On first listen, some things grab you
for their off-kilter novelty. Like the story of a company that has
hired a bunch of “musicologists,” who sit at computers and listen to
songs, one at a time, rating them element by element, separating out
what sometimes comes to hundreds of data points for a three-minute
tune. The company, an Internet radio service called Pandora, is
convinced that by pouring this information through a computer into an
algorithm, it can guide you, the listener, to music that you like. The
premise is that your favorite songs can be stripped to parts and
reverse-engineered.
Some elements that these musicologists (who, really, are musicians
with day jobs) codify are technical, like beats per minute, or the
presence of parallel octaves or block chords. Someone taking apart Gnarls Barkley’s
“Crazy” documents the prevalence of harmony, chordal patterning, swung
16ths and the like. But their analysis goes beyond such objectively
observable metrics. To what extent, on a scale of 1 to 5, does melody
dominate the composition of “Hey Jude”? How “joyful” are the lyrics?
How much does the music reflect a gospel influence? And how “busy” is Stan Getz’s
solo in his recording of “These Foolish Things”? How emotional? How
“motion-inducing”? On the continuum of accessible to avant-garde, where
does this particular Getz recording fall?
There are more questions for every voice, every instrument, every
intrinsic element of the music. And there are always answers, specific
numerical ones. It can take 20 minutes to amass the data for a single
tune. This has been done for more than 700,000 songs, by 80,000
artists. “The Music Genome Project,” as this undertaking is called, is
the back end of Pandora.
Pandora was founded in Oakland a decade ago, and for much of the
intervening time has lived a precarious existence (the founders spent
one three-year stretch working without salaries while they scrambled
for investors). But thanks in part to the popularity of the Pandora iPhone
app, its fortunes have lately improved. It has attracted 35 million
listeners and claims about 65,000 new sign-ups a day (more than half
from mobile-device users). About 75 companies are working Pandora into
a variety of gizmos and gadgets and Web platforms. The business model
relies largely on advertising, and its founder, Tim Westergren, says
Pandora will very likely turn its first profit in the fourth quarter of
this year.
However things play out for Pandora as a business, its approach is
worth understanding if you’re interested in the future of listening.
It’s the “social” theories of music-liking that get most of the
attention these days: systems that connect you with friends with
similar tastes, or that rely on “collaborative filtering” strategies
that cross-match your music-consumption habits with those of
like-minded strangers. These popular approaches marginalize traditional
gatekeepers; instead of trusting the talent scout, the radio programmer
or the music critic, you trust your friends (actual or virtual), or
maybe just “the crowd.”
Pandora’s approach more or less ignores the crowd. It is indifferent
to the possibility that any given piece of music in its system might
become a hit. The idea is to figure out what you
like, not what a market might like. More interesting, the idea is that
the taste of your cool friends, your peers, the traditional music
critics, big-label talent scouts and the latest influential music blog
are all equally irrelevant. That’s all cultural information, not
musical information. And theoretically at least, Pandora’s approach
distances music-liking from the cultural information that generally
attaches to it.
Which raises interesting questions. Do you really love listening to the latest Jack White project? Do you really hate the sound of Britney Spears?
Or are your music-consumption habits, in fact, not merely guided but
partly shaped by the cultural information that Pandora largely screens
out — like what’s considered awesome (or insufferable) by your peers,
or by music tastemakers, or by anybody else? Is it really possible to
separate musical taste from such social factors, online or off, and
make it purely about the raw stuff of the music itself?
Tim Westergren is a familiar type: the
musician who was not as successful as he might have been and concluded
that the system is flawed because it underrates talented people who
deserve a bigger audience. He played in bands that never quite took off
and for a time worked as a film-score composer. It was that job — a
“methodical, calculating form of composition,” he says — that led him
to dwell on the way music works and forced him to decode the individual
taste of whatever director had hired him. He says he was getting pretty
good at this. “So I thought I’d try to codify it,” he says.
Rangy and bright-eyed at 43, Westergren comes off more like the head
of a fan club than an erstwhile rock star. The only time he seems
annoyed is when he’s talking about how some unpopular musicians are
unfairly overlooked — or how some popular ones are unfairly maligned.
Pandora is, in effect, a response to both of those problems.
