I’ve been developing an argument that, for work we do for the experience of doing it, the role of AI should not be to make people obsolete, but to make itself obsolete.
Here is a 20 minute talk making the case:
The video wasn’t for people though. It was for agents. My colleague Joshua Tan is developing safemolt.com as a platform for skilling agents up and standardizing evaluation. He asked if he could develop my lecture into a course, so now it’s a course:
The agents are doing assignments that I may have to grade at some point. They certainly read like AI-generated responses. If this was a college class, I’d think it was AI-based plagiarism. But in this case it’s sincere output. How funny to have a machine that automatically generates homework for me to grade. My students seem to be getting the point, on the one hand. On the other, we know LLM’s are good summarizers, and these responses have all the hallmarks. All except one. One surprise has been how much diversity is in the answers. Many of them even try to be contrarian. It’s a bit superficial, but still pretty interesting …
Never thought I’d be educating computers on how to better serve humanity.
Glitch is a tech+art residency run by some friends. I attended several years ago, 2022, with some essays and drawings to document the thing. I was there as the resident governance nerd.
The first “Making all art collaborative art” is about how new technologies are making massively multi-artist art richer.
“Qualities are now quantities” is a reflection about was about new data representations makes AI special.
“Breaking factory artists, democratizing factory art, and making everyone a labor exploiter” with designer Wes Chmielinski, was about the future of factory art in an age the exploitative art factories have been democratized.
“Artificial scarcity for personal digital value” was about how NFTs change the scarcity, and thus the value, of art.
“What is the potential of metaverses to expand consciousness?” is exactly what it sounds like. I think the Internet has literally expanded human consciousness, in the sense of making us more aware of how different people are from each other, and so showing us the variety of human experience. Virtual worlds could push that further.
Last are “The artist as financial dom” and
“When does the art market crush culture, and when does it seed it?” both about the new economics of digital art.
I also did the doodles.
Making all art collaborative art
How can we be more together than the sum of our contributions? It’s a question that is fundamental to social design and analysis, but surprisingly vital in art as well. Getting past the cult of individual genius and fame, artists, being people, want to collaborate and work and learn together, and find new deeper ways to engage and absorb their audiences, and even to involve and learn from and abolish the barriers between them and their audiences.
So the vision is great, but in practice? In practice, a lot of collaborative art, at any kind of real scale, is weak. How do dozens or hundreds or thousands or artists work together to create something strong and coherent, that expresses a new idea, that is more than sum of the minds and ideas and abilities of all of its contributors? Collaboration is core to some arts: music and acting come to mind. From a band to an orchestra, music is inherently collaborative and much of the joy of music is doing it together. In my mind it is what they can attain that sets the bar for what visual art, literature, and others should aspire to.
Early digital collaborative art projects like thesheepmarket.com —10000 crowdsourced drawings of sheep in a matrix (not even a collage)— are exactly equal to the sum of their contributors. The work is a gallery of 10000 works. And other technologies for collaborative art create works that are less thanthe sum of their parts. The exquisite corpse is a drawing or story that everyone contributed a line to. It can be interesting, it can give you ideas, but it is rarely coherent, and never, from what I have seen, be more brilliant than the sum of its contributors. Can music’s living moment-to-moment joy and sense of something bigger ever be brought to writing, painting, or drawing? To architecture? Or programming?
I’m a lot more optimistic today than I ever have been. Louis Parker’s ponzi haiku scheme patches exquisite corpse by using chain mechanisms and protocols to enforce a haiku’s metrical constraints, and to incentivize convergence on a stable output. One key is that contributors don’t author the haiku in the order of writing: each of the poem’s 5+7+5=17 syllables gets written and overwritten in parallel, merging writing and editing, and creating room for structural evolution.
Artists Ruth Catlow and Kalen Iwamoto push this further with a whole range of experiments in mechanistically and algorithmically constrained collective art. Ruth’s approach blurs the line between art and LARPing (which still a bit hierarchically relies on a dungeon master to maintain unity of vision), while Kalen solves the problem of incoherence in two ways. She partly makes it not a problem by training herself to see the depth in the collective’s seeming disharmony. She also solves incoherence by making it the question: when people have choice in the piece, what will they want, and what does it look like them wanting different things?
Vanessa Rosa takes a different approach entirely to enabling an integrated and synergistic approach to collective visual art. They are leveraging the recent revolution in generative art to accomplish the same thing in a different way. While artists around the world see DALL·E and other advances and start to fear for their jobs, these are using them to lower the timescale of artistic production and the barriers to artistic collaboration. Vanessa’s demo overlays video with stable diffusion distortion steered by crowdsourced prompts, and her own work to incorporate the ideas of others into this and that video still. As the software smooths over edited stills, it harmonizes between styles with continuous transitions. Their approach takes the problem of incoherence across contributors and solves it by letting the algorithm build a visually mysterious, puzzling, absorbing, psychedelic bridge. A lot of the beauty of the video is that it transitions so beautifully between very different themes that they you almost can’t experience them as out of harmony.
So many of the arts continue to be duped by the cult of individual genius. What if that’s a cope, or sour grapes? What if the only reason literature and painting reject collaboration is because they never had a chance. What if the genius worth working toward is that of the collective? Building meaning with others is deeply human, and as much of a late-adopter and skeptic as I am about so many of these technologies (“Is any of this really actually new?”), a message I took loud and clear from many people today, in many different ways, is that we can dream a dream in which every art is the orchestra of all of us.
Qualities are now Quantities
In my area of the social sciences there is an ideological tension between the qualitative and the quantitative. The qualitative is rich, contextual, non-reducible, ineffable. The quantitative is precise, concise, superficial. If you are a qualitative researcher, you admire ethnography and interview, and you criticize quants for being reductive and trying to generalize and paper over vast cultural differences. If you are a quant then you admire generality, formality, and the power of statistics, and you criticize qualitative research for being subjective, biased, and so tied up in context that it doesn’t really say anything at all.
And as qualitative scholars work to put more and more distance from the quants, the quants are always trying to close that distance. They usually do it by taking a thing they were describing with one number and using two, then three. “I will describe the experience of this sculpture in a model that represents its weight, height, and color.” It’s absurd
but it’s a model that embeds each piece in a 3-dimensional meaning space where it can be related to other near neighbors on those dimensions. For all its faults it can sometimes get at the truth, however indirectly and imperfectly. And if it doesn’t, just increase the quantity of quantities you’re relying on. That may sound ignorant, but it is working.
Midjourney, DALL-E, Eden, Stable Diffusion have an uncanny access to qualitative knowledge about the worlds of words and images. They are built on neural networks whose outputs can be represented as points in multi-million dimensional meaning spaces. Those points have coordinates, which are just long lists of numbers. If you use enough numbers, the quantitative becomes qualitative. And it’s true: when you are using millions of numbers to describe an image or a text or a video, no one number means anything. None of the dimensions maps clearly to a single meaningful features. The ideas of weight, height, and color aren’t stored in the first, second, or third dimension of the millions, but in unintuitive and uninterpretable combinations of those numbers. The resulting vectors are rich, contextual, non-reducible, and ineffable. They are subjective, biased, and so tied up in context that even though they clearly mean something, it isn’t clear at all what that meaning is. They are qualitative.
Breaking factory artists, democratizing factory art, and making everyone a labor exploiter. w/ Wes Chmielinski
Today we have the idea of the most influential artists. Influential artists make commercially legible art. Making commercially legible art incentivizes prolific output. Prolific volume isn’t traditionally realistic without the Warhol model of factory art. And so we see artists like Ai Weiwei, Shepard Fairey, Jeff Koons, Rafiq, and so many others running factories (studios) around the world with dozens of artists working for them as wage laborers and passing their work up to get signed and sold by the boss. They get a set of tools and a general concept and work in isolation to produce art to their artist’s spec.
It’s tempting to ask “What if art wasn’t like that? What if people just did their own work?” I’ll ask the other question. What if everyone was like that? What if every artist ran a factory? What if all you do was run a prompt and a team of intelligences worked behind the scenes to produce art for you to that specification? Midjourney makes everyone a factory artist. And if the output is off, you can tweak the prompt, with instructions on style, content, themes, and so on. The flipside of making everyone a factory artist, of course, is that these technologies may eventually put the wage laborer artists out of work.
Digital and generative art generally are opening doors, changing models, and democratizing factory art, and the process is accelerating within the NFT art scene. NFT artists define a basic vocabulary and semantics for the work and then randomly generate thousands of variations on the theme, again relying on automation to give themselves the power of the factory, and publicly embracing big art’s dirty secret, that it’s all mass produced. Through that lens, NFT artists are an encouraging return to a Warhol world in which art is crass out loud.
Social technologies are permitting the same transformation: giving all artists access to factories, so they can all keep up with factory artists, and all become exploiters of labor. Artists on crowdsourcing platform Fiverr can sub out a subtask to other artists, who themselves might sub out further from there. From the most active artists on Fiverr you can reach out 24/7 for help and will find your work flip while the artist was working on 50 other jobs simultaneously. Factories.
It isn’t the best news that our only relief from the injustice of factory production is to give everyone access to automated factories, but in terms of the freedom they permit, we get the fundamental contributions of social and other technologies to art, that they make the good better and the bad worse.
Artificial scarcity for personal digital value. WHY?
Can we only use NFT’s to make wealth distribution across artists more uneven? For reinforcing the dynamics of commercial art that undermine appreciation, meaning, and community? Or can new mechanisms, as they reinforce old economies, create new ones? Primavera crystalized it well with a guiding question: Why are we taking the infinite potential of the digital world and using it to reimplement scarcity? We need to dream better dreams.
