I went on a queer wiki walk and was astonished that some people are apparently capable of quantifying the exact percentage of masculinity/femininity in their gender. Like a deaf man who reads about Superman's hearing. It sounds absurd.
The seductive, science fictional power of spreadsheets
Tomorrow (Apr 30) at 2PM, Iâll be at the San Francisco Public Library with my new book, Red Team Blues, hosted by Annalee Newitz.
This week, John Scalzi was kind enough to let me write a guest-editorial for his Whatever blog about the themes in my new crime technothriller, Red Team Blues; specifically, about the ways that spreadsheets embody the power and the pitfalls of science fiction at its best and worst:
If youâd like an essay-formatted version of this post to read or share, hereâs a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:
Yes, spreadsheets. Marty Hench (the protagonist of Red Team Blues) is a 67-year-old forensic accountant who specializes in unwinding Silicon Valley financial frauds, a field he basically invented 40 years ago, when, as a PC-struck MIT dropout, he moved from Cambridge to San Francisco to recover the stolen millions hidden in spreadsheets.
Working through this bookâââand its two sequels, which travel back in time to the 1980s and Martyâs first encounters with VisiCalc and Lotus 1â2â3âââI was struck by the similarities between spreadsheets and science fiction.
While many people use spreadsheets as an overgrown calculator, adding up long columns of numbers, the rise and rise of spreadsheets comes from their use in modeling. Using a spreadsheet, a complex process can be expressed as a series of mathematical operations: we put these inputs into the factory and we get these finished goods. Once the model is built, we can easily test out contrafactuals: what if I add a third shift? What if I bargain harder for discounts on a key component? If I give my workers a productivity-increasing raise, will the profits make up for the costs?
These are the questions that anyone managing a complex system asks themselves all the time. Historically, the answers have sprung from intuition, from fingerspitzengefĂŒhlâââthe âfingertip feelingâ of how a systemâs components work and what their potential and limitations are. But intuition can calcify, become a rigid set of rules that increasingly diverge from the best strategy.
By contrast, spreadsheets yield a set of crisp, instantly tallied answers to any question you put to them. Change the input and watch as that change ripples through the whole system in an eyeblink. If youâre adding three more people to your camping trip, will the amount of additional water require renting another vehicle? No need to guess: just check and see.
This has a lot in common with science fiction, a genre full of thought experiments that ask Heinleinâs famous three questions:
What if?
If only, and
If this goes onâŠ
These contrafactuals are incredibly useful and important. As critical tools, science fictionâs parables about the future are the best chance we have for resisting the inevitabilism that insists that technology must be used in a certain way, or must exist at all. Science fiction doesnât just interrogate what the gadget does, but who it does it for and who it does it to:
One of science fictionâs key methods comes from sf grandmaster Theodore Sturgeon: âask the next question.â Ask a question, then ask âwhat happens next?â Do it again, and again, and again:
This technique produces excellent, critical ways of interrogating technological narrativesâââcheck out this delightful example of the possible pipeline from self-driving cars to ransomware gangs to mutual aid societies to the reinvention of the train:
The commonalities between sf and spreadsheets donât stop thereâââsf and spreadsheets share pitfalls, too. A spreadsheet is a model and a model is not the thing it models. The map is not the territory. Every time a messy, real-world process is converted to a crisp, mathematical operation, some important qualitative element is lost.
Modeling is an intrinsically lossy operation. Thatâs why âall models are wrong, but some models are useful.â There is no process so simple that it can be losslessly converted to a model. Even the actions of the nanoscale transistors in a microchip, which toggle between â0â and â1,â are rarely in a state of âno voltageâ and âvoltage.â That clean, square-wave line thatâs used to describe what happens in a chip is a lieâââthat is to say, it is a model.
The wave isnât square, itâs a squiggly line that hovers around zero and around one. Under normal circumstances, âzeroâ and âzero-ishâ is a distinction without a difference. But when computers go wrong, itâs sometimes because a sufficiently ambiguous âzero-ishâ acts like a âone.â Thatâs true all the way up the stack. On engineering diagrams, the nanoscale lines that electrons travel along inside a chip are represented as sharp paths, the kind of thing a Tron-cycle would lay down. But in the real world, we get all kinds of weird effects at that scaleâââelectrons sometimes tunnel through those lines, performing a spooky quantum trick that reminds us that Newtononian physics are also just a model.
Every real-world phenomenon contains qualitative and quantitative elements, but computers can only do math on the quantitative parts. This creates a powerful temptation to incinerate the qualitative and perform operations on whatever dubious quantitative residue is left in the crucible, often with disastrous results.
