3000 years of literary brain hacks
This is a review of Wonderworks: literary invention and the science of stories – an unusual book that has attracted a lot of attention in literary circles and no small amount of controversy.
The book’s author Angus Fletcher is a professor of ‘story science’ at Ohio State University with dual degrees in literature and neuroscience. The combination may seem strange but if you read the book, you’ll quickly grasp the logic.
For Fletcher, stories are technology. They are tools developed by writers that do interesting things to human brains. Lightening sorrow, banishing loneliness, diminishing anxiety, treating the symptoms of trauma, bringing hope, heightening joy, stirring love, ushering in tranquillity ‘and so on and so on’. And although Fletcher concedes they are also literature, it’s stories-as-technology and their impact on brains that interest him most.
He starts by throwing the net a long way back. All the back way to Ur, on the banks of the Euphrates in the year 2300 BCE, where the king’s daughter, Enheduanna, collected an anthology of her poetry and became the world’s first (self) declared author: ‘I, Enheduanna, created this booklet – a thing which no one else had ever created.’
The book proceeds through a series of 25 case studies, ranging from Homer to Elena Ferrante. In each of these he follows a method: ‘Step One. Identify the unique psychological effect of a literary work… Step Two. Trace that unique effect back to an invention crafted from an original use of plot, character, storyworld, narrator, or some other element of narrative.’
Each case study brings forth a new technology. Thus, Homer’s Iliad and the invention of the Almighty Heart; Sophocles’ Oedipus Tyrannus and the Hurt Delay; Dante’s Inferno and the Vigilance Trigger; Mary Shelley’s Frankenstein and the Stress Transformer; Virginia Woolf and the Riverbank of Consciousness.
Fletcher leans heavily into contemporary neuroscience, giving chapter and verse of recent research – and this is perhaps the element that will hold most interest for literary readers.
Take for example the story of Giovanni Straparola, a down-at-heels writer who in 1533 composed a fairy tale called Adamantina and the Doll. Adamantina is the anti-Cinderella, a poor girl who through blind luck – no merit at all – gets to marry the king. This is the ‘Fairy-tale Twist’, a reminder to our pessimistic brains that good things can happen out of the blue!
Why is this a great story technology? Because it turns out stories about unearned rewards fire up the brain’s left hemisphere – the optimistic half that counters the pessimistic right hemisphere (the part that keeps you awake at night, catastrophising).
Fletcher follows the story all the way down to Charlie Chaplin and his film The Gold Rush, another story of ‘random serendipity’ featuring the hapless Tramp. Interestingly, he notes Chaplin’s film was out of step with the Motion Picture Production Code, which specified that characters in a film should always get their just deserts. The Code, thankfully, has been abandoned.
Not every case study is as satisfying. Fletcher’s treatment of Shakespeare’s Hamlet – the ‘Sorrow Resolver’ – is unconvincing, with big leaps and small misreadings that few Shakespeare scholars would credit. But Fletcher is a man with a hammer and everything looks like a nail.
Part of the pleasure of the book is its scope and the many unusual texts it brings to the reader’s attention, such as this small gem of a poem from the Zhou dynasty, painted on bamboo in the Yangtze marshes:
‘After you return from the hunt,
With red-footed geese and wild duck,
We will feast to songs of silk,
And I will wish for life’s long years
To keep me, grey haired,
In your arms.’
At the conclusion of the book, Fletcher gives a genealogy for his method, tracing it back to US academic Ronald Crane, who published an influential college textbook in the 1950s. Crane in turn reached back to Aristotle’s Poetics and the secret teaching of the Sophists. So the book and the method sit, in fact, within a long tradition.
A quick survey shows many reviewers are enthusiastic about Fletcher, the book and the method. They’re intrigued by the coupling of literature and neuroscience, finding it ‘readable, likeable and consistently interesting’ and ‘tech for our times’.
Others, however, view it as ‘techno-reductionism’ and a ‘dispiriting survey’ that ‘reduces centuries of art into self-help videos’. Members of the Compton book club were similarly divided.
Do androids dream of creating art?
Where are the limits of artificial intelligence?
Christie’s is soon to become the first auction house to offer a work of art created by an algorithm: Portrait of Edmond Belamy by an artist whose signature is an equation.
But can an algorithm really create art? It’s a question for philosophers… or perhaps mathematicians.
Hannah Fry, who’s a professor of mathematics at University College London, has applied herself to the question. She analyses three issues in an attempt to resolve it.
First, luck vs talent. If it’s true that luck plays such a large part in the success of an artist, then algorithms are in with a chance. But if talent really matters, writers of algorithms will have to work harder.
Second, familiarity vs novelty. Algorithms do well when they have a lot of data — familiarity — to work with. Novelty is more challenging. So how much novelty does art demand?