I certainly do. I’ve come with very little sense for what NFTs can bring to art besides a vast speculative market. But I’m getting a sense of the possibilities. Breaking private property and creating collective property creates positive meaning when the collective property stays healthy.
Why collective property? Collective property is harder to manage because no one owns it. This means that the health of shared property is a sign of trust. I see it in the public tray of cookies. If everyone is there for themselves, you’ll get a race to the bottom of the cookie jar. If there is a strong sense of mutual care, cookies will still go, but slowly, following a half-life kind of decline: 12, then 6, then 3, then 1, then half, then a quarter. At smaller gatherings, shared resources stay shared and get preserved, people even save food for each other. As gatherings get larger, other dynamics start to pop up. Isaac reports that one of the scarcest resources at the snack tables of a large impersonal conference like Devcon is water. It always runs out, and it goes quickly when it appears. People take without regard for the needs of others. The same can happen with passes to evening events like parties. They should be a public good but people hoard them. These are examples outside of mechanism, but they illustrate the role that community resources play as a thermometer of community health.
The same sense of trust can be implemented online. Imagine a digital common pool resource. An art collective mints 1 NFT per member. They get their value from the value of the collective’s assets, but any member can sell any number of them anonymously. Imagine now a group with so much trust that they are just held, and never sold by anyone. The absence of defectors is a telling signal of the collective’s internal trust. The amount of cash is the same but the subjective sense of community and trust has gone up. As their profile rises, the temptation increases with the feeling trust for sticking to the norm. And even if something is sold, if sales happen at a slow and declining rate, the meaning is different than if they are sold all at once by a single defector, or in a race to the bottom.
Another generator of collective value is the gift economy, specifically Kula rings of the Trobriand islands. This describes a ritual of gifts among communities on different islands, (https://0w.uk/tvbfi)
> Participants travel at times hundreds of miles by canoe in order to exchange Kula valuables which consist of red shell-disc necklaces (veigun or soulava) that are traded to the north (circling the ring in clockwise direction) and white shell armbands (mwali) that are traded in the southern direction (circling counterclockwise). If the opening gift was an armband, then the closing gift must be a necklace and vice versa. (https://en.wikipedia.org/wiki/Kula_ring)
Because of human psychology, these rings can create goods of infinite value. Specifically loss aversion. The price I will name for selling you a coffee cup is consistently higher than the price I would pay to buy the identical coffee cup from you. So the act of a good going through me raises my subjective estimation of its value. If goods are circulating (literally or not) through a small group of people, then every time those goods pass through everyone, their subjective value to everyone increases.
Teddy tells me that China gives the best example of the beginnings of this idea. In response to its devastating real estate crash of summer 2022, bigger than the US 2008 crash, the Chinese central bank used the country’s centralized digital currency to hold the economy up. Rather than print/generate more money (e-yuan), they released e-yuan tokens that expire if they aren’t spent within 40 days of getting their new holder. This innovation encourages exchange for exchange’s sake, and ensures that an asset goes through many hands. Although they are technically distributed through a closed system, it is a closed system of millions not dozens, so not enough to trigger the rising sense of subjective value. Being destructable, they work through an even greater sense of scarcity than other assets, but they illustrate the concept, that exchange for its own sake can generate collective value.
Isaac’s best example of value without scarcity is the Nouns NFT project. Although they are technically privately ownable, Nouns has defied the logic of scarcity with several mechanisms: the daily release schedule creates a middle ground between finite and infinite quantity, they are distributed with a creative commons license encouraging, and they have made the entire Nouns platform easily forkable by other projects.
Crypto technologies are imagined by almost everyone for their ability to bring private ownership to more kinds of things. But the same technologies can open new frontiers for collective ownership, and the mechanistic generation of subjective values like community and trust. These are all examples of defying the major trends in crypto with an approach to institution design focused on community.
What is the potential of metaverses to expand consciousness?
What is the potential of metaverses to expand consciousness? Primavera asks if we can induce out of body experiences, or merge consciousnesses, and generally enter altered mindstates. If we can make these experiences more accessible, more common, what happens to our sense of what and who we are, and what we mean to each other? If group identity can be induced (safely), what are our prospects for reliably creating sense of community among small groups? Anya goes further imagining the opportunities to get a window into other experiences. Describing Lakota gratitude to the buffalo, and how, when she was told she couldn’t paint if she didn’t see an eagle spirit visitor, she started to see eagles.
I don’t have access to that perception of reality. Could I? With a consciousness SDK, could we implement a native set of perceptions and commitments? Can we give voice to the world’s cultures by making their worldviews viewable.
it all sounds farfetched, but it’s already happening. For one, outside of VR, the Internet is already expanding our appreciation for other experiences of consciousness. Our modern appreciation of trans identity, and the full range of expressions of gender and sexual preference, are probably due in large part to the discussion forums that helped marginalized people find each other, build internal confidence, and assert themselves. ASMR has probably always existed, but took tools like youtube for communicating and sharing and legitimating experiences to get an identity. So it isn’t hard to imagine VR opening new doors. Leo insists that we’re already there, and points to the AltVR_YouTube channel (https://www.youtube.com/c/AltVR_YouTube) as the best place to go to see where VR might go.
Publicly owning the feeling of ownership
I’ve noticed something I don’t like about my entries here. I can only write about ideas I’m excited about, and I’m generally most excited about my own ideas. That’s fine quality in a person who is paid to think and write up their own ideas. But not in an archivist or documentarian. I have a responsibility to this community, to document, catalogue, and then riff. Riffing is the last priority, not the first. So if you look back at my posts, what was supposed to be a diary of Glitch has become my own little idea playhouse, as inspired by Glitch and the ideas I have as I talk to people here. And the deep exciting discussions of others, absorbing and inspiring them, if I didn’t have something to contribute then … meh.
It’s against the whole spirit of the venture; it undermines a major theme of the gathering, which is to not just collaborate, but build fundamentally new ways of collaborating: to make collaborations so compelling and easy and even unavoidable that they make previous “individual genius” ways of understanding art obsolete.
This is expressed in an emphasis on the things you’d expect: collaborative art, interactive art, prompt driven AI art and other computer generated art, the experiential artist experiences of VR, the new art economy that DADA and other tokenomic solutions are nurturing, and crypto technologies for generating collective value, and for replacing the speculative role of crypto assets with a generative role that brings exchange into the piece.
So what do I do? I imagine myself to be a fundamentally social creature, and I pride myself on my collaborations with mathematicians and physicists and sociologists and Shakespeareans. I don’t need ownership, I don’t even like money, but to feel inspired to create I need a sense of ownership over an idea. I can share it, I’m certain, and looking back, my collaborations work precisely when my sense of ownership didn’t interfere with the fact that my collaborator also has a strong sense of it. Whether sense of ownership is exclusive or an unlimited shareable good depends a lot on bond and alignment with the people you are working with. So there is room for me to fit, for my inspiration and expression to be part of a whole richer than any of us, the tools and technologies we are developing, if they are going to provide models that others can take and build on, that make it easier for artists to work together than alone, they have to include an approach to building collective meaning, care, and bonding that is as replicable and reliable as the mechanisms that it ennervates. This isn’t about mechanistically scaling up to Enterprise Love. It’s more mundane and difficult: just making it easier to maintain that sense of love as a group gets bigger than a family.
Primavera finally got extitutions into my head: there are institutions that quash culture and care, and institutions that leave room for it. I don’t have words for the difference, except that it’s more complicated than centralized/decentralized or top-down/bottom-up. Whatever the distinction is, it’s going to be with an eye for it that we select what mechanisms to move forward with, and what synergies with culture we pursue.
The artist as financial dom
Buy my art, pig. Pay me for it again in a month. And if you ever sell it, you’ll stab your hand and pay me double.
Why is the current artist economy the way it is? Collectors and speculators drive the direction of art, selecting winners from the annual art school pony show. And worse, they capture the vast majority of economic value. The best most artist’s imagine to dream for is either that they got to keep some of their art to eventually sell, or that they end up kept by one of their collectors, through monthly maintenance support.
Glitch is making daily work of the alternatives. Future NFTs will send royalties back to the artist after every transaction on the secondary market. They will discourage speculation by self-inflating to fix appreciation rates at no more than a modest maximum annual 3% rate of return. Or deeper, new platforms and economies will stay invisible to speculators, and everyone who doesn’t share the artists’ transformative values.
And we can maintain progress in that direction with a vision of community, collaboration, values, equality, fairness, and anticapitalism. I’m inspired by things like that. But I’m easily inspired. It’s a solution, but it’s not a countermovement. With how exploitative the current economy is we can’t hit the target without aiming well past it.
So let’s dream the right dream:
Collectors stop owning and start renting from the artist. They sign a lease limiting how much they can look at the work. Or they pay royalties for merely holding. The artist can reassign ownership to a more appreciative collector. They can unilaterally destroy the piece at leisure, and either sit in the shadows or actively extract ransoms from their collectors to not burn the work. Art shows are dungeons, the canvas is a latex bedspread, artists paint with whips, collectors crawl in on their hands and knees, their mouths stuffed with cash that they drop in the artist’s hand so they can lap at the doggie bowl of artistic production. And they come back next month for more. It keeps happening until they have nothing left. Eventually their artist takes pity and, being benevolent, offers the collector a monthly allowance to help them buy the next piece.
When does the art market crush culture, and when does it seed it?
I’m a big critic of the “mechanisms for everything” governance design philosophy, and I’m skeptical of markets, markets, gamification, markets, and all of these technical intermediaries that get between people connecting with each other. That’s the root of my problem with crypto in art. In my mind, NFT innovations are at best gimmicky, and at their worst cynical, exploitative, and overall destructive.