Remember during lockdown, when a pair of University of Illinois at Urbana-Champaign physicists produced a model of covid spread that predicted that the campus could safely reopen, predicting no more than 500 cases over the entire semester and no more than 100 cases at any one time? The physicists were openly contemptuous of their epidemiologist peers, saying that this kind of model making lacked the âintellectual thrillâ of real science.
UI was so swayed by the crisp, precise model that they invited students back to campusâââonly to shut down again in a matter of weeks, with 780 active cases on campus and more rolling in every day.
The model reduced qualitative factorsâââlike the propensity of undergrads to get drunk, take off their masks, and lick each othersâ eyeballsâââto a quantitative probability, using the highly precise, scientific technique of taking a wild-ass guess. That guess was wrong. The campus reopening was a super-spreader event.
Any model runs the risk of hiding the irreducible complexity of qualitative factors behind a formula, turning uncertainty into certainty and humility into arrogance.
Think of how we replaced contact tracing with exposure notification. Contact tracing has a qualitative foundation: public health workers establish rapport with infected people, win their trust, and get them to fully enumerate the places theyâve been and the activities they participated in.
By contrast, exposure notification measures whether two Bluetooth radios were within range of each other for a predetermined interval. It substitutes signal strength for a personâs own understanding of their experience. Now, people can be wrong about their own experienceâââwe lose track of time, we misremember emotionally charged events, and so onâââbut that doesnât mean we can substitute Bluetooth measurements for personal experience.
Thatâs why, despite all the clever privacy-preserving math and interesting analysis, exposure notification was a bust, something between a distraction and a false-confidence-generating disaster. Contact tracing ended the 2014 ebola outbreak. Exposure notification just wasted a lot of time:
Itâs just too easy to forget which parts of a model are based on guesses and which parts are based on ground truth. And even if you can keep track of those differences, itâs even harder to re-check the modelâs ground truth to determine whether the underlying factors have changed. Thatâs how we got into so much trouble with collateralized debt obligations, which were supposed to be ârisk-freeâ mortgage derivatives that could be safely insured and invested in.
The formulas behind CDO hedging were designed by some of the worldâs smartest mathematicians and physicists, who simply assumed that market actorsâââfrom loan-originating bank officers to insurance underwritersâââwould act in reliable, predictable ways. They were so very wrong that they brought the world economy to the brink of ruin:
https://www.wired.com/2009/02/wp-quant/
This is also science fictionâs failure-mode: any science fictional âask-the-next-questionâ exercise represents a series of guesses or speculations or maybe possibilitiesâââbut when you combine that guesswork with the deceptive certainty that comes from inhabiting a cracking story, itâs easy to mistake âguessingâ for âprediction.â
Prediction is hard, especially about the future. The assumptions that go into a prediction are always incomplete, not least because human beings have free will and agency and can change the circumstances that go into the assumptions. The very best science fiction embodies this principle. Iâm thinking here of the likes of Ada Palmer, an historian and sf writer whose deep historical knowledge informs her sf and her pedagogy at the University of Chicago:
Palmer is famousâââeven notoriousâââfor her annual four-week undergraduate LARP in which students re-enact the election of the Medicisâ Pope. Itâs four weeks of alliances, betrayal and skullduggery by the students, each of whom is enacting the agenda of a real-world Cardinal or other power-broker.
The final investiture is done in full costume at the universityâs massive faux-gothic cathedral, and going into that climax, of the four candidates, two are always the same, because the great forces of history are bearing down on that moment to ensure that the champions of the two dominant power-blocs are in the running. But the other two? Theyâre never the sameâââbecause the agency of the actors jockeying for power change the outcome, every single time, in absolutely unpredictable ways.
Like any other model, sf is wrong, but sometimes useful. Thinking about jetpacks and flying cars is âusefulâ insofar as it gets us to interrogate how we think about cities, about mobility, about privilege and geography. But itâs not a prediction. Worse, the endless tales in which flying cars are presented a fait accompli is a gift to grifters raising money for the objectively stupid idea of flying cars. After all, we all know flying cars are inevitable, so itâs basically a risk-free investment, right? With flying cars just around the corner, wouldnât it be irresponsible to build a city with mass-transit instead of helipads?
Thereâs a whole range of thought-experiments that got transformed into predictions and then certainties: self-driving cars, âgeneral artificial intelligence,â infinite life-extension, space colonization, faster-than-light travel, cryptocurrency, etc etc.