Third, the question of beauty. Is it in the eye of the beholder or is it real? If the former, then so much the better for algorithms. More consumer data to crunch.
Fry’s conclusion is that there’s more to art than an algorithm can compass. She writes:
‘True art can’t be created by accident. There are boundaries to the reach of algorithms. Limits to what can be quantified. Among all of the staggeringly impressive, mindboggling things that data and statistics can tell me, how it feels to be human isn’t one of them.’
To read more visit https://aeon.co/essays/can-algorithms-create-true-art-or-do-they-only-imitate
The case for public broadcasting
30-something years ago British economist Sir Alan Peacock made the case for funding the BBC — and for public broadcasting generally — in terms of ‘spectrum scarcity’.
Because electromagnetic spectrum was scarce, the argument went, it mattered who owned and controlled it, and the public interest should prevail.
Peacock’s report was much cited and very influential. But what about now? Spectrum scarcity has been abolished by the internet. A thousand flowers are blooming without a drop of public interest funding.
So how can public broadcasters sustain a case for their continued public funding?
The ABC’s Richard Aedy tackled this topic in a recent edition of The Money: http://www.abc.net.au/radionational/programs/themoney/the-economics-of-public-broadcasting/9786988
I give my two bobs’ worth about 20 minutes in.
The case for intergenerational businesses
Creative professionals should take a close look at how other professionals build their businesses. Think architects, lawyers and accountants. These professions have mastered the practice of intergenerational exchange – by bringing junior partners into the business. In doing so they:
• stay connected to the next generation, the zeitgeist, and emerging new technologies
• bring youthful passion and energy into the business
• recruit and develop the people who one day will lead the business
• secure their own eventual exit from the business.
To find out more, read my recent piece in Screenhub, ‘Building true longevity – the case for an intergenerational screen business’
You may have experienced this — the excitement of a new business venture, the rush of possibilities, working it all out with a new business partner, or perhaps breathing new life into an old partnership.
So how should you share the equity in the new venture?
Caught up in the rush and bloom of partnership, you volunteer: 50/50. It seems only fair. The right thing to do. An earnest of your mutual goodwill and best intentions. To offer anything less would seem, well, deflating.
But then you get into the grind of building the new business. It turns out your new partner can’t commit 100 percent. Perhaps they have another business. Or something comes up. Or maybe it turns out you can’t deliver 100%. That first blush of enthusiasm starts to wear thin.
Fast forward a few months or maybe a year, or even years later. The business is still going. Perhaps it is starting to pay off and profits are being shared out. But one of you is carrying the other, doing most of the work, and shouldering more of the risk. And still having to share 50:50.
Research shows a lot of businesses come to grief in this way. A breakdown of the partnership caused by chronic unfairness — one party carrying an unequal share of the work and risk.
The solution of course is to adjust the equity. Give more shares to the person doing the work and taking the risks. But will your partner agree to redivide the pie? Or will they dispute your analysis, and claim they’re doing more than you think? And how will you prove your case?
There is a better solution. It’s called dynamic equity sharing — basing the allocation of shares on the contributions people make over time. Both money and time. Adjusting continually, from the very first day, whether you have two people or 10. It’s fair, logical, and once you get your head around it, inarguable.
This is a shout out to Mike Moyers’ brilliant ‘slicing pie’ model of dynamic equity allocation. You can look it up here: https://slicingpie.com/
Moyers has written a short book explaining the idea and also created an easy-to-use app. Have a look — and never again agree to an upfront 50:50 split.
What’s so good about growth?
I’ve been running a workshop for an amazing group of New Zealand filmmakers: https://compton.school/the-work/partner-workshops/. The theme of the workshop is growth and I kicked off with six good reasons to grow your business, drawn straight from the textbooks:
1. Economies of scale and scope. Things get easier and cheaper when you do more of them.
2. Specialisation. Individuals can focus on the things they do best.
3. Bargaining power. Size commands respect.
4. Smoothing. Bigness smooths out revenues and risks.
5. Resilience. Team spirit sustains the enterprise.
6. Visibility. You get noticed.
Proving the power of the group, we came up with six more:
7. The information. More eyes and ears.
8. Trust. People place more trust in bigger entities.
9. Accountability. Responsibility to others brings more discipline.
10. Bigger projects. Ambition is enabled.
11. More ideas. More minds, working together.
12. Completion of the team. No gaps.
I’m not sure I swayed everyone but it’s a pretty good list.
Power: given or taken?
Fans of The Godfather and students of Machiavelli know that real power must be taken, not given. It’s a brutal vision of the world, life at the top table, a fierce contest for whatever spoils are judged the most valuable.
Yet most of us play a different game. A closer contest, less fatal, more open-ended. We know that power is something we mostly hold at the pleasure of others.