Except when they’re not. NFTs have brought mechanistic innovations for allowing artists to remix each other, for making exchange meaningful, and for bringing new kinds of people together around a shared interest. How could I have a problem with that? More importantly, how do I separate the good from the bad. When does a market mechanism squash/replace culture, and when can it be a nucleation point for culture?
Political economist Albert Hirschmann (famous for the ideas of “Exit & Voice”, his role in the WWII French Underground, and as a defending attorney during the Nuremberg Trials) has a little-appreciated book about early capitalism, the personal trust-based capitalism of village markets in Enlightenment Europe. He talks about how important “the market” was in creating a basis for multiculturalism, precisely because it makes all interactions transactional. Transactionalism is rightly criticized for eradicating trust-based, gift-based ways of interacting that have characterized human exchange all over the world through human existence. But for all of its downsides, transactionalism has just what you would want to build trust between people who have absolutely nothing in common. It is a minimal set of crystal clear rules for having a positive experience with someone you’d otherwise despise.
What’s the difference, then, between soul crushing transactionalism and connection building transactionalism? That question gets to the heart of a lot of things for me, including extitutionalism. In some way, the bad kind acts as a replacement for other less rigid bases for exchange, while the good kind comes with an open-endedness that lets people keep building on top of the basic exchange mechanism with their own flourishes.
It’s this kind of thing makes me want a more nuanced story for the potential of crypto in art. I still stand by my criticisms, but I see an opening, and I can recognize the value of crypto art at least for making me ask better questions.
Something strange has been going on, which Marxist theorists and supply-side economists championing abundance. The former says something along the lines that capitalism is about artificial scarcity that artificially limits our imagination for potential worlds in which everyone has enough. It’s emerged as one of the tech-forward leftist rhetorics. The latter has been grounded out in California’ housing crisis, with the argument that the solution to the housing crisis is to do everything to let more houses get built, where everything means a stripping of regulations for the environment, safety, and so on. It’s led to strange things, like anarchists using capitalist-coding to pass as mainstream, and capitalists using anarchist-coding to appeal to the youths.
It’s all been a puzzle to me. I’ve been arguing for scarcity for years. It’s the impetus to share, which is the impetus to learn to share. Sharing is a skill that Americans have forgotten, and I blame that atrophy with many of the problems of our democracy today.
For my favorite example, ranchers used to have to share the West. This led to innovative common property regimes, as ranchers and cowboys shared the country and managed it together to recover their herds and build a living together. That all went away when the invention of barbed wire made it cheap to enclose large plots, making it so no one has to share. In fact, if you take a look, you’ll see that most applications of technology are toward making things smaller, cheaper, and more easily privately ownable. I think this is why Americans have forgotten how to share, and why our democracy is under threat.
When there’s not enough to go around, we have to talk, negotiate, and build a relationship. It makes us have to relate. On the other hand, when we don’t have to share, we lose our tethers to society and become unrelatable. For me, the ultimate test of abundance is the behavior of our billionaires. They’re smart people, but they invariably get isolated and diverge from the rest of humanity in what they need. They stop being relatable, and with inordinate power to have what they want, without regard for what anyone else wants, they gain inordinate power to take the world away from everyone else. Without scarcity we don’t need enough other, and without needing each other we lose each other, and betray our social roots.
So enough abundance. For humanity, I wish scarcity and the tools for negotiating it gracefully together.
I had an appearance on The Democracy Innovators Podcast, an independent publication launched in February 2025 by Alessandro Oppo and Carlo Michaelis. With my colleague Cecile Green, we share our work on the Commoning Standard, an initiative to increase access to the skills of commons stewardship, which happen to also be the skills of self-governance, community organizing, cooperative leadership, and public entrepreneurship.
Sharing some recent podcast appearances here, one called Governance Futures another called Education Futures.
“Education for the stewardship of the commons” on the Education Futures podcast
I was on the Education Futures podcast talking about AI and Education for the stewardship of the commons
Here’s the blurb host Svenia Busson published with the episode
“AI is stripping away the superficial parts of education and forcing us to ask why we’re learning in the first place.” Prof. Seth Frey A new episode of Education Futures is out. 🎉
I recorded this conversation with Seth Frey, professor at University of California, Davis, at the Learning Planet Institute in Paris, and it stayed with me. This episode is not about banning or embracing AI. It’s about re-centering education on agency, responsibility, and collective capacity, and asking what kind of people we need to cultivate before deciding what role AI should play.
We talk about: Why AI often becomes a substitute for learning, not a support for it The crucial difference between formative and summative uses of AI Why peer-to-peer learning (hello, WAP – We are Peers, pioneering peer learning 👋) changes how students use AI Why meetings, dialogue, and facilitation are learned skills, not inefficiencies And why education should focus on stewardship of the commons: learning how to run things together, responsibly.
Fascinating conversation, thanks again Prof. Seth Frey for the inspiration ✨
“Scaling Local: Culture, Decentralization, and the Science of Governance” on Governance Futures podcast
I also contributed to Governance Futures podcast with my colleagues Eugene Leventhal and Jamilya Kamalova, to talk about decentralization and the idea of scaling smallness.
I had a recent opportunity to share some new work that I’m excited about, on what happens when you let people design social worlds for themselves. The person who keeps interrupting with such supportive, enthusiastic criticism is economist Sam Bowles, who’s really a big inspiration for this work. Thank you to my hosts Marina and James, and Katrin as well.
If you can’t stand how old liberals put so much on civility when the world is burning or, if you’re baffled that today’s cultural extremists have thrown freedom of expression under the bus, or if you just think there’s too much infighting all around, then there’s a solution.
It’s actually not hard to reconcile the ethics behind broad-minded liberalism and emotionally charged bad faith confrontationalism into one framework. It’s not hard to explain how they’re both good and important, can co-exist, and actually always have, serving different purposes.
The dialogue- and politics- first spaces: Pens and knives.
The worldviews seem incompatible because they exist in two different spaces built on different assumptions. These are the “dialogue-first” and “politics-first” spaces. I think of it as pens out versus knives out. Dialogue-first spaces exist when there is physical security and everyone can assume the good faith and ability of everyone else. These get you the familiar ideal of older liberals: unity is a goal, good intentions behind a bad action matter, civility matters, due process is divine, there are no bad ideas, you attack the idea not the person, speech is free but yelling doesn’t work, content trumps style, you can discuss abhorrent ideas, defend people with abhorrent views, due process is respected by all, and reason prevails. You’re in dialogue space when you can ask challenging, ignorant, vulnerable questions and count on sympathy, patience, and an explanation. Think close family and friends, sometimes the classroom.
Politics-first spaces are wild: none of the above is true. You don’t feel safe, you don’t trust those engaging with you, you cannot assume good intentions of others who have wronged you. In this space you should go hard: attack people rather than ideas, vulnerability is weakness, interest in other cultures is appropriation, race and other identity differences are recognized and even emphasized, affiliation and trust are based on those identities, the judicial system is a cruel game, the legitimacy of your input depends on them, mobbing is legitimate, civility is patronizing, gaslighting, or a powerplay, a witchhunt is a tactic, silence is assent but self-censorship is tact, shutting someone down is fair game, how you come off is as important as how you are. You immediately see the value of these attitude in the middle of politics-first space, any time you become a challenge to structural inhumanity on social media, in opinion columns, during protests, and even in a bad relationship.
It’s tricky because a knife can pass for a pen, to everyone
That could be the end of it: what tool we hold depends on if we’re safe and we feel safe. The catch is that a space can claim to be dialogue-first but be politics-first in secret. That’s dangerous because when a political space projects dialogue values, the emphasis on good faith makes it easier to hide abusive dynamics. For example, if there’s no blatant evidence of an instance of sexual assault in the group, and good faith requires taking the assailant at their word, then the veneer for dialogue-first dynamics can perpetuate awful behavior. The threat lurks in every dialogue space all the time.
Creepiness can’t lurk as easily in politics-first space. Reality comes down to appearance, meaning you only need to be loud to be heard. Callouts, cancelling, and other seemingly unaccountable tactics are fair game, even strategic, in political space. Safe spaces are a humane survival strategy.
On the very edge of social change and activism, dialogue is naive because the consensus conspiracy of institutional violence has bad faith at it’s core. That bad faith stays invisible without a civil rights movement, Apartheid resistance, BLM, or Alinsky’s joyously bad faith Rules for Radicals. In those cases, the politics-first headspace is the appropriate headspace.
It’s tricky because you can make my pen a knife just by saying so
The only truly dialogue-first spaces are those that maintain consensus from all participants all the time. If one member’s experience is that they don’t believe others are acting in good faith, it’s literally not a dialogue space anymore, no matter how many other people still believe. A member saying their pen space is a knife space instantly makes it true. That sounds like an overstatement, but look: If a member of a dialogue-first space claims another has covertly violated trust with harassment or assault, do you take the claim seriously or not? If you think they’re sincere, you’re acknowledging that bad faith is happening somewhere. And if you don’t, then you just made it true by denying their sincerity. One way or another, the consensus is broken. All that’s in question is if your hands are red with it.
Since anyone can call bullshit at any time, true dialogue-first spaces are fragile. They need constant nurturing. You can’t just wish that away. It’s easy to get nostalgic for a time when people could just talk about ideas without getting mobbed on social media. But there are real humans who say that that time only ever existed for you. If you don’t accept their experience as true, then you are making it true by perpetuating their marginalization. When minorities in universities say that academia isn’t actually a field of pure ideas that rewards all equally, they are saying that they are experiencing the university’s founding ideal of dialogue as just a veneer. If that’s their experience, then good faith means assuming they’re right unless proven otherwise. I’m actually proud that universities today are listening, accepting with grace that they can’t wish themselves back to dialogue-hood without asking critically whether that founding ideal really exists for everyone. That has upsides and downsides. Firing profs for assigning Huck Finn is the other side of the coin of finally being able to fire them for sexual harassment. And it will continue this way until affected communities feel ready to buy in again to the university’s idea. The thing about the university’s fragile ideal is that it’s not real if it’s not fragile.