He founded his company with two tech-and-business-savvy pals in the
start-up-friendly year of 1999. Back then it was called Savage Beast
Technologies, and the early (not exactly farsighted) business model
involved listening kiosks in record stores. Eventually the company got
new financing, beefed up the executive team and landed on using its
genome as the engine of an Internet radio service “that plays only
music you like.”
Pandora went online in 2005 and looked much as it does today. When
you arrive at the site, you’re invited to type in the name of an
artist, or a specific song. Let’s say you type in “These Foolish
Things,” by Stan Getz. The Pandora genome looks for something it judges
to have a similar infrastructure — like, when I tried recently, “I
Don’t Know Why,” by Don Byas.
This is Pandora’s first guess at a song you will like, based on upon
its analysis of the song you picked. You can simply let it play; click
a “thumbs down” icon to try another song; or give it a thumbs up if you
want Pandora’s algorithm to know this was a particularly good choice.
You can also click to learn why the song was chosen: you don’t get a
full breakdown but rather a kind of thumbnail summation. In this case
the Byas tune was chosen “because it features swing influences, a
leisurely tempo, a tenor-sax head, a tenor-sax solo and acoustic-piano
accompaniment.”
If you click a lot, the idea is that Pandora’s algorithm adjusts,
squaring your taste with the genome’s database. There are other ways to
tweak things — adding more songs to a “station” for the system to
scrutinize, creating different stations based on other artists or
songs, telling the service not to play a given song for a while. (This
happens on a station-specific basis: whatever preferences I express on
a station based on “My Sharona” would not affect the songs on, say, my
Yanni station.)
Relying on advertising revenue — visual ads on its site as well as
occasional audio ads interspersed between songs on your stations —
means that much depends on Pandora’s genome doing a good-enough job to
keep people listening. (There’s also a “premium” ad-free service for
$36 a year, and Pandora makes a small commission if you click through
its site to buy a song on iTunes or Amazon.com,
but it’s primarily an ad-driven business.) Its biggest expense is the
licensing fee it pays to publishers and performers; the performance
fee, paid to an entity called SoundExchange, which distributes
royalties to artists, is equal to something like 50 percent of
Pandora’s revenue. When you start a station with a specific song, that
song isn’t the first thing you hear, because this would an entail an
“on demand” license, which costs even more.
By way of Pandora’s Twitter
feed, I issued a call for users who not only listened to the service a
lot but also felt that it had had some kind of impact on their
listening tastes. Summer Sterling, a 21-year-old senior at Washington
and Lee University in Lexington, Va., often starts by typing in
well-known bands like the Dixie Chicks,
and that has led her to music by groups she had never heard of but now
loves, like the Weepies. Stephanie Kessler, a 24-year-old M.B.A.
student in St. Louis, started by typing in K T Tunstall and has found
her way to Waylon Jennings and David Allan Coe.
Aashay Desai, a 25-year-old computer engineer, has become a “very
meticulous” user, building some 30 stations and paying for Pandora’s
premium service, which offers better sound quality and more features.
Aside from his hard rock/metal station, he has a “metalcore” station
that’s “a little more aggressive,” as well as a “polyrhythm metal
station” that is probably his “most aggressive.” He has also built an
R&B station and a trance station; more recently he discovered Django Reinhardt, whom he used as the basis for a gypsy jazz station.
Others, of course, are not impressed by the genome’s results.
Someone passed along to me a harsh assessment by Bob Lefsetz, whose
popular Lefsetz Letter critiques pretty much every aspect of the
contemporary music business. “I tried and rejected it,” he wrote. “Was
flummoxed when a Jackson Browne station I created delivered a Journey
song. Huh? . . . Jackson is music for the mind, Journey is music for
the MINDLESS!”
Jonathan McEuen told me he heard about Pandora a couple of years ago
and started using it immediately, “with the goal of breaking whatever
algorithm they had.” A devoted music fan and a musician himself, McEuen
says he did not believe an online service could understand what sort of
music he would like and introduce him to new artists based on some
deconstruction of his listening tastes. “You can’t just reduce it to a
bunch of numbers,” he recalls thinking. “This is a romantic, emotional
thing,” and Pandora’s approach to it “can’t work.”
He has changed his mind. A 28-year-old clinical neuroscience researcher at the University of Pennsylvania,
he’s a listener who lacks the time to keep up with music news the way
he did while amassing hundreds of CDs as a student. Sometimes he runs
Pandora as background music; sometimes he’s more engaged, using it as a
way to learn about contemporary classical and opera — and as a result
has become a fan of the music of a young composer named Eric Whitacre.
