not to worry much about my sleep, but I do worry about my running. It’s easy to see how the powerful lure of a training plan that understands neither ice nor injury could prompt me and others like me into counter-productive overtraining — even permanent damage.

Some of these risks come from poor product design. Garmin’s Connect app, for example, prominently celebrates “streaks” of exercise, meaning the number of consecutive days in which I’ve recorded some kind of activity. Yet any coach will tell you that rest days are vital, so it is strange that my main fitness app applauds me for the number of consecutive days in which I have failed to take a rest.

Other risks are more subtle. When I signed up for the Runna app, for example, it suggested what seemed an absurdly aggressive target for my first marathon time — almost an hour faster than Garmin’s race prediction. The first training run the app proposed was at a blistering pace.

I spoke to Walter Holohan, the chief technology officer of Runna, who was keen to emphasise that the Runna training plan was personalised and it would use a proprietary algorithm to adapt the training schedule to my performance. Could he share any details?

“Obviously, we wouldn’t want to share our proprietary algorithms,” he explained. Obviously. I’ve not yet found a company that will. But that leaves users taking things on trust.

“It’s understandable, of course, because they’ve got competition between one another,” says Joe Warne of the Sports Science Replication Centre at Technological University Dublin. “They don’t want to share their secrets of how they’ve arrived at these values. But the more that we continue to do that, the less that we’re going to have any real insight.”

Given that the history of fitness trackers begins with someone picking 10,000 steps because it’s a nice-sounding round number, the lack of transparency and independent verification of these apps and devices is not wholly reassuring. They are not being sold as medical devices, so regulators do not get involved. I am often told that older runners need more time to recover between each run, so I asked Runna’s Holohan to reassure me that Runna would take into account the fact that I was 52 years old. Alas, he could not. Age-adaptive plans were still on the drawing board, he told me. So were training plans that reflected the menstrual cycle of female athletes.

Reassurance was no more forthcoming from Garmin. The company wouldn’t make anyone available for an interview, and ducked every question about whether the Garmin training recommendations took into account my age.

Facing a marathon, then, which app should I choose? I respect their behavioural savvy and would expect any of them to tug my strings like an expert puppet master, but I am less confident of the physiological science behind their recommendations, as their methods are secret and their pretensions to rigour largely untested.

I don’t mean to be ungrateful: my inexpensive Garmin watch and the free coaching app that was bundled with it took me from weekly wayward 5k runs to a well-paced half-marathon. But perhaps I have come to expect a little too much from my silicon coach.

Iefore my half-marathon, my Garmin app told me my predicted time was 1hr 54 minutes and 56 seconds. Strava, looking at exactly the same data, told me I could go a full 11 minutes faster. Even over a distance of more than 21km, 11 minutes is a huge difference. This put me in a quandary before the race. Everyone warned me not to go off too fast — but given the yawning gap between the algorithmic forecasts, what did “too fast” even mean?

“If you spoke to two different humans they might do the same thing,” says the digital health expert Maher. “It’s easy to believe that technology just has the answer.”

A fair point. I’d never tried to set a half-marathon time before, so any forecast would be little better than a guess. Yet that did not stop both Strava and Garmin making their race predictions to within the nearest second. And it did not stop me taking both of them seriously, and hesitating when they contradicted each other.

*

It is a sobering experience to stare at a marathon training plan.

Monday — Strength Training — 30 minutes

Tuesday — Fartlek (“speed play”) run — 10 minutes @6:05/km. 8 mins @5:35/km, 2 mins easy. 5 mins @ 5:15/km, 90 sec easy. 4 mins @5:10/km, 90 sec easy. 3 mins @5:00/km, 90 sec easy. 2 mins @4:50/km, 90 sec easy. 1 min @4:35/km, 90 sec easy. 10 mins @6:05/km

Wednesday — Easy Run — 45 mins @6.05/km, 15 mins @5.45/km

Thursday — Cross Training — 45-60 mins

Friday — Strength Training — 30 mins

Saturday — Threshold Run — 15 mins @6:05/km, 5 x 5 mins @5:05/km with 1 min rest after each, 15 mins @ 6:05/km

Sunday — Long Run — 120 minutes @6.05/km

That’s week one. It would be an oversimplification to suggest that the following 15 weeks are the same, but further and faster — but not a grotesque one. Although such a training block isn’t easy, it isn’t complicated either. With your fitness watch on and the training schedules programmed in, just pull on your shoes, head out the door and follow the watch’s orders.