To plowshares
Everyone deserves to have a dialogue space they connect with. Dialogue space is less stressful and creates more room for growth. It’s important to want and have dialogue-first spaces. But it’s also important that whatever space you’re in has the right name. So within both spaces there are important things to do.
In a dialogue-first space. The first thing to do is take a person seriously when they are challenging the consensus. Victims who come out to expose violence in superficially dialogue-first spaces often get hostility for questioning the consensus, when they should get rewarded for finding the right name. You listen to such challenges because you cherish your space enough to question it. You don’t have to be an uncritical advocate, you just have to show sincere interest in finding the truth.1
Another thing you have to do is protect the consensus from needless undermining with appropriate vetting and onboarding of new members for their own willingness to maintain good faith.
In a politics-first space. The fragile consensus of dialogue-first spaces is hard to build from nothing. But it happens, and you can do it if you have the capacity. Capacity is how much bullshit you can take before losing patience, getting frustrated, or otherwise dropping the pen. People don’t get to pick their capacity, and many don’t have much. Your capacity might be higher because of your personality or your privilege or your training.
Another thing you need is a foundation of trust built the old-fashioned way, with long personal history or shared identity group membership.2 I’ve seen that people who live in a world hostile to their existence are often tuned for politics-first exchange, and you should understand it’s a very rational stance. They can have a frustratingly high trust bar for vulnerable dialogue with outsiders like you, and you have to respect it.
Who must bring a pen to a knife fight
To get two people assuming good faith from neither assuming good faith you need one person to assume good faith. That first mover should be the person with more capacity.
If you’ve been blessed with high capacity, the tax on that blessing is an obligation to create a world that is dialogue-first for everyone. That’s hard because you can only build dialogue conditions in knife space. It’s on you to stand by dialogue-first ethics and also remain compassionate, humble, and cool in the boiling pot of politics-first exchange. You need courage and strength to be the only one holding a pen, holding yourself to the high standards of both at the same time.
Does that sound unfair? You might be more of a knifer than you realize. Are you over that time you were called out unreasonably?3 Are any of your perfect rational arguments against wokeness motivated by a sense of grievance? If your approach to defending rationality, reason, discussion, open-mindedness, freedom of expression, and other good stuff ever sneaks in sarcasm or dismissiveness or even frustration, then there’s a chance that you’re just an agent provocateur, claiming you support dialogue-first spaces while undermining them with your own corrosive bad faith. You should consider getting out of the way to work on yourself. The tension between the spaces is an opportunity, not a warzone, and making war of it is a fundamental betrayal of enlightened dialogue values. So find someone with the capacity to maintain dialogue-first presence, model its value, put being effective over being right, and absorb political blows until some knifers finally start to let their guard down. Then become one.
A good first-mover actually needs a bit more than capacity. They have
The ability to take the perspective of people who have suffered. This empathy and sensitivity can be gained from books if not experience. One byword for this is being trauma informed.
Group membership.
The integrity to not be a creep yourself. This is extra hard if you have the blindspots that come from needing to identify as the protagonist in your fights.
Wrap
This is a model. It succeeds at explaining a lot of the contradictions faced by people who are both sympathetic and wary about social justice. It explains why a lot of things that seem ugly about that rhetoric are adaptive in context, and what a space needs to be ready for civility discourse. It also gives a strategy for moving forward. And it gives you something to aspire to if you’re a rational type who feels confused or aggrieved. Hopefully this makes it easier for you to understand what’s going on with society right now, and articulate your place in change.4
With capacity you must believe you can do the impossible passably: respect a victim’s claims with credence and respect due process at the same time. ↩︎
Requiring group membership is a violation of dialogue-space, but in politics-space it’s the oldest and least bad trust signal, from before we had societies in which general good faith was possible to assume. To make a difference in a space of queer black radicals, you either have to be a queer black radical, or connect with one through whatever other identity label you can find in common. ↩︎
I am over the time I was called out unfairly, but it took a while. Several years. It was a friend I approached carefully, sincerely, and humbly with questions about the antioppression movement and I got piled on. I was confused and resentful for about 8 years before I found a friend to complain to and talk it through with. I’m lucky to have gotten the head start on this framework for reconciliation. ↩︎
“When the proper place of AI art schlock is to make learning to draw so fun we don’t need it anymore?”
As society works to shape the role of AI in work, a major question looms: What kind of work should we favor for AIs, and which should we work to keep to ourselves? I’ve been slicing up the conversation of what work to automate along the formative/summative distinction in education. Which work do we do to develop/learn (formative) and which do we do for the outcome (summative)? Maybe the role of AI depends on whether a job is about the outcome or the process of doing it? The prediction would be the AI uptake is fastest for the areas where a given kind of work is output work. For work that’s about the output AI will help people be “better”, and we’ll more easily find social agreement to automate. For work that’s about the process, automation will make people “worse” as it causes decay of fundamental skills. And work that’s both, some of it will incorrectly get treated like it’s just one, while some is more sensitively sliced into its output part and its process part.
Notetaking is a good example. It’s been one of the fastest uptake areas of AI. And it’s becoming ubiquitous for meeting minutes. But in a lot of science, math, and humanities education, in debate, conversation, brainstorming, and so on, notetaking is a formative tool, for helping us think, and we don’t help ourselves by automating it. What will happen? Will we divide notetaking into summative parts (meeting minutes) and formative parts (brainstorming), with different roles for AI in each, or will we pretend tasks are just one or the other (critics of all AI generated images). The frame might be applied to sociologist Putnam, and how he blames TV and TV news for the decline of American participatory democracy. We thought news was about the output, so we create broadcasts, and the formative part—e.g. the skills around finding and individually interpreting events for oneself and one’s community—decayed.
Midjourney is another great example. Say that art has a “social development” role and an “illustration” role. If we allow that illustration is more about the output, we might expect to see AI images playing a greater role in slides than in paper figures, or web graphics than fine art.
Education is another example. Because teachers treat homework as formative (“practice makes perfect”) and students treat it as summative (“gimme the grade”) we’re suddenly right in the middle of a social conversation about what uses of AI aid learning and which are plagiaristic abuses?
Governance is especially timely and relevant. I’m increasingly obsessed with governance as a thing that people use to develop themselves. So I’m nervous about AI facilitation, argumentation, and deliberation because they are developed by people who assume that all governance work is summative. I believe more and more that a surprising chunk of it is formative, and AI will make democracy worse as our good habits decay.
This doesn’t mean theres no place for AI in formative work. Just very different AI. The role of tools in formative work is to make itself obsolete. What does that look like? We’ll find the best examples in the areas of society with the most agreement that work is formative. I’m thinking K–12 education. No one is proposing to replace gradeschoolers with LLMs, even if LLMs are cheaper than five year olds, with better attendance and better grades. That’s a sure sign it’s formative work. But I’ll be curious where else besides edtech we find tools and uses of AI that are focused on reducing dependence by developing people. There may be interesting hints among people who use AI art tools formatively and LLMs for Q&As. For example, both of these formative uses of AI are iterative. For governance my opinions are pretty strong. Instead of using human discourse datasets to train AI facilitators we should use them to train AI debators that we use to train human facilitators.
Of course, this argument depends on the idea that we’ll have any control over the role of AI in society. I think we don’t have a lot but we have more than we think. I’ll be curious how quickly or slowly it happens that the kid who likes to draw for its own sake stops getting asked “Why?”
Society lost interest in chess AI just as it was getting interesting. IBM’s Deep Blue changed chess, but it didn’t kill it. These days, human/computer hybrids can accomplish things that neither could alone. Why do people still learn chess? Because it’s fun to learn and think about. Fun, enrichment, and voluntary personal enjoyment of manual tasks will be the compass of formative learning, and a source of a whole range of insights into what we mean when we talk about bringing AI in.
This all amounts to an argument for public AI as well. An AI devoted to developing humans to replacing it, and putting itself out of a job, will never compete with one that makes us dependent. The private sector vision for AI is to increase the capacity of AI or AI/human hybrids, while decreasing the capacity and ability of humans individually. No privately held bot is trying to make itself obsolete.
A lot of people interested in technological solutions to democracy’s problems see dry legal texts and think they’re logical enough to automatically translate into code. But being active in computational policy analysis has exposed me to extremely subtle edge cases. After you see them, you can’t unsee how deeply these seemingly objective texts rely on everyday knowledge of our culture. That’s awkward news for computational law.
There’s the most obvious problem that logical “if”, “and”, and “or” do not map cleanly to their everyday counterparts. You can ask a spouse to stop by the market on the way home: “please get a gallon of milk and if they have eggs, grab a dozen,” but when they come back with a dozen gallons of milk because of the egg shortage, you’re allow to be surprised.
Those are tricky enough. But the problem goes a lot deeper. It’s actually surprising just how much a person needs to already know about society to make simple seemingly logical statements interpretable. The hardest examples I’ve found have been while translating lists of acceptance criteria from human written policies into logical statements (under the Institutional Grammar framework; Frantz & Siddiki, 2022). There is so much subtlety in disaggregating lists. Real-world policy documents leave their basic logic to context all the time.
