The Shape of Stories

Part I — The idea

One line per story

In 1985 Kurt Vonnegut walked to a chalkboard and drew story after story as a single line — good fortune up, ill fortune down, beginning to end. His point: the specifics vanish and a handful of simple shapes remain. His joke with teeth: since the shapes are so simple, “there is no reason why they can’t be fed into computers.” Thirty years later, a team at the University of Vermont did exactly that.

Part II — The shapes

Eight shapes cover everything here

Six were confirmed by the Vermont team across 1,327 novels — three basic moves, each also possible upside down. The seventh is Vonnegut’s own exception: the line that refuses to commit. The eighth is a braid — stories that genuinely reverse again and again; not a new basic shape, but a weave of the six. Counts are from this atlas’s 114 stories.

Part III — The machine

How a computer read 1,327 books it couldn’t understand

A computer can’t follow a plot. But ten thousand common words have been rated for happiness by human readers — “laughter” scores 8.5 out of 9, “terrorist” 1.3. Take a moving chunk of a book, average the ratings of its words, slide forward, average again: the line below draws itself from vocabulary alone. Watch the window slide:

The fine print that matters: this line hears the mood of the words, not the luck of the hero. Most of the time they travel together. When they don’t — and we tested this — the word-line can be flat, or even upside down. That test is Part V.

Part IV — The atlas

Ninety-two Western stories, one line each

Every line answers one question — how well are things going for the protagonist? — and every line was scored the same way: an AI agent judged the story’s events beat by beat. For a few works the agent read the entire text; for the rest it worked from the book’s published plot synopsis, which is open, citable information anyone can check. Each book’s page says which, and links the source. Lines are colored by their shape family. The Eastern classics have a room of their own below.

Part V — Why this method

Why score events instead of counting words?

We took five books and scored them twice: once with the word-mood method, once with an AI agent that read the full text and judged the events. On four of the five, the two lines barely relate — and the failures cluster at endings, where weddings and redemptions are brief in words but enormous in meaning. The word-counter hears A Christmas Carol’s party scenes; it cannot hear that the last page is the whole point. (Thirty-seven books in the atlas carry their word-mood line as a toggle, so you can check the disagreement yourself on any of them.)

the hero’s events, scored by reading (solid, colored by shape) mood of the words (2016 method)

And the shapes themselves? Every reader-scored line still lands inside the same six families. The better instrument corrects which shape a book gets — it found no new shapes. Vonnegut’s alphabet survives.

Part VI — East and West

Twenty-two Eastern classics, same alphabet, different accents

From Gilgamesh — the oldest story we have — through the Sanskrit epics, the Persian romances, China’s four classic novels, Genji, the Heike, Chunhyang and the Tale of Kieu: the same basic shapes appear everywhere. The accents differ. The trapdoor in both Indian epics is a dice game; battlefield victory scores as grief at Kurukshetra; the Ramayana and the Heike keep going past the moment a Western telling would stop the camera — triumph is followed by the wheel turning; and Vietnam’s Tale of Kieu is the truest sawtooth in the whole atlas, catastrophe and rescue over and over.

One caveat we owe the material: several of these traditions compose a sequence of emotions (Sanskrit rasa theory) or a cyclical cosmology rather than one hero’s luck — where an Eastern text looks odd under this lens, part of that is the lens.

Part VII — The map

Every book in shape-space

Left–right: does the story end worse or better than it began? Up–down: is its middle a peak or a pit? The shape families fall out as neighborhoods.

Appendix

Method & sources

How every line was made

Every fortune line in the atlas was scored by an AI agent judging the story’s events against one shared rubric (1 = catastrophic, 5 = ordinary life, 9 = triumphant; prose mood never counts). Lines are drawn as a gently smoothed envelope of those scores — beat scoring exaggerates each event into a full swing — with the raw scored beats shown as small dots on the line. The difference between books is the evidence the agent worked from:

Full text (9 works): the agent read the entire book in equal slices — twenty for novels and epics, sixteen for the two shorter works — scoring each slice as it went.

Published synopsis (105 works): the agent scored beat-by-beat from the book’s public plot synopsis (linked on each book’s page), so every score is checkable against the same source. Event positions along the book are approximate — synopses compress unevenly. Known wrinkles, kept visible: one book (A Man Called Ove) was scored from its faithful film adaptation’s synopsis because the novel’s page lacks one; Into Thin Air’s thin plot section was supplemented with the 1996 Everest disaster article; The Hunger Games was re-scored after a first pass accidentally covered the whole trilogy — the validation gates exist because these things happen.

Word-mood lines (the 2016 method — average happiness of a moving chunk of vocabulary, Reagan et al., EPJ Data Science 2016, labMT lexicon) appear only as toggleable overlays on 37 pre-1923 books, for comparison: they are the book’s soundtrack, not its plot. A scale note: event scores use absolute anchors (1 = catastrophic anywhere in literature, 9 = fairy-tale peak; across all 1,771 scored beats only 2% are 9s), while the 2016 lines have no absolute units — each book is normalized to its own range — so overlays are rescaled to the book’s event range and compare shape only.

Validation of the synopsis method

Before trusting synopsis-scored lines, we tested them against ground truth: nine works that had been scored from their full texts were independently re-scored from synopses alone. Seven of nine reproduce the full-text shape (r = +0.61 to +0.87, median +0.68). The two failures are the two epics — whose synopses summarize a different selection of episodes than the condensed translations the full-text reader used — so epic-scale works are the method’s known weak spot, and the two epics in this atlas display their full-text lines, not synopsis lines. Separately, adversarial audits re-checked 199 beats across 12 random books against their cited synopses: one fabricated event (corrected), no order errors beyond one, and a tail of embellished details beyond the synopsis text (corrected). Treat the lines as careful readings with known error bars — not ground truth.

What we verified ourselves

Re-ran the 2016 decomposition on the corpus: three basic curves ± their mirrors explain ~84% of all arc variance. The six shapes are real.
Word-mood vs reader-scored fortune on 5 books: near-zero agreement on 4 (r = −0.14…+0.16); Dorian Gray the exception (r = +0.90). Failures concentrate at endings.
No new shapes in 9 reader-scored stories, Western or Indian.
The “Ramayana” in one common anthology is Book One only — always check a text covers the full arc before trusting its line.

Sources

Kurt Vonnegut, “The Shapes of Stories” lecture (1985); rejected master’s thesis, U. Chicago
Reagan et al. 2016 · EPJ Data Science 5:31 · github.com/andyreagan/core-stories
Dodds et al. 2011 (labMT lexicon)
Boyd, Blackburn & Pennebaker 2020 · Science Advances (narrative structure beyond sentiment)
R. C. Dutt’s condensed Ramayana & Mahabharata (1898–99); Ryder’s Shakuntala; Arnold’s Nala and Damayanti
Campbell, The Hero with a Thousand Faces — his monomyth, traced as fortune, is the valley shape