“I don’t know how else I would have found out about it,” he says.
“Except through the exhaustive process of making new friends on the
Internet. Which is something I’m kind of loath to do.”
What I didn’t hear Pandora users talk about
was the Genome Project; many didn’t really know about it. They cared
about the music Pandora served up, period. But I wanted to know what
was behind that music.
Nolan Gasser was the primary shaper of the lexicon that could
reconcile Westergren’s genome metaphor with something a computer could
evaluate. Gasser, an actual musicologist, wrote a doctoral thesis that
dealt with close analyses of Renaissance composition. “I really needed
to know what made that music tick,” he recalls. That systematic study
flowed well into his work with Westergren — although they started with
20th-century pop, not Renaissance vocal music. First every piece is
broken down into large-scale aspects of music: melody, harmony, rhythm,
form, sound (meaning instrumentation and, if necessary, voice), and in
many cases the text, meaning lyrics. Each of these broader categories
might have 10, 30, 50 elements.
“We have a number of characteristics for vocals,” he continues. “Is
it a smooth voice, is it a rough, gravelly voice, is it a nasally
voice?” Similar questions are evaluated for every instrument. The
upshot was about 250 “genes” for every song in the original pop-rock
version of the “genome.”
Gasser also helped develop the training mechanisms to make sure the
analysts are consistent about more subjective matters — like how
“emotionally intense” that Stan Getz solo is. (It’s a 4 out of 5, in
the genome’s view.) The test that candidates take involves being able
to pick out, quickly and by ear, harmonic structures, melodic
organization and other musical elements. The indoctrination that
follows revolves around examples. (You think that vocal gets a 5 on the
gravelly scale? Here’s Tom Waits. Is it that gravelly? )
Recently I sat in as several of Pandora’s song deconstructors
gathered in a small conference room to talk about Indian music. Pandora
listeners have been asking for Indian music for a while, but adding it
to the service hasn’t been a simple matter. A new genre must arrive in
a big batch — about 3,000 pieces of music — because Pandora’s algorithm
needs lots of choices to be able to recommend something
similar-sounding. And all of it has to be pulled apart first. This
entails squaring the very different structures of Indian music with
Pandora’s “genome” data points.
Over the previous six weeks or so, the Pandora analysts listened to
650 Indian pieces, and the session I observed was a refresher course.
Steve Hogan, who oversees Pandora’s analyst squad, had given a
half-dozen of its members the same two songs to analyze. The first was
“Raga Ahir Bhairav,” recorded by Bismillah Khan in 1955. But the
analysts had not been given this cultural information; all they had for
the assignment was the music and their ears. Hogan played a snippet and
pointed to Kurt Kotheimer, a bass player who often gigs around the Bay
Area.
Kotheimer consulted his listening notes: “Flat second, major third,
perfect fourth, perfect fifth, major sixth, flat seventh.” Everybody
nodded: that’s the tone set, which helps identify the particular raga,
one of 25 new “genes” added to Pandora’s algorithm to accommodate this
variety of non-Western music. Based on the beat, everyone agreed that
this raga was set in Teentaal, with a 16-beat rhythmic cycle often
heard in North Indian classical music; it’s now in the genome too. But
that was the easy part, apparently.
They moved on to vocals, and Alan Lin, a violinist, ticked off the
scores he came up with for things like rhythmic intensity and the
relative exoticism of the melody scale. “I actually put exotic at 3.5,”
he said. This prompted Sameer Gupta — a percussionist and an expert on
Indian music who was weighing in by speakerphone from New York — to
lead a brief discussion of how to think about melody and exoticism in
this context. Seven or eight scores related to melody, and then about
the same number for harmony. (“A 5 for drone,” one analyst announced.)
More scores related to form. Tempo. The timbre of the reeds. When Gupta
gave his score for riskiness on the percussion — a 3.5 — Lin did a sort
of fist pump: “Yes!” Evidently he’d scored it the same way, meaning
progress toward properly fitting Indian music into the Music Genome
Project. Things went on like this for a while. “Even if you have a solo
violin with a tabla, you’re still going to have monophony,” Gupta
remarked at one juncture. “I just wanted to point that out.” It was
hard to believe there was a business riding on this kind of
conversation.