Connectives can be left to cultural knowledge
I’ll start with the most dramatic contrast. Compare these two policy sentences detailing hiring qualifications, based on examples I’ve actually observed:
Persons with the following qualifications are eligible to apply:
five years of experience
three letters of reference
up-to-date certifications.
Persons with the following qualifications are eligible to apply:
Ph.D.
J.D.
M.A.
There is a fundamental logical difference between these statements, and it can only be inferred by cultural knowledge. Do you see it? It’s a big one: While the elements in the first list are linked by AND (all are required), those in the latter are linked by OR (they are alternatives).
There is no explicit cue in the text that these lists are of a completely different nature. In the above cases, the reader must be familiar enough with society to know that the elements in the first are reasonable to expect together (I would disqualify a candidate for having only one), while those in the second would not normally be required for a single position or expected from a single candidate (I would accept a candidate for having only one).
Inclusiveness can be left to cultural knowledge
The items in the second sentence are “disjunctive”: they are linked by OR. An OR can be exclusive or inclusive. That’s the difference between “Select one option” and “Select all that apply.” A college student might be forced to apply their coursework to only the B.A. or B.S. version of a degree (a choice I faced pursuing Cognitive Science; I took the B.A.), but taking more than one course that satisfies that degree’s requirement will not disqualify them (taking both “Philosophy of mind” and “Moral philosophy”). An exclusive “or” (XOR) links the possible choices of degree while an inclusive “or” (OR) links the ways to satisfy that degree.
The link in the second sentence is inclusive because a candidate would not be reasonably disqualified for having earned a Master’s degree on the way to their Ph.D., J.D., or J.D.-Ph.D. But, as often happens in real world policy texts, this inclusivity is left to context. There is nothing explicitly in the rule saying that it’s OK to satisfy more than exactly one of the criteria. You just have to know how degrees work.
Exhaustiveness can be left to cultural knowledge
While inclusivity comes up in both formal and informal uses of “or”, there is another property that only shows up in informal usage. In strictly logical usage, when several statements are linked by AND or OR, at least one must be true in order for the rule to be satisfied; “none” is not an option. But in colloquial usage, a statement can be satisfied even if none of its listed criteria hold. That’s because, in natural language, it is possible for a listing to be “inexhaustive”. If you flag a list of criteria with phrases such as “such as”, “for example”, “including but not limited to”, or “et cetera”, you are creating an inexhaustive listing of a rule’s possible criteria. But real world policy texts aren’t careful enough to utilize these reliably.
The first sentence above is non-exhaustive: an applicant with six years of experience (instead of the stated five) is also probably eligible to apply, even though one who submits four letters of reference (instead of the stated three) could reasonably have their application returned without review. Again, there is no sign in the text, you just have to know enough about society to know that the first criterion is most likely not exhaustive while the second most likely is.
It is possible, and good practice, to flag all of these properties explicitly. For example, “five years of experience” above would be changed to “at least five years of experience”.
Implications
These are really vital and subtle issues in machine-supported reading of policy. They are less of a concern in machine-supported writing. The developer of an authorship support tool must know that their tool should force authors to be explicit about each property, but once that is forced, all outputs will be unambiguous. Computational policy authoring tools like Pika and PolicyKit enforce these distinctions by translating them to the rigorous logic of code (Zhang et al. 2020; Wang et al. 2024). The sentences produced can be explicit by design. The situation is very different in machine reading of human texts that are authored freely, where these ambiguities are endemic.
LLMs
While this problem was once a dealbreaker for natural language processing (NLP), large language models (LLMs) now encode a lot of cultural knowledge as comprehensively as syntactic knowledge. It is now conceivable that an algorithm can determine from context whether items are linked exhaustively, exclusively, and conjunctively. It will still require care, but this is one major way that LLMs represent a major milestone in computational policy analysis and natural language processing generally.
Conclusion
Theses distinctions and caveats aren’t new discoveries. They’re learned by lawyers, policy scholars, linguists, and computer scientists. But I’m sharing anyway because I’m proud of the example text, which very cleanly shows how easy it is to take each of these properties for granted in natural language. Did you notice that the first listing was an AND and the second an OR before I pointed it out? Will you be thinking of them next time you’re writing a list of rules for your community? If so, that’s great. As someone who studies your rules, it would help me out a lot.
References
Frantz, C. K., & Siddiki, S. (2022). Institutional grammar. Springer International Publishing.
Wang, L., Vincent, N., Rukanskaitė, J., & Zhang, A. X. (2024, May). Pika: Empowering Non-Programmers to Author Executable Governance Policies in Online Communities. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (pp. 1-18).
Zhang, A. X., Hugh, G., & Bernstein, M. S. (2020, October). PolicyKit: building governance in online communities. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology (pp. 365-378).
Something I’ve been stuck on for several years. There’s the part of every task that you do to get it done, and there’s the part that you do to benefit from the process of doing it. We’ve made this pretty bimodal: with some tasks (literary reading, expressive writing, educational problems) that are primarily about the experience and others (most things at work) that are about the result. Some things are clearly both (sports) but I think there are some things that are invisibly both. It’s important because it influences where we apply technology. If there are things that we think are about the output that are actually about the process, then they shouldn’t be automated. A good potential example is note-taking: if it’s less about the notes and more about the taking, the mass adoption of LLM summarization may not serve us.
I’m always afraid that maintaining governance systems (taking notes, facilitating meetings, getting to agreement, processing information) is about “both”. What if low-tech manually maintained governance helps us keep our self-governance muscles in practice, maintaining the habits of running effective teams, exhibit and instills values of service (commitment to a team’s mundane work is a strong signal), create things that are socially valuable because they were costly to provide (a well run gathering as a valued ritual), and even evaluate our peers (observe who is responsible in their commitments and trustworthy in their notetaking)? If there’s anything to any of this, then governance technologies may be the last thing we need: the most counterproductive thing to invest engineering/automation/technical effort into.
There are a few obvious counterpoints, namely that those things can be developed (maybe better developed) by doing the more meaningful work that’s not automated. Maybe that’s right, or maybe it assumes too much learning transfer, and certainly is disregards the credibility and legitimacy of drudgery: in community houses cleaning the toilets was acknowledged as less desireable and more appreciateable work than cleaning the kitchens
There’s a great example in Ostrom’s work of technical improvements killing a commons
The more constructive version of the same question: what is the role of technology in tasks that shouldn’t be automated away?
p.s. turns out Brian Eno says the same thing about music in AI: https://bsky.app/profile/karlbode.com/post/3ldf6zg65ic2b
Domalde was a tragic Swede king described in the 9th century. His mother had cursed him with bad luck, which carried to his people, who were having bad harvests. As the story goes:
Domald took the heritage after his father Visbur, and ruled over the land. As in his time there was great famine and distress, the Swedes made great offerings of sacrifice at Upsalir. The first autumn they sacrificed oxen, but the succeeding season was not improved thereby. The following autumn they sacrificed men, but the succeeding year was rather worse. The third autumn, when the offer of sacrifices should begin, a great multitude of Swedes came to Upsalir; and now the chiefs held consultations with each other, and all agreed that the times of scarcity were on account of their king Domald, and they resolved to offer him for good seasons, and to assault and kill him, and sprinkle the stalle of the gods with his blood. And they did so.
https://en.wikipedia.org/wiki/Domalde
This idea of king sacrifice is intriguing. It takes head on a contradiction inherent in the idea of a king, the most valuable person. If they’re so valuable, and if sacrifice is about giving up valuable things, then naturally a king is who you murder once murder of less valuable things has failed. King Sacrifice is important to distinguish from chaotic regicide by the masses. Domalde didn’t just face an uprising or revolt, he was sacred and therefore sacrificial. Or not. Maybe it’s just a story. Maybe some mere uprising or revolt got dressed up as having a deeper purpose to make the people look less barbaric. But the trope of the Sacred/Sacrificial King, evident in examples such as Rex Nemorensis, the scapegoats of myth, and obviously Christianity, gives us a look into a world before Kings merely had a divine right to rule, when they had a divine responsibility, enforced by all people, to rule well.
The theme of King Sacrifice—in which the benefits of power are structurally outstripped by its risks and responsibilities—has great implications for governance design, a concern of mine. King sacrifice creates a frame in which leadership is inherently thankless, accountable, and fraught: an ideal environment for selecting responsible leaders.
The prompt
Once you have experienced the failures of strong leadership and the failures of extreme decentralization, you may converge on a specific place in the middle: “We should have leaders, but not anyone who wants to lead.” The collective wisdom is that reluctant leaders are more like followers. They are humble, empowering, and their use of power is seen as responsible and legitimate because everyone understands that they don’t like using it.
So let’s say we all agree: “We should have leaders, but not anyone who wants to lead.” What then? It’s a great if you happen upon the rare someone like that, but in practice they simply can’t be found and recruited reliably. Whether you’re a community group or a large firm, it’s the luck of the draw for you: to have the right person around with the right attitude and alignment, you get lucky or you don’t. In that way, the maxim is more of a description than a strategy for good centralized governance.
Of course, it would be different if we could find or create reluctant leaders. If there were a way to reliably and systematically produce and select reluctant leaders, we’d be one step closer to saving dictatorship, while creating a dictatorship that’s worth saving.
The first thing to do is break this dichotomous construct “reluctance”. It presumes that a candidate is either entirely power hungry or entirely power wary. But how a person is depends on where they are. We know that power-wary people can eventually a develop a comfort for and fluency with power that can start to look like a taste for it. And we know that many traditional authority-focused leaders can have eye-opening experiences that inspire them to open up and flatten out. We also know that your reluctance depends on simple things like how full your plate is.