But the longer I have followed this sort of plan, and the more I spoke to people in the world of fitness trackers, the more I feel that there is something missing — something unquantifiable. Serendipity, perhaps? Variety? Playfulness? Look again at that Tuesday “speed play” session. Speed, yes. But there is nothing playful about it.

These training plans are relentless and not just in the obvious fashion, where a 52-year-old body with niggles and twinges and the occasional 14-hour work day faces an implacable silicon coach which refuses to negotiate. My physiotherapist shook her head in exasperation when I told her I was planning to use the Runna app for my marathon preparation. Having seen too many people allow an app to overtrain them into injury, she urged me to think again.

But the relentlessness comes in another guise, too. It isn’t just the grind and the risk of injury, but all the times I passed up opportunities that the watch and the training plan could not quantify — opportunities to run with a friend or my wife or my informal local running club. The watch tends to have other plans, and I do not want to disappoint the watch. That is the nature of quantification fixation.

As I reflected on these missed opportunities, I realised that running apps could, in principle, set us a very different kind of training programme.

*

In 1976, David Bowie fled to West Berlin. Beset by legal troubles, drug abuse and a disintegrating marriage, he later recalled, “It was a dangerous period for me.” In the shadow of the machine gun nests along the Berlin Wall, it seemed an unpromising place to make a record. But Bowie had a way of finding new challenges and constraints, which may be why he asked Brian Eno to join him.

Eno began showing up at the Hansa Studios with a selection of cards he called Oblique Strategies. Each card had a different, often baffling instruction:

Emphasise the flaws

Only a part, not the whole

Change instrument roles

Eno would draw a single card at random, and push the musicians to respond. They did not necessarily approve of his randomised provocations — “This experiment is stupid”, complained guitarist Carlos Alomar — but it is hard to argue with the results: two of the decade’s most critically acclaimed albums, Low and “Heroes”.

Years later I asked Eno what the idea of these cards was supposed to be. “The enemy of creative work is boredom,” he told me. “And the friend is alertness.” The random inscrutability of the cards kept generating new situations and new problems. And, as a result, pushed the musicians into situations that could be frustrating but could also be exciting.

So what about injecting a little excitement into marathon training with the occasional Oblique Training Run?

Monday, gym. Tuesday, easy run. Wednesday, go for a run dressed as superman.

Monday, gym. Tuesday, easy run. Wednesday, pack a picnic, run somewhere nice, get the bus home.

Monday, gym. Tuesday, easy run. Wednesday, get a head torch and run in the dark.

Run with a fast friend.

Run with a slow friend.

Make three people smile.

Run a route that draws a picture on the Strava map.

Run with a different soundtrack.

Run in silence.

I’m in training now; wish me luck. My fitness watch will be a vital part of my training practice, but it won’t be the only part. If you see an economist running up the river Thames dressed as Superman or carrying a picnic, that is because in running, as in life, much of what matters cannot be measured.

In their ability to track our running metrics, plot out complex progressions, and push us hard, fitness watches are a wrist-borne marvel. If I make it to the start line of the London marathon in April, I will have my watch on my wrist, pacing every step.

But like Pheidippides, I’ll also hope to have joy in my blood.

I’m running the London marathon run is in aid of the Teenage Cancer Trust. tinyurl.com/HarfordMarathon

First published in FT Magazine on 17 January 2026

To celebrate – or commiserate – this year’s Valentine’s Day, Tim has something a little different. Straight from the Cautionary Library of misadventures comes a bumper crop of romantic blunders, featuring super glue, a lost hotel deposit and a very wet explosion. Love, it seems, makes fools of us all. 

This episode is available exclusively to members of the Cautionary Club, and Pushkin+ subscribers.

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Further Reading

This episode of Cautionary Tales relied on the Cautionary Tales library – and specifically The World’s Greatest Mistakes by Nigel Blundell, Epic Fail by Mark Leigh, and two wonderful collections by Stephen Pile: The Book of Heroic Failures and its sequel, The Return of Heroic Failures.