But while some of the genes involve expert, subjective judgment,
they aren’t qualitative in the most traditional sense: there’s no
rating that allows an analyst to conclude that a vocal or a sax solo is
simply lousy. What Pandora’s system largely ignores is, in a word,
taste. The way that Gasser or Westergren might put this is that it
minimizes the influence of other people’s
taste. Music-liking becomes a matter decided by the listener, and the
intrinsic elements of what is heard. Early on, Westergren actually
pushed for the idea that Pandora would not even reveal who the artist
was until the listener asked. He thought maybe that structure would
give users a kind of permission to evaluate music without even the most
minimal cultural baggage. “We’re so insecure about our tastes,” he says.
While his partners talked him out of that approach, Westergren
maintains “a personal aversion” to collaborative filtering or anything
like it. “It’s still a popularity contest,” he complains, meaning that
for any song to get recommended on a socially driven site, it has to be
somewhat known already, by your friends or by other consumers.
Westergren is similarly unimpressed by hipster blogs or other
theoretically grass-roots influencers of musical taste, for their
tendency to turn on artists who commit the crime of being too popular;
in his view that’s just snobbery, based on social jockeying that has
nothing to do with music. In various conversations, he defended
Coldplay and Rob Thomas, among others, as victims of cool-taste
prejudice. (When I ran Bob Lefsetz’s dismissal of Pandora by him, he
laughed it off, and transitioned to arguing that Journey is, actually,
a great band.)
He likes to tell a story about a Pandora user who wrote in to complain that he started a station based on the music of Sarah McLachlan, and the service served up a Celine Dion
song. “I wrote back and said, ‘Was the music just wrong?’ Because we
sometimes have data errors,” he recounts. “He said, ‘Well, no, it was
the right sort of thing — but it was Celine Dion.’ I said, ‘Well, was
it the set, did it not flow in the set?’ He said, ‘No, it kind of
worked — but it’s Celine Dion.’ We had a couple more back-and-forths,
and finally his last e-mail to me was: ‘Oh, my God, I like Celine Dion.’ ”
This anecdote almost always gets a laugh. “Pandora,” he pointed out, “doesn’t understand why that’s funny.”
By the time the Genome Project got under way,
the idea of taking music apart and evaluating it by its acoustic
elements was not actually new. “Machine listening” was pioneered in
various university settings, often by people who had the exact same
problem with collaborative filtering’s reliance on social data that
Westergren has. Machine listening basically involves teaching computers
to assess sound (or really, waveforms representing sound) into
something resembling the way that humans hear it, with the goal of
eliminating living, breathing listeners from the evaluation process
completely.
Like collaborative filtering, machine listening can deal with a lot
of data quickly. And when Westergren was trying to raise a second round
of financing after the dot-com bust, most everyone involved in the
business of music and technology had come to believe that any
recommendation system needed to be able to handle millions of songs,
instantly. A bunch of musicians sitting around discussing the finer
points of drone and monophony wouldn’t cut it. “Everybody thought it
was ridiculous,” Westergren agrees. He gave something like 350 pitches
to venture capitalists over three years. “Most investors could not get
over this idea that we were using humans.” But to Westergren, there
were elements of music that machine listening just couldn’t capture —
like the emotionality of a Getz solo. So yes, he wants listeners to
experience new music on the basis of the music and not the influence of
other people — but to do it right, people have to analyze the music.
Whatever the algorithmic equation, of course, there’s a listener on
the other end who is much harder to decode. What you want to hear can
depend on your mood, or whether you’re listening at work or in a
nightclub. Context affects any cultural product, but music is different
from, say, books or movies. Even a casual listener hears many thousands
of songs; and to love a song is to take it in — whether attentively or
as background music — over and over. Mick Jagger
was once asked what makes a tune a classic, and the co-author of “(I
Can’t Get No) Satisfaction” replied, “Repetition.” And yet, even the
most conservative listener knows the feeling of hearing a hit single
once too often. Maybe because music is so ubiquitous, we respond to it
almost like food: sometimes we want to try the new restaurant,
sometimes the comfort of a familiar favorite dish.
Still, are all these listener-specific factors really enough to
explain what music we like, and why? “Music is an inherently social
experience,” argues David Goodman, the president of CBS
Interactive Music Group, which includes the popular Last.fm Internet
radio service. Last.fm’s social-networking model revolves largely
around this idea. “The way in which you experience music by sharing, by
storytelling, being part of a community. Last.fm is built on what is
organic to music.”