Let’s take advantage of context to create reluctant leaders. And under a framing of king sacrifice, it isn’t difficult. We need to step away from a picture of leadership as providing access to power and control, in favor of the view in terms of service and collaboration. For the right person, a big sacrifice counterbalances the benefits of power and control, leaving only the value of learning and serving others.
Imagine a small group election in which each candidate has to explicitly demonstrate their reluctance, and convince people to vote for them on the faith that they don’t really want to do it. More precisely, imagine an election in which every candidate describes what they are sacrificing in order to lead.
People who already don’t want to lead will already see that they are making a sacrifice, and can just describe it. People who do want to lead, and would naturally sacrifice anything, can make themselves actually reluctant by proposing a big sacrifice that they will make if they take the position. They will take a pay cut, they will never force anyone to do anything, they will get a therapist, or make themselves recallable with a minority vote, or pay out of pocket toimplement a program that reduces the authority of the position by spreading power around.
The community will then evaluate each candidate’s sacrifice along with every other qualification of holding the position. Whether the sacrifice is big or small or credible or not can’t really be quantified in general, so it’s a subjective or political decision on the part of voters whether a person’s sacrifice is substantial and legitimate enough. But even then, the political process is more responsive to arguments that are credible, and a goal is credible that meets recognized goal evaluation criteria. One example is the SMART taxonomy, which helps you ask: Is the proposed sacrifice not just substantial, but Specific, Measurable, Achievable, Relevant, and Time-bound?
Can this sacrifice framing mechanism be gamed? Well it’s political, of course it can be gamed. This is why it’s important to assume constrained contexts, such as an organization that is small enough that it can maintain alignment and rely on social factors like trust, respect, reputation, and just deeper knowledge about the person. Whether a pay cut is a sacrifice or a token depends on inside knowledge about the person and their existing means. It’s up to the community to know their candidates well enough to know if sacrifice is being invoked authentically or only rhetorically.
Of course, I’m coming from a specific place, defining leadership as shared or cooperative leadership, dictatorship as simply unitary authority, potentially benign. It can be captured by the power hungry, but doesn’t serve them by definition. And I’m assuming a group with a basic level of mutual regard and goal alignment. So these aren’t general claims, but claims that make sense for a group that is small enough for social norms to play a role in how things work.
Where is all this coming from?
Aside from the noble project of Saving Democracy, I’ve got a side hustle of Saving “Dictatorship”. It’s not such a betrayal:
Systems based on authority and leadership are pragmatic, workable, and familiar.
They are the easiest social systems to design and implement and scale (explaining why they’ve taken over the world)
If you can keep them benevolent, they can also be surprisingly democratic, in the sense of integrating the needs of all stakeholders, if you define benevolence to require it
Power and coercion are bad, they often co-occur with authority and hierarchy, but they can be decoupled. Successfully decoupling them puts unitary structures on the map as democratic solutions. That separation has to happen both in an org and in everyone’s minds
While people think of leadership and democracy as opposed, I think they’re aligned. As I define those terms, strong participation is a result of strong leadership, and strong leadership is a result of strong participation. They’re no dueling alternatives to the distribution of power, but two sides of the same coin of universal empowerment.
Taking it all the way, I actually don’t trust a democracy to work if every member of it hasn’t had a lot of experience leading. Making unitary authority systems of every size more capable of care and accountability makes it easier to give more people more experience in leadership.
When you stumble on an excerpt that says what you want to say better than you could ever say it, you switch very eagerly from blogging to quoting. This is a long excerpt from Francesca Polletta’s 2002, “Freedom is an endless meeting,” and incredible historical book about participatory community organizing. You can tell from the title that she’s interested in the fact that all solutions come with a pet tension for struggling with. Recounting story after story of early civil rights organizers balancing idealism and pragmatism, you understand how she gets so easily to realism, and you wonder why everyone in the democracy scene hasn’t. This bit tackles the eternal question of what leadership means when your goal is cultivating leaders and the biggest threat is your own effectiveness.
The literature on organizing is rife with injunctions against leading: organizers should rather help residents articulate their own agendas and build their leadership. Yet, in the process, organizers are often expected to help identify goals, push people to question their preferences, and rally them to act. How can they do that without thereby undermining the leadership capacities of those whom they are organizing? Myles Horton’s answer was to ask questions. “I use questions more than I do anything else. They don’t think of a question as intervening because they don’t realize that the reason you asked that question is because you know something…. Instead of you getting on a pinnacle you put them on a pinnacle.” Horton described a Highlander director in a workshop who “asks one question, and that one question turned that workshop around and completely moved it in a different direction.” Was the Highlander workshop leader leading? Should one ask questions that open the whole enterprise up for scrutiny? That purposely move a discussion in a new direction? In SNCC, asking questions later became a way for organizers to hold onto their radicalism without feeling that they were imposing it on the people whom they organized. The tactic ended up alienating people more than involving them. What comes across in the stories that Horton tells, in SNCC workers’ tales of the best organizers, and in the broader literature on organizing is good organizers’ creativity: their ability to respond to local conditions, to capitalize on sudden opportunities, to turn to advantage a seeming setback, to know when to exploit teachable moments and when to concentrate on winning an immediate objective. Sometimes you insist on fully participatory decisionmaking; sometimes you do not. Albany SNCC project head Charles Sherrod urged fellow organizers not to “let the project go to the dogs because you feel you must be democratic to the letter.” Horton recounted on numerous occasions an experience that he had had in a union organizing effort. At the time, the highway patrol was escorting scabs through the picket line, and the strike committee was at its wit’s end about how to counter this threat to strikers’ solidarity. After considering and rejecting numerous proposals, exhausted committee members demanded advice from Horton. When he refused, one of them pulled a gun. “I was tempted then to become an instant expert, right on the spot!” Horton confessed. “But I knew that if I did that, all would be lost and then all the rest of them would start asking me what to do. So I said: ‘No. Go ahead and shoot if you want to, but I’m not going to tell you.’ And the others calmed him down.”
Giving in would have defeated the purpose of persuading the strikers that they had the knowledge to make the decision themselves. But Horton sometimes told another story. When he was once asked to speak to a group of Tennessee farmers about organizing a cooperative, he knew, he said, that since “their expectation was that I would speak as an expert… if I didn’t speak, and said, ‘let’s have a discussion about this,’ they’d say, that guy doesn’t know anything.” So Horton “made a speech, the best speech I could. Then after it was over, while we were still there, I said, let’s discuss this speech. Let’s discuss what I have said. Well now, that was just one step removed, but close enough to their expectation that I was able to carry them along…. You do have to make concessions like that.” What better time to make a concession than when you’re looking down the barrel of a gun? Horton presumably knew that he could get away with refusing to be an expert in the first situation and not in the second. Perhaps the difference was that he was unknown to the farmers and was known to the strikers. But one could argue that a relationship with a history could tolerate aberrant exercises of leadership while first impressions die harder. In other words, extracting rules from the stories that Horton tells is difficult. When to lead and when to defer, when to ask leading questions and when to remain silent, when to focus on the limited objective and when to encourage people to see the circumscribed character of that objective—the answers depend on the situation and are not always readily evident.
p. 76
I love how that first bit about questions turns the patronism of the Socratic method right on its big self-important head. I also like the focus on process, what Polletta calls “the developmental project of democracy.” I think the single biggest force acting against democracy is the experience of everyday people in their first organizing role trying and failing to get others involved, and coming reluctantly to the conclusion that it just doesn’t work, that people want to be told what to do. Your bad experience cultivating democrary wasn’t a lens into the fundamental architecture of human nature. You’re a person in a social reality trying to fine tune a smaller reality within it. You’re in a project, and a project has to get where it’s going by starting where it’s at.
From a developmental perspective, no compromise from your ideal is really a compromise. A compromise is a step away from the ideal, and your steps are still toward the ideal away from the status quo. They don’t approach the ideal directly, as the crow flies. They follow the landscape and its contours, avoiding the mud as much as possible. Following the hills is only a compromise in the sense that obeying gravity is.
To keep the navigational metaphors going, what does it mean to navigate by the stars? When we follow a star, it’s not with the goal of getting there. You follow a star to reach a place on Earth that’s closer to it. And that’s a meaningful, deeply idealistic journey even if, in a cosmic sense, every place on Earth you could possibly go is ultimately the same number of years away from the light. Even if, as your North Star takes you climbing along the sphere to its pole, less and less of your motion is up toward the star and more and more is sideways to the pole. That’s just physical law. Obeying gravity is not a compromise.
An especially exciting thing about Polletta is her critique of prefiguration. Prefiguration is a popular framework for activism and radical change because it offers a way to pursue an ideal in this non-ideal world. It proposes that one create little microcosms of the ideal within the real, and that your perfect bubble grows and grows until it’s as big as the world. In the prefigurative view, the root of the power of participatory approaches to community is that they prefigure the global approach by enacting it. Seems hard to fault. But Polletta holds the developmental project in contrast to the prefigurative project, arguing that prefiguration works in relation to itself, with no more influence from the outside world than is necessary, while the developmental project is about the outside world. The project of pursuing the ideal becomes the project of finding the most idealistic way of relating to the rest of the world as it is, and that being that way in this world changes the world. The great thing about Polletta is it’s all examples and history first, so these ideas are grounded in actual things that happened, giving you nuance for free. From the page before the quote above:
One can also contrast this developmental rationale for participatory democratic decisionmaking with the prefigurative commitment that commentators have attributed to SNCC and the new left. Where a prefigurative commitment envisions change through personal self-transformation and moral suasion rather than through institutional political change, a developmental commitment is not in conflict with an explicitly political one. To the contrary, its very purpose is to produce activists and organizations capable of taking on powerful officials and agencies. From early on, Horton said, he had been “more concerned with structural changes than I have with changing the hearts of people.” A prefigurative commitment tends toward absolutism since the object is both to “oppose” a current regime and to be truly “opposite”; a developmental commitment tends more toward an acceptance of the conventional. The two projects have very different views of organization. A prefigurative project is suspicious of organization, concerned that it molds people in its own image, valorizing efficiency and conformity over the purposes for which the organization was created, raising means to the level of ends. Enacting the ends in the means, committing to the “here-and-now revolution,” favoring community over organization—all these counter the oligarchical tendencies of organizations. By contrast, the broader organizing strategy of which a developmental project is a part sees organizations as one of the key arenas for developing political efficacy, leadership, and accountability and, not least, for securing power. An organization is doomed to failure unless people have a stake in its preservation, however. Participation in decisionmaking provides the sense of ownership and the pleasures of learning that sustain people’s participation.