Other sources include the Sydney Morning Herald, the Los Angeles Times, Futility Closet and BookTryst.

In the final days of the Sixties, The Rolling Stones join forces with other rock legends to plan a free concert at Altamont that will rival Woodstock.The “bad boys of rock” don’t have the best relationship with the police, so they think of another option for security: The Hells Angels. They’re both anti-establishment, they’re both counterculture: what could possibly go wrong? 

This episode was originally released to subscribers. For ad-free listening, monthly bonus episodes, monthly behind-the-scenes conversations, our newsletter, and more, please consider joining the Cautionary Club.

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Further reading

Altamont: The Rolling Stones, the Hells Angels, and the Inside Story of Rock’s Darkest Day by Joel Selvin

LIFE Rides With Hells Angels, 1965

A Long Strange Trip, Dennis McNally

Hell’s Angels, Hunter s Thompson

Keith Richards on Keith Richards, ed Sean Egan

Keith Richards, Victor Bockris 

Life, Keith Richards 

Mick Jagger, Philip Norman

Stone Alone, Bill Wyman

Old Gods Almost Dead, Stephen Davis

Don’t look back: The story of Altamont, the rock festival that the ’60s wants to forget. Geoff Edgers, Washington Post 21 Nov, 2019

The Rolling Stones Disaster at Altamont: Let It Bleed. Rolling Stone January 21, 1970

The long strange saga of the Grateful Dead and the Hells Angels. SF Gate, June 2022. 

Tappin, B., Van Der Leer, L., & McKay, R. (2017). The heart trumps the head: Desirability bias in political belief revision. Journal of Experimental Psychology: General, 146(8), 1143-1149. https://doi.org/10.1037/xge0000298

In February 1912, noted scientist Arthur Woodward received an intriguing letter from Charles Dawson, a country lawyer with a growing reputation as an amateur geologist. Dawson told Woodward that he had found fossilised fragments of human skull in the flint beds of Piltdown near the south coast of England. The find looked pretty special. It was.

The skull of Piltdown Man, with an apelike jaw and a large cranium, seemed to be a missing link in the evolutionary chain between modern humans and our primate ancestors. It was more than four decades before researchers began to suspect a hoax — and quickly discovered compelling evidence that every single discovery associated with Piltdown had been a fake.

I had long regarded the Piltdown fake as a unique product of the Edwardian age. Now I am not so sure. Some of the most famous “discoveries” in psychology are also being exposed — sometimes decades after the fact — as distorted, misreported or exaggerated to a disturbing degree. For a while, it seemed that 1950-1975 was a heroic age of psychological research, in which bold — if ethically questionable — findings seared themselves on the public consciousness. There was the Stanford Prison Experiment in 1971, in which student volunteers were invited by the psychologist Philip Zimbardo to act out the roles of prisoners and prison guards. The study swiftly deteriorated into dehumanising abuse, as the guards embraced their role as fascist thugs with too much enthusiasm. 

There was the UFO cult who intensified their beliefs at precisely the moment (December 22 1954, just after midnight) that their prophecy of the end of the world failed to materialise: all witnessed by undercover researchers. 

There was the “Robber’s Cave experiment”, also in 1954, in which the psychologist Muzafer Sherif organised a summer camp at Robbers Cave State Park, Oklahoma, for 11-year-old boys. He and his associates then took notes as the camp descended into a hellish real-life version of Lord of the Flies. 

What a collection of daring, epic research discoveries. Alas, they were more than merely daring: they were downright misleading. Begin with the Stanford Prison Experiment — a misnomer from the start, since there was no experimental control. Thanks to some detective work by Thibault Le Texier, a historian, it seems clear that the experiment’s mastermind, Zimbardo, heavily coached the “guards” to dehumanise and brutalise the “prisoners”. The prison simulation has traditionally been described as a surprising and spontaneous eruption of brutality. Le Texier sets out a strong case that the brutality was orchestrated by the experimenters from the start.

There is a similar story to be told about the Robbers Cave study. The superficial telling of this tale is that a group of boys were recruited to participate in a summer camp. Sherif and his collaborators — playing the role of camp counsellors — split the boys into two groups (the “Eagles” and the “Rattlers”) and organised baseball and tug-of-war contests with prizes. Sherif correctly predicted that the competition for resources between the groups would lead to bitter rivalry and fighting, and that the groups could then be reconciled by the presence of an external threat: vandalism to the camp’s water supply.