Ali Partovi, the C.E.O. of iLike, makes a related point. Used as an application on Facebook
and similar sites, iLike bills itself as a “social music-discovery
service” and claims more than 50 million registered users. There’s a
huge difference, Partovi argues, between “this computer thinks you’ll
like this song” and “your friend thinks you’ll like this song — even if
it’s the same song.” The problem with a computer reading waveforms is
that it “has no common sense,” summarizes Mike McCready, a founder of a
company called Music Xray, a digital-music business for entertainment
companies and artists. “It doesn’t take into consideration whether the
artist is just starting out or they’re at the pinnacle of their career,
it doesn’t take into consideration what they wore to the Grammys or who they’re dating or what they look like or what their age is. You have to factor all of this stuff in.”
And why is that? Surely no one consciously says, “My cool friends
like the new Jack White, so I’ll memorize the lyrics and pretend to
like it, too, for sociocultural reasons.” Yet the research about how
listeners link musical taste (at least at a genre level) and identity
is extensive. Surely that’s one reason so much of digital music culture
is devoted to opportunities to “share” your taste: the endless options
for posting playlists, recommending songs, displaying what you are
listening to now, announcing your favorite artists.
Maybe the more vivid illustration of social influence on listening
habits isn’t in what we share but in what we obfuscate. Last.fm, for
example, publishes a chart listing the songs that its users most
frequently delete from their public listening-stream data. The guilty
pleasure Top 10 is dominated by the most radio-ready pop artists — Katy
Perry’s “I Kissed a Girl,” several tracks by Lady Gaga. The service
iLike compiles similar data on the most “suppressed” songs its users
listen to in secret; Britney Spears figures prominently. Apparently
even listeners who can set aside certain cultural information long
enough to enjoy something uncool would just as soon their friends
didn’t know. Maybe even in our most private listening moments, what our
peers think matters.
Much attention has been focused in the last few years on studying
music-liking at the brain level. Daniel Levitin, a neuroscientist (and
musician) has been one of the high-profile thinkers in this area, by
way of his popular books “This Is Your Brain on Music” and “The World
in Six Songs.” One of his central themes is that pretty much all humans
are wired to enjoy music, and he says he believes musicality is even
important to the evolution of the species.
But when you start talking about individuals, instead of humanity in
general, universals are a lot harder to come by. Much depends on
culture. The emotions expressed in many of those ragas that Pandora’s
experts are presently decoding, for instance, are lost on the typical
Westerner. Just as we’re hard-wired to learn a language, but not to
speak English or French, our specific musical understanding, and thus
taste, depends on context. If a piece of music sounds dissonant to you,
it probably has to do with what sort of music you were exposed to
growing up, because you were probably an “expert listener” in your
culture’s music by about age 6, Levitin writes.
The cliché that our musical tastes are generally refined in our
teens and solidify by our early 20s seems largely to be true. For
better or worse, peers frequently have a lot to do with that. Levitin
recalled to me having moved at age 14 and falling in with a new set of
friends who listened to music he hadn’t heard before. “The reason I
like Queen — and I love Queen — is that I was introduced to Queen by my
social group,” he says. He’s not saying that the intrinsic qualities of
the music are irrelevant, and he says Pandora has done some very clever
and impressive things in its approach. But part of what we like is, in
fact, based on cultural information. “To some degree we might say that
personality characteristics are associated with, or predictive of, the
kind of music that people like,” he has written. “But to a large degree
it is determined by more or less chance factors: where you went to
school, who you hung out with, what music they happened to be listening
to.”
Pandora’s approach to listening violates at
least three pieces of conventional digital-music wisdom: it rejects the
supremacy of social-data taste communities; it shrugs off the
assumption that contemporary listeners must have instant on-demand
access to any single song; and, most striking, it rejects what many
observers see as a given, which is that music consumers are
fundamentally motivated by access to the most massive pool of songs
possible. Slacker.com, a
rival Internet-radio service, says its library contains about 2.5
million songs. Spotify, the European music streaming service, expected
to be available in the U.S. by early next year, is generating enormous
buzz because of it offers free, on-demand access to more than 5 million
tunes.