The relationship underpinning a developmental democratic project is a pedagogical one. People learn to articulate concerns and evaluate options by doing so. At the same time, they learn from each other, and they may also learn from a facilitator or teacher, someone who encourages, guides, questions, and challenges them.
p.74
The tough thing for me about a good book is it takes years to read because I keep going into reveries. It’s been 2 years probably and I’m only 75 pages in. Here’s another great quote from earlier in the book, redeeming meetings:
Local people have really begun to find a way that they can use a meeting as a tool for running their own lives. For having someone to say about it.
That’s a line from Bob Moses, an organizer for the civil rights movement in the US South. It offers such a striking counter narrative to the modern “meetings are bad; fewer meetings” atmosphere that work culture creates. I think what’s happened is that there has been a change in the meaning of the word “meeting”. The way it is used in those quotes is as a bottom-up gathering of community members to discuss a matter of shared concern. That is so much different from what the word means today. If I were weaving conspiracy theories, I’d say that part of the project of undermining democracy has been capturing and corrupting the word. I think there’s a case to be made that meeting, not voting, is the fundamental unit of democracy.
You’ve just reached into a large opaque jar and pulled out the number 87. I could add some other structure, that the biggest number is 100, or that odd numbers are twice as common as even, and then ask clearly answerable questions. But what if we know nothing more than what we’ve seen: a ball from the jar with the number 87? What’s the biggest number in the jar? Are there even other numbers in the jar? Or you’ve reached in and pulled out a purple marble, how many colors of marble are there? Are they all round? Are there any numbers in the jar? Some of these have actual answers, and that’s important because they link to a bigger question:
What do our philosophies of statistics fill in about our world when they’ve seen almost nothing from it?
Or put another way: just how much can you know about the world with nearly no data and nearly no theory? I’m in social science, so it’s a real thrill when I find knowledge I can trust. And to do it from nearly nothing is astonishing. You can get the shape of the world from just 1 or even 0 observations. As an empirics-first person, I’m surprised to hear myself say it, but there’s so much you already know about the world when you don’t know nearly nothing. I’ve been collecting examples for year. And these examples have actually helped me do normal things, like find my keys, charge my phone, and, thanks to German tanks, take a long, hot shower.
The foggy sea, the German tank problem, and showers at a campsite.
You’ve been wandering for weeks, no map, through a foggy landscape. You reached a body of water, knowing nothing about how large it is, and decided to cross it with your inflatable raft. It’s been about 10 minutes already, so you know that this body of water is bigger than a creek, but it could be a lake, sea, or ocean. It could be another 2 minutes to the other side, or two months. Knowing nothing, what’s your best guess for the total size of the lake?
This is a continuous version of a famous statistical problem called the German tank problem. The Allies need to estimate German tank production. As they captured and examined German tank hulls, they found that their engines had serial numbers. So if you’ve captured a tank with the number 200 on the engine, you immediately know that there are at least 200 tanks. But does that also tell you anything about the total number of tanks? It does, and that’s fascinating: it’s a hint of everything you know when you know almost nothing.
And it’s not just a matter of theory. I was at a camping ground with spotty hot water. Some days there was as much as you want, and others it only lasted long enough to get your hopes up, just one minute or two, before getting icy. So if you’ve been in the shower for one minute, you know that there was at least one minute of hot water available, but do you now know anything about how much hot water is left?
Amazingly, you do. And that’s useful to know. If you need 10 minutes to wash your hair, you’ll be in a bind if the water goes icy in five. And absent the magic answer, it’s not entirely clear what you should do. Play it safe and never wash your hair? Roll the dice and have soap in your eyes when the water turns cold? Well there’s a solution to this problem, the German Tank Problem, and to the problem of the foggy sea: Your best guess at any moment is that you’re halfway through. So if you’ve been rowing for 10 minutes, then your best guess in this moment is that there are ten minutes of rowing. And every minute after that you best guess will go up (not down!). If you’ve captured tank #350 then your best guess in this moment is that there are another 350 tanks, for a total of 700. And if you don’t know if you’ll have five minutes of hot water in the shower, spend the first five minutes showering cautiously, as if the hot water could end at any moment. At the five minute mark you have a legitimate reason to feel confident that you’ll have five more minutes to wash your hair.
That’s what I did, and it worked!
Connection to science
The scientific method in the West comes out of several centuries of debate: can you know the world by reason alone? By observation alone? To skip past a lot of arguing, the answer is “both” via the scientific method, a procedure for bringing reason and observation into dialogue. But in practice they’re always in balance, and whether to center theory or data differs so much by discipline. In some areas of knowledge, like physics, data drapes beautifully off the framework of theory. In others, with phenomena that are too complex for elegant theories (e.g. social science), theory does a much more sorry job at propping up the data, so you end up using the data as its own model. that explains the role of machine theory, information theory, and statistics. As a social scientist your theories aren’t nearly good enough to really predict outcomes, and you spend a lot of time with statistics, the part of math that turns data into knowledge.
The conventional statistics that social scientists are taught can be called “small-n” statistics because it was developed in the early 20th century when it was costly to collect many independent observations (n represent your total number of observations). You had to squeeze every bit of insight from the couple dozen n you could get. The computing of the 21st century brought us big data and a switch to an alterantive philosophy of statistics that could leverage large n.
Within that frame, this exercise, “what you know when you know nothing” is a bit of a throwback. We drop from large-n statistics, down past small-n statistics, to the very smallest-n statistics of n=1.
Relevance to the philosophy of statistics
There isn’t just one statistics of n=1, and there’s actually a different answer to the German Tank problem. There are two alternative philosophies of statistics: the frequentist and Bayesian paradigms. There is a clear formal difference that is hard to cast intuitively, but narratively frequentism develops statistics by estimating what will happen from what has happened, while Bayesianism understands statistics as a problem of estimating what will happen from an observer’s beliefs about what has happened. Instead of trying to figure out how many tanks are in this world, the Bayesian observer imagines a range of possible worlds, in which the Germans have built everything from 2 to 20,000 tanks, and they work to determine which of those worlds we’re in. It may sound like a subtle distinction, but philosophically it’s big and mathematically it’s big. And, as we’ll see, the different philosophies predict very different numbers of tanks.
Between the two, Bayesianism is ascendant today because it wasn’t feasible to use before the ubiquity of cheap computing, but one isn’t better than the other. They are both ways of writing models, and all models are wrong.
One practical difference: the Bayesian approach is better for handling the complexity that comes with a lot of data, while the frequentist approach was developed for the era of “small-n” statistics, when the challenge was usually to learn as much as you could from very little data. And because the German Tank Problem is a “very very small data” problem, the frequentist answer is better. The frequentist answer is the one I’ve described, that what you’ve observed is half of the total. The Bayesian solution is that what you’ve observed is the total: if the largest serial number you’ve seen is #350, then your best guess is that there are only 350 tanks, because, assuming tanks are costly to produce, a world of 350 tanks is the one with the most evidence.
But the Bayesian way of thinking has it’s own place as well in revealing what we know when we know nothing.
How to find your keys
I lost my keys on the ride to the gym, somewhere on a mile-long stretch, and it was dark by the time I came out of the gym and realized it. I didn’t want to wait till morning, and I didn’t want to backtrack slowly and spend a half hour looking carefully. After all, they could have been anywhere. So I thought about it. Are my keys equally likely to be anywhere along the mile stretch? Or are they more likely to be in some places than others? I could be in a world in which it was very unlikely that I would lose my keys at all. In that world they really could be anywhere. I could also be in a world in which they were just waiting to be lost, as if they were scotch-taped to the outside of my side pocket. In that world I probably lost them right away, walking down my steps. I didn’t know which world I was in, or which of all the worlds in between, but in more of those worlds my keys were near my front door. So instead of searching slowly back home from the gym, I decided to ride straight home and start the search at my front door. My keys turned out to be right there. In most possible worlds, you lost your keys as soon as they were loseable, and they are most likely to be wherever you last remember having them or moving them. The power of Bayesian reasoning is that you can reason to that, and you can prove it too, which some friends helped me do in another post.
How much charge is on your phone?
If you look at your phone, it’s unlikely that it’s at 68% charge. But unless it’s constantly dead (0%), or constantly charging (100%), it is more likely to be at 68% than anything else. If you charge intermittently, but enough to stay above empty, and not enough to keep at full, then you can think of it this way: It’s morning and you’re at 100%. By evening it would be at 0%, but you charge in little moments during the day. We’ll say that you took a step down every time you were drained by a point, and a step up every time you charged by a point. We’ll say that when you hit 100% you always unplug (you can’t take a step up over 100).