As with Zimbardo, there were always questions over the ethics of this study — some of the boys found the experience distressing, and none of them was told that they had been the subjects of an experiment.

But more recent research raises scientific questions, too. Historian Gina Perry, in her book The Lost Boys (2018), points out that the experimenters had to go to some lengths to engineer the tribal rivalry they had predicted, and that the note-taking observers often disagreed about what they were seeing. Those who had worked with Sherif on his theories found evidence to support them, while more independent observers would often describe very different dynamics. Strangest of all, Sherif had run another study the year before, in which the boys stubbornly refused to hate each other, and concluded — correctly — that the camp staff kept trying to stir up trouble. That study was buried in the archives, barely mentioned. “It was as if Sherif wanted to forget it,” writes Perry.

The next shoe to drop? When Prophecy Fails (1956), the classic account of the UFO cult, was written by more giants of 20th century psychology: Leon Festinger, Henry Riecken and Stanley Schachter. Festinger and his colleagues had infiltrated the UFO cult and described behaviour in line with Festinger’s theory of cognitive dissonance: when the cult’s apocalyptic predictions did not emerge, the core members of the group clung even more firmly to their beliefs, and began to evangelise about them at the very moment they seemed to have been disproved.

In work published late last year, researcher Thomas Kelly shreds this story of its credibility. Kelly had access to unsealed archival material, which demonstrated that the authors had misreported many of the events, distorting them to fit Festinger’s theory. They also interfered with the psychological processes they were purporting to observe, manipulating cult members through their conversations and even fabricating psychic messages. “Every major claim of the book is false,” writes Kelly, “and the researchers’ notes leave no option but to conclude the misrepresentations were intentional.”

Most shocking of all to fans of elegant writing — if not to scientists — has been the recent revelation by Rachel Aviv in The New Yorker that the neurologist Oliver Sacks, author of beloved books such as Awakenings (1973), had exaggerated and distorted the cases he wrote about and was wracked with guilt about the fabrications. 

In a letter to his brother, Sacks described The Man Who Mistook His Wife for a Hat (1985) as “fairy tales” and “half-report, half-imagined, half-science, half-fable”. Were millions of readers told they were paying for fairy tales? They were not. Are there any lessons to be drawn from such a catalogue of distortion and exaggeration? There’s the old warning against stories that are too good to be true, and it applies here. But there’s also a structural problem. The rewards to “discovering” a spectacular scientific finding are large; the rewards to debunking frauds or deflating exaggerated claims are small if not non-existent. If these are the rules of the game, we should not be surprised at the way the game is played.

Written for and first published in the Financial Times on 14 Jan 2026.

I’m running the London Marathon in April in support of a very good cause. If you felt able to contribute something, I’d be extremely grateful.

Run by the charismatic Nicole Deadone, OneTaste billed itself as a sexual wellness startup celebrating the power of female orgasm. But behind the celebrity endorsements and promises of healing, lay a darker reality. When Bloomberg journalist Ellen Huet began to dig into the organisation, she uncovered financial, emotional and sexual exploitation of its members, many of whom would call the company a cult. Huet, author of Empire of Orgasm, joins Tim to discuss why we should beware people promising pleasure, and what we can learn from the rise and fall of OneTaste.

Cautionary Club members get access to ad-free listening, monthly bonus episodes, monthly behind-the-scenes video conversations with the production team, and our monthly newsletter. Please consider joining if you would like to support what we do on Cautionary Tales. Thank you!

[Apple] [Spotify] [Stitcher]

Run by the charismatic Nicole Deadone, OneTaste billed itself as a sexual wellness startup celebrating the power of female orgasm. But behind the celebrity endorsements and promises of healing, lay a darker reality. When Bloomberg journalist Ellen Huet began to dig into the organisation, she uncovered financial, emotional and sexual exploitation of its members, many of whom would call the company a cult. Huet, author of Empire of Orgasm, joins Tim to discuss why we should beware people promising pleasure, and what we can learn from the rise and fall of OneTaste.