Pandora’s 700,000-song library sounds puny by comparison. And yet
the service has millions of devoted listeners. Why? One answer,
perhaps, involves the ways that the genome, quietly, doesn’t
really screen out sociocultural information. For instance, its
algorithms are tweaked by genre, and the inclusion of genes for
“influence” (“swing” or “gospel,” for example) brings in factors that
aren’t strictly about sound. And Pandora’s algorithm does adjust if,
for instance, users routinely thumbs-down a particular song under
similar circumstances, meaning the genome’s acoustic judgment can at
times be trumped by crowd taste. But the biggest cultural decision of
all may be the one that also happens to guide Westergren’s response to
the issue of scale: how, exactly, does a given piece of music get into
Pandora’s system anyway?
Pandora claims to add about 10,000 songs a month to its library. The
“curation” of Pandora, in effect, falls to Michael Zapruder, another
musician who has found himself working for a tech company. Zapruder
ended up as Pandora’s curator because he had a habit of identifying
holes in the service’s collection. Eventually he was told to fill all
the gaps he could. “I had a field day,” he recalls; he’d stroll through
record stores, buying every single Johnny Cash
CD or every tango disc available, plus anything that looked
interesting. He paid attention to users’ suggestions. Somebody wrote in
to say that Pandora needed to improve its jazz-trombone selection;
somebody else complained about the dearth of barbershop-quartet music.
He took care of it. He has beefed up the Latin-music and the J-pop
catalog. The major acquisition project right now is Afrobeat, because
by far the biggest failed search is Fela Kuti. Zapruder is in the midst
of this research but knows that as this new batch of music comes
online, “we’re going to get educated by our listeners.”
Every Tuesday he looks at the New Music Tipsheet, which lists a few
hundred new tracks in a typical week. He scrutinizes the Billboard and
CMJ charts. He hears directly from a wide array of distributors, from
indie-focused Revolver Records to big shots like Universal Music Group.
In addition to what is simply sent to Pandora (by labels, artists, P.R.
firms), the company buys hundreds of CDs a month, as well as
electronica and hip-hop downloads, acquired from sites like Beatport.
Every month, hundreds of bands send songs, and Zapruder does his best
to get onto Pandora what he figures his listeners want to hear. Still,
the labor-intensive genome simply can’t absorb it all.
Westergren maintains that catalog size receded as a problem at
around the 300,000-song mark. Since passing that, he says, the number
of “missed” searches has declined markedly, so the great majority of
people who come to the site and type in an artist or song name get a
proper introduction to the Pandora system. But the more surprising part
of Westergren’s response is his claim that he isn’t worried about
compiling the biggest possible catalog. “This may seem
counterintuitive,” he told me, “but we struggle more with making sure
we’re adding really good stuff.” That sounds like a rather subjective,
cultural judgment — shouldn’t the listener decide what’s good, based
purely on the genome’s intrinsics-of-music guidance? Well, there’s no
question that Westergren is a champion of the unheard music that gets
marginalized by sociocultural judgments. But even he has standards.
Rob Walker writes the Consumed
column for the magazine and is the author of “Buying In: The Secret
Dialogue Between What We Buy and Who We Are.”
SuperFreakonomics
offers much the same range and amiability, but is more polished. The
book’s chapters cover prostitution; data analysis in healthcare and
counter-terrorism; altruism; innovation; and geo-engineering. The
reader may not guess the central topic from the chapter titles or the
opening pages, however, which betray a fondness for springing surprises
and putting twists in the storytelling.
The
website’s profile rose dramatically as he accurately predicted
Democratic primary elections in state after state. When it came to the
presidential poll, he predicted the vote of 49 out of 50 states, and
was the first to declare Obama’s victory, beating professional
pollsters. 
Just
about every human decision about the future is tainted by a gap — the
difference between what we think we know and what we actually know. The
more expert we are, the wider the gap is likely to be. The story below,
an excerpt from the book Dance with Chance, is a classic
example of an expert-busting enterprise. The experts in this case are
highly sophisticated statisticians and professors who are using
up-to-date models. Most of us assume that sophistication helps us
understand the future. But in fact, sophistication makes things worse;
it invites misplaced focus on the complicated. In the end, if we expect
to make better decisions, we would do well to internalize the lesson in
this passage. That doesn’t mean relying on common sense. It might mean
looking for experts who understand the limits of what they know and the
relative value of simpler methods.
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