Spreadsheets donât just lead their users astrayâââthey also trick their creators. The very same people who transform wild-assed guesses about hairy, unknowable outcomes into neat mathematical relationships are perfectly capable of acting as if those relationships are based on fact, rather than supposition. The Great Financial Crisis wasnât just about people who didnât understand the uncertainty in the hedging algorithm going all-inâââthe people who made those models were also fooled by them.
Itâs very easy to get high on your own supply. Iâll never forget the sf convention panel I was on with Robert Silverberg about sfâs supposed predictive value, where the subject of Robert A Heinlein came up, and Silverberg sniffed, and, in that trademark bone-dry way of his, said, âAh yes, âRobert A Timeline.ââ
Sf isnât just full of writers who mistake their suppositions for predictionsâââthe canon is full of tales in which brilliant people can and do predict the future, with near-perfection. Think of Hari Seldon, the hero of Asimovâs Foundation series, who is able to forecast the future several millennia out. Or Heinleinâs first-ever story, âLife-Line,â in which a genius inventor destroys the insurance industry by creating a computer that can predict your exact date of death using statistical methods.
Thereâs something wild about this phenomenon, in which writers make stuff up and then assume that anything that cool must also be accurate. One tantalizing explanation for this comes from EL Doctorowâs (no relation) essay âGenesis,â from his 2007 collection âThe Creationistsâ:
Doctorow tells the history of the Genesis story, which the Hebrews plagiarized from the Babylonians. In Doctorowâs telling, the Babylonian mystics who made up the Genesis story assumed that it had to be true, because they considered themselves to be nowhere near imaginative enough to have come up with something as great as Genesis. An idea that amazing had to be divinely inspired.
I like this because itâs a story of being led astray by humility, rather than hubris.
Imaginative exercisesâââwhether or not they are assisted by mathematical models and self-updating digital spreadsheetsâââare powerful tools for thinking about the future we want, and to guide our attempts to make that future come true. All models are wrong but some models are useful, of course!
Iâm on tour with Red Team Blues right nowâââIâm writing this post while waiting for my flight to San Francisco, where Iâm appearing at the public library with Annalee Newitz tomorrow (4/30) at 2PM:
One especially fun stop on this tour will be on May 5, at the Books, Inc in Mountain View, where Iâll be talking about the book with Mitch Kapor, the creator of Lotus 1â2â3, who knows a thing or two about spreadsheets:
The tour is bringing me to Berkeley, Vancouver, Calgary, DC, Gaithersburg, Toronto, PDX, Nottingham, Hay, London, Manchester, Edinburgh and BerlinâââI hope to see you!
Catch me on tour with Red Team Blues in Mountain View, Berkeley, San Francisco, Portland, Vancouver, Calgary, Toronto, DC, Gaithersburg, Oxford, Hay, Manchester, Nottingham, London, and Berlin!
[Image ID: A Lotus 1-2-3 spreadsheet with green-on-black, low-res type; its center has an irregular vignette revealing a space station.]
Quantization is the process of correcting, or shifting, imprecise musical notes and beats to underlying musical representation or grid. To preserve more of natural human timing nuances, percentage of quantization can be applied to in many sequencers or DAWs.
While swing, in short, means a method of transforming straight grooves, by timing of notes, to shuffled patterns. And when it comes to swing, the MPC sampler series has an iconic status for its groovy musical timing. Its influence on electronic and hip hop music cannot be denied.
The MPC's creator, Roger Linn, has claimed that he stumbled upon note quantizing and swing by accident when developing the Linn LM-1 drum computer: by only permitting 16th notes using 1 byte per 16th note, the sequencer program was correcting played timing errors, hence quantization. And by delaying the playback of alternate 16th notes, and by varying the amount of delay, the swing/shuffle feature was invented.
Linn's implementation of swing applied to quantized 16th-note beats is merely delaying the second 16th note within each 8th note, or all the even-numbered 16th notes within the beat (2, 4, 6, 8, etc.)
Swing amount is the ratio of time duration between the first and second 16th notes within each 8th note. 50% is means both 16th notes within each 8th note are given equal timing, in other words no swing. 66% sets perfect triplet swing. Most useful swing increments are between 50% and around 75%. 62% will feel looser than at a perfect swing setting of 66%, while 54% will loosen up the feel without it sounding like swing, according to Linn.
Compare GGUF, GPTQ, and AWQ quantization formats for LLMs on consumer GPUs. Learn how to balance model quality, speed, and memory usage with Q4_K_M, IQ4_XS, and Q3_K_S variants for optimal inference performance.