Cautionary Club members get access to ad-free listening, monthly bonus episodes, monthly behind-the-scenes video conversations with the production team, and our monthly newsletter. Please consider joining if you would like to support what we do on Cautionary Tales. Thank you!

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As a way of dealing with high demand, the age-old practice of forming a long, orderly queue has something to be said for it: simplicity, transparency and equal treatment for all. But no matter how much the British are said to love a queue, you can have too much of a good thing. The UK’s public services are under strain in all sorts of ways and it is striking how many of the problems can be described as a gridlock of hidden queues.

Consider ambulance response times. Ambulances in England are supposed to arrive within a given target time, depending on how urgent the situation is. In “category 2” situations such as heart attacks and strokes (aka pretty damned urgent), the target is to respond in 18 minutes on average. The bad news is that the NHS isn’t hitting these targets: in November last year, the average response for category 2 calls was more than 32 minutes, the worst since last January. The good news is that things have been much worse than that in recent memory: at the end of 2022, the average response time was more than an hour. That’s the result of a queue that not even the British could love.

Why is this happening? The obvious explanation is that there are not enough ambulances, but the deeper problem is that ambulances themselves are being delayed in discharging patients into A&E units, which are themselves often overwhelmed: in the first quarter of 2014, 134 patients waited more than 12 hours in A&E before being admitted; 10 years later the figure was 141,693. The long delays in A&E are in part the result of the hospital beds all being full and that, in turn, is in part because hospitals sometimes struggle to discharge vulnerable patients into an overstretched social care system. All of these problems are a kind of queue and they all interact in a surprising way: you can die waiting for an ambulance because there aren’t enough nursing homes in your area.

What is striking is that the same pattern emerges in other parts of the public sector. For example, the prison system is full almost to capacity. That is partly the result of three decades of successive governments deciding that sentencing guidelines should be punitive, while also being unwilling to build enough prisons. But it is also the result of interacting queues: about one-fifth of the prison population is either awaiting a trial or awaiting a sentence, which means that delays in the court system feed into crowding in the prison system.

The study of queues dates back more than a century, with the initial spark coming from a mathematically gifted Danish telephone engineer named Agner K Erlang. Erlang combined his elegant mathematical ideas with a practical approach. He wandered around the streets of Copenhagen accompanied by a ladder-bearing assistant so that Erlang could descend through manholes to measure currents. (The unit of load on a queue-processing element, whether a telephone line, a supermarket checkout or a toilet cubicle in a theatre, is the erlang. So now you know.)

Since Erlang, the modelling of queues has blossomed, along with an alphabet soup of acronyms, including PQ (priority queuing), FCFS (first come first served — as any true Briton would advise) and the suspiciously continental-sounding SIRO (service in random order). The queuing literature has produced many ideas and, while some of the conclusions are obvious (queues form when there isn’t enough capacity to match demand), there are subtleties worth pondering.

First, when bottlenecks feed into bottlenecks, some strategic thinking is required to fix the system. There is often more than one bottleneck in a congested system and opening that bottleneck will sometimes mean the same queue builds up somewhere else.

Second, the optimum queuing time probably isn’t zero. In most cases, demand arrives at irregular intervals and it is likely to be impractically wasteful to have so much capacity (so many doctors, so many ambulances, so many crown courts) that even after a sudden surge in demand, nobody has to wait.

That said, the optimum queuing time should probably be kept quite short. Imagine a situation where an emergency doctor can see four patients an hour and patients arrive every 15 minutes. At first everything is fine: every patient can be seen immediately. Then something goes wrong. Perhaps there’s a sudden rush, when five patients unexpectedly arrive together. Perhaps the doctor takes an hour off for lunch. The waiting time suddenly increases from nothing to an hour, even though the doctor is still seeing four patients an hour and four patients an hour are still arriving.

The moral of this very simple story is that even if the capacity of the system is equal to the demand for it, queues can grow and then stay at unpleasant lengths. What’s needed is a little extra capacity to work through the inevitable queues that build up from time to time. Unfortunately, systems under intense pressure rarely have a little extra capacity hanging around.

Third, it can be hard to increase the capacity of a system. Let’s say that we have one million nurses and each nurse trains for two years before working for 20. Arithmetically, that requires 100,000 nurses to be in training at any given moment. What if it is decided that we need 1.1 million trained nurses and we need them as soon as possible? That would require an immediate recruitment boost, doubling the number of nurses in training.