If you are playing this different, more open-ended game you might be interested in the work of psychology professor Dacher Keltner. His book The Power Paradox sets out the rules of the game in 20 principles. As a taster, here are the first five:
#1 Power is about altering the states of others.
#2 Power is part of every relationship and interaction.
#3 Power is found in everyday actions.
#4 Power comes from empowering others in social networks.
#5 Groups give power to those who advance the greater good.
We’re a long way from Machiavelli and quite some distance from the operating premises of most of the leadership literature. We’re in the everyday world of ordinary people and the interplay of ordinary business, carried on without the sanction of violence.
Of course it’s not all sunshine and light. Keltner’s last 8 principles are all about the downside: incivility, threat, powerlessness, adverse health effects.
But the model I think is helpful, particularly for people working in the creative economy, where persuasion and teamwork are the everyday mode.
Gamekeeper turns poacher
Ex Droga 5 CEO Andrew Essex has written a book called The End Of Advertising. His epiphany and resulting departure from the ad industry – he is now CEO of Tribeca Enterprises – came when he learned about ad blocking and instantly converted. And like any true convert, he fell hard: ‘One day, we’ll look back on the fact that we forced people to watch ads with the same incredulity we reserve for, say, smoking cigarettes or wearing fedoras. Perhaps the most enlightened brands should start thinking about reparations.’ The book is repetitive and longer than it needs to be but definitely worth a look. What can advertisers do now that audiences have the power to zap their ads? Get more creative, Essex says. ‘And by creativity I don’t mean simply making less annoying ads, which are always welcome. The real goals are big ideas that reinvent or fully replace ads.’ As examples he cites The Lego Movie – a hit movie ‘that also happened to be an ad’ – and Citibank’s Citi Bike program in New York City. The book ends with 10 principles for better advertising: in brief, ‘Adapt or Die’.
AI breeds more nimble, fan-driven media
NYC Media Lab has released a white paper about the influence of artificial intelligence on media economics. Some conclusions: 1. Rapid audience feedback means media ‘can take an iterative approach, test more, and improve faster’. 2. ‘Media brands will invest in technology to attract fans, not casual viewers.’ 3. Media employment won’t shrink yet, but production roles will change.’ To download the paper visit –
Wagging the long tail
Chris Anderson excited a lot of interest when he argued back in 2006 that movies, books and songs in the ‘long tail’ – ie at the tail end of the sales charts – could collectively equal or outsell the hit movies, books and songs at the ‘head’ of the charts.
What powered Anderson’s claim was the increasing importance of online sales, where inventory and transaction costs fall away sharply, allowing platforms like Amazon and Netflix to offer people literally millions of titles.
Anderson’s claim has been contested, notably by Harvard’s Anita Alberse, who argues that the internet in fact magnifies the returns to blockbusters:
There’s an excellent discussion of the rival claims in Michael Smith and Rahul Telang’s book Streaming, Sharing, Stealing (see chapter 5 in particular):
Smith and Telang argue that the long tail is a whole new business model quite different to the blockbuster model perfected by the record companies and Hollywood studios:
‘Long tail business models use a very different set of processes to capture value. These processes—on display at Amazon and Netflix—rely on selection (building an integrated platform that allows consumers to access a wide variety of content) and satisfaction (using data, recommendation engines, and peer reviews to help customers sift through the wide selection to discover exactly the sort of products they want to consume when they want to consume them). They replace human curators with a set of technology-enabled processes that let consumers decide which products make it to the front of the line. They can do this because shelf space and promotion capacity are no longer scarce resources.’
Clearly what matters here is technology – the internet itself and the algorithms that help people find what they want. And it is these algorithms, so-called machine learning algorithms or ‘learners’, that are driving the rollout of streaming services like Netflix, Spotify, Amazon and locally, Stan.
Machine learning then is the engine wagging the long tail of audience choice and the emergence of ‘niche’ blockbusters like House of Cards, Top of the Lake and Wolf Creek.
Machine learning expert Pedro Domingos sums it up:
‘In retrospect, we can see that the progression from computers to the Internet to machine learning was inevitable: computers enable the Internet, which creates a flood of data and the problem of limitless choice; and machine learning uses the flood of data to help solve the limitless choice problem. The Internet by itself is not enough to move demand from ‘one size fits all’ to the long tail of infinite variety. Netflix may have one hundred thousand DVD titles in stock, but if customers don’t know how to find the ones they like, they will default to choosing the hits. It’s only when Netflix has a learning algorithm to figure out your tastes and recommend DVDs that the long tail really takes off.’
To learn more about machine learning, have a look at Domingos’ book:
Does character matter in business? A recent study says yes. Companies run by CEOs who rate well on measures of integrity, responsibility, forgiveness and compassion outperform companies whose CEOs don’t. The study authors call them ‘virtuoso CEOs’. It’s a striking finding.
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