Would that be possible? Even though the expansion in nursing personnel seems modest, it requires nursing courses to double in size and then to shrink again after a couple of years. An even more dramatic expansion will be needed at the advanced training colleges at which the teachers of nursing are themselves trained. It might be easier to persuade nurses to stay a little longer in the profession or to recruit from the Philippines.

This unpleasant arithmetic makes it all the more frustrating when the obstacles to capacity expansion seem unnecessary. My colleague Sarah O’Connor recently described the large stock of frustrated foreign-qualified dentists in the UK who cannot practice dentistry because they are waiting to take a registration exam. There are 8,000 dentists on the waiting list; 350 of them scrambled through the chaotic registration process in 2024. At this rate everyone now on the waiting list will be able to practise dentistry before 2050.

Queuing can be a fiendishly difficult problem to solve, but not always. Sometimes the free lunch is right in front of us, waiting to be eaten.

Written for and first published in the Financial Times on 7 Jan 2026

I’m running the London Marathon in April in support of a very good cause. If you felt able to contribute something, I’d be extremely grateful.

Tony Hsieh, the billionaire CEO of Zappos, is passionate about community. He pours his time, energy and fortune into building a network of like-minded people – first in Las Vegas, then Park City, Utah. But Tony’s quest to build connection soon spirals into isolation, addiction and mistrust of those closest to him, revealing a contradictory truth about the pursuit of one of our most fundamental human needs.

Cautionary Club members get access to ad-free listening, monthly bonus episodes, monthly behind-the-scenes video conversations with the production team, and our monthly newsletter. Please consider joining if you would like to support what we do on Cautionary Tales. Thank you!

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Further reading

Tony Hsieh’s life story is told in Wonder Boy: Tony Hsieh, Zappos and the Myth of Happiness in Silicon Valley, by Angel Au-Yeung and David Jeans; Happy at Any Cost: The Revolutionary Vision and Fatal Quest of Zappos CEO Tony Hsieh, by Kirsten Grind and Katherine Sayre; and Tony’s own autobiographical business book, Delivering Happiness: A Path to Profits, Passion and Purpose.

Phil Prentiss’s account of Jody Sherman’s story is reported in Business Insider. This script also drew on Aimee Groth’s book The Kingdom of Happiness: Inside Tony Hsieh’s Zapponian Utopia, and reporting in QZ, Vox [1], [2], The Atlantic, New Republic, Las Vegas Review Journal, and an interview with Tyler Williams on Burner Podcast.

John Kay’s book is Obliquity: Why our goals are best achieved indirectly. Can Seeking Happiness Make People Happy? Paradoxical Effects of Valuing Happiness is a study by Iris B Mauss, Maya Tamir, Craig L Anderson, and Nicole S Savino.

They say the company Zappos is harder to get into than Harvard. The company may sell shoes, but its mission is to deliver WOW, through a fun-focused, values driven company culture, making it one of the most coveted places to work in America. At the centre is CEO Tony Hsieh, obsessed with the hunt for happiness and driven by increasingly  bold – and strange – ideas about how to find it. 

Cautionary Club members get access to ad-free listening, monthly bonus episodes, monthly behind-the-scenes video conversations with the production team, and our monthly newsletter. Please consider joining if you would like to support what we do on Cautionary Tales. Thank you!

[Apple] [Spotify] [Stitcher]

Further reading

Tony Hsieh’s life story is told in Wonder Boy: Tony Hsieh, Zappos and the Myth of Happiness in Silicon Valley, by Angel Au-Yeung and David Jeans; Happy at Any Cost: The Revolutionary Vision and Fatal Quest of Zappos CEO Tony Hsieh, by Kirsten Grind and Katherine Sayre; and Tony’s own autobiographical business book, Delivering Happiness: A Path to Profits, Passion and Purpose.

This script also drew on Aimee Groth’s book The Kingdom of Happiness: Inside Tony Hsieh’s Zapponian Utopia, and reporting in the New Yorker, New Republic, Fortune, The Atlantic, HBR, 8 News Now, Medium, and an interview with Tyler Williams on Burner Podcast.

John Kay’s book is Obliquity: Why our goals are best achieved indirectly.