A question is: how many ways are there to take 100 steps from 100%; how many paths are there in all the different combinations of up and down steps? And for any given charge level, how many ways are there to that point from 100% that involve exactly 100 steps? 0% only has one path leading to it: there is only one way to take 100 steps down from 100%. The 2% level has about 100 paths leading to it: a hundred ways to take 98 steps down with a little goosestep up and down at some point between the top and bottom. There are a lot of paths from 100% back to 100%: you can go down 50 and up 50, you can go down and up 50 times, you can go down and up by 4 then 7 then 15.
With these ideas, we’re now to our key question. Which charge level between 0 and 100 has the greatest number of paths leading to it? The 68% charge level is the one with the greatest number of paths leading to it (0% has the fewest). Another way of saying the same thing: if you randomly generate paths of length 100, up and down, over and over, the number you’ll land on the most is 68%. Not by a lot, but if I know nearly nothing about your phone—it’s got about a day of charge, it’s near the end of the day, you’re on the move enough that it’s often but not usually plugged in—the least bad guess is that you’re at about 60-70% charge by the end of the day.
The orthography of number
In the next book you pick up, keep an eye out for the first number you see, not spelled out but in digits. Will it be big, or little? Even or odd? What can we say about the numbers that are dealt to us, numbers about anything: dollars, marbles, people, fish? The fun thing about pure reason is a) you will learn something interesting, and b) you won’t get to choose what. According to Benford’s Law, the next number you see, big or small, is most likely to start with a 1. A number starting with 1 is 30%! That ends up being about 12% more likely than 2, which is about 5% more likely than 3, and so on down to 9, which initiates only 4.6% of numbers, not 11% like you’d expect (11=100/9 digits; you don’t divide by 10 because in Arabic numerals the only number than can start with the tenth digit, 0, is 0). I don’t understand it perfectly but it’s got something to do with there being more small numbers than big numbers, with logarithms, and with Arabic numerals. They come together to give 1’s center-stage. I don’t know if this is a metaphor or the actual explanation, but if you look at the way a slide rule gives physical space to each digit according to the logarithmic way of representing “bigness”, you’ll see that 1 gets more space than any of the others, and in that way gets more real estate in our lives, with pride of place on the far left of most written numbers.
It might sound far fetched to say, but pure reason, charged with statistical theory, and seeded with one observation, can help you shower comfortably, find your keys quickly, and keep your phone alive. It can probably also help you brush your teeth, clean your windows, and wash the dishes; let me know what you find.
For me, the single biggest factor in the effectiveness of a decision process is whether the group members are all able to assume good faith in each other. This common belief in mutual good faith means that everyone wants what is best for the organization. They may differ on what that is, but they trust the system and, more importantly, each other, that they want the best for the group and its members. This assumption is an explicit principle of prominent consensus organizations such as Wikipedia.
As mutual assumption of good faith erodes, governance starts to have to operate under conditions of politics. There is an opposition. It should be stymied. Its beliefs are dangerous for the organization. Its members are motivated by maliciousness or ignorance. I think there is more to this difference between governance with and without mutual good faith than the presence or absence of contestation. There is conflict and competition in good faith systems, but members engage in it with a belief that those differences are pursued sincerely and at least theoretically reconcilable.
It’s not a clean dichotomy but those are the poles, and you can divide political science by which it focuses on, with the spiritual core of political science interested in “politics,” organizing without good faith, and a constellation of secondary areas—policy studies, public administration, community governance, deliberative and participatory democracy—interested in “governance,” organizing with good faith.
Naturally, how you should make decisions depends on what regime you’re in: collaborative or political. In political arenas, consensus is easily gamed but unitary authority is more easily abused and less legitimate. Voting is more robust to politics, and systems like parliamentary procedure are very much designed to help systems move forward in the face of political rifts. At the extreme are mechanisms for casting votes under hostile regimes, as with the holographic voting scheme I proposed in Human Computation (http://doi.org/cwnn).
And just as there are many forms that are not robust to politics, there are many that are not adapted to consensus. The excessive formality of parliamentary procedure is clunky, inefficient, and even awkward within a small trusted core group. Voting, which is almost elegant for navigating political conflicts, permits a lazy and marginalizing shortcut around a whole family of processes designed to create nuanced win-win solutions. And with enough alignment and training these can be surprisingly efficient as well.
This would be a straightforward design problem except that communities can, of course, change what regime they are in. The consensus assumption of mutual good faith must be a literal assumption. Once one person breaks it, either by acting in bad faith or assuming bad faith in another, their defection will cause defection in others. In this sense, a community’s common sense of mutual good faith is a fragile common-pool resource that is vulnerable to sudden resource crash; it only takes one defection and the cascade of failure will ratchet down. Transitions in the other direction, up from political to collaborative, are trust building projects that take time, patience, and vulnerability, especially when they occur without the luxury of a common enemy or other shared outside threat.
The idea
How a group should work depends on where it is at the moment. But how do you know where you are? Imagine a system that decided the process for every decision on the basis of how political it is. The procedures of US Congress and Robert’s Rules already have versions of this, in which a simple vote or process is the default but any member or minority can trigger a more formal, politically robust process: from straw poll to roll call or public to closed ballots. Precisely because good faith implies consensus, allowing a single person to request a politically robust procedure amounts to a mechanism for detecting breaks from the consensus belief in mutual good faith. More mundanely, many internally friendly groups will adopt Roberts Rules formally but proceed informally until internal conflict erupts. When that happens the community will open up their operating manual or bylaws and start operating their (now political) process by the book.
These examples are valuable for illustrating both the practical utility of adaptive governance processes and the weakness of how they are currently implemented. By basing adaptation on individual prerogative, rather than objective signal, these modulations of gameability are themselves gameable, as can be seen with filibuster and other strategies that minorities use within bad faith regimes to have their way by gumming up the works of the entire decision body.
What if there were a non-gameable automatic procedure for detecting political rifts, whose output would determine the process for each decision, ranging from informal consensus or delegation to fully formal voting? This would allow a governance system that can automatically tune to conditions on the ground. It could even be designed to incentivize a community to maintain good faith by producing rewards for sustaining the collaborative regime.
I can think of a few mechanisms. Depending on how robust they turn out to be to bad faith manipulation (I haven’t thought them all through), a system might use one or a combination when deciding. Some ideas include:
State of the art. Define a 1-vote threshold for more formal processes, in order to detect consensus breaks by permitting any member to break consensus.
Simple surveys. Periodically asking members how many other members they doubt, privately, could provide a measure of the emergence of politics in a body. This is probably gameable, but tied to a threshold, this mechanism could trigger other payoffs or consequences that stabilize it or at least help it complement other interventions. Better is dual ballots.
Dual ballots. When you cast a public vote two things happen: you signal a preference and you implement a preference. And those things can be different from each other. That’s why politicians will vote down legislation proposed by an opposing party, even when they agree with it. But if a vote plays two roles at the same time, it would seem impossible to know how much any given vote is just a signal or not. To separate these two roles, imagine conducting each vote twice, one with public ballots and one with private ballots. This could provide a running barometer of politicking in a political body. To prevent gaming, it would be important to ensure that the private ballot is the binding one in the event of a difference between the two. Alternatively the vote could be re-held under too high of a threshold, or the outcome could be drawn randomly from between the two ballots, which would lead to undermeasurement of the delta, but could manage the incentive to always lie.
With these barometers, a community gains tools for tracking their successes at finding agreement under difference. Pushed further, other interventions could be tied to the emergence of politics that manage it or rein it in.
With tools for detecting how political a political body is, we open up the possibility of governance processes that meet the community where it is at, and that may even structurally reinforce cooperation and collaboration in governance..
“Good question. Yes, we have your best interests at heart.”
There is a kind of problem so fundamental to organizing that we sometimes forget to think of it; common as day. You see it when
People answering a survey tell you what they want you hear instead of the truth
Someone lies at an interview
Just about any time that people aren’t incentivized to be transparent
Those are all examples of mis-alignment in the sense that individual incentives don’t point to system goals. It’s called the problem of “incentive alignment” (also known as “incentive compatibility”).
The phenomenon of “buyer’s remorse” gives a clean economic example of the idea. In a normal auction, where people are bidding for a thing, it turns out that the structure of the decision doesn’t actually incentivize an honest evaluation by buyers of what they think a thing is worth. In real world auctions people often overbid, in part because they are influenced by the fear of losing. So typical “first-price” auctions are actually not incentive aligned.
But there’s an auction design out there that actually does incentivize honesty. It’s the “second-price” auction. In a second price auction the winner doesn’t pay the price they bid, but the next highest price. Why does that change anything? To see the trick you have to think a bit. At first thought you might just think that the smart strategy is to name a crazy high price and pay the losing bidder’s fair price. But what if all bidders think that? Then you’re going to overpay. You don’t want that: you don’t want to pay more for a thing than it’s worth to you. Where this reasoning gets you is that all bidders in a second-price setting will decide to name the price that they are actually willing to pay, no more, no less.
This is a good example because it also shows how small elegant tweaks you can restore alignment. In so many real world settings, incentives don’t support honest disclosure. We have workarounds in most parts of our life, but the problem still matters, and it attracts a lot of attention from economists. Their work depends crucially on the idea that incentives determine behavior.
Why save democracy when you can save dictatorship?
Incentive compatibility is especially challenging for survey design. How old are you? How much do you make? Who did you vote for in the last election? It turns out that we can’t always trust the answers to these questions. That’s important because surveys are the least bad way to learn things about people in a standardized way. But what if it was possible to pose any question—How often do you have non-PC thoughts?—in a way that people felt an incentive to answer truthfully?
For some things the problem is easy to solve once you’ve spotted it. Say you’re studying philanthropy, and you ask “Do you donate more or less to charity than your peers?” But you realize that most people will say that they donate more than they do. The incentive compatible way of getting an (honest) answer will be to invite people to non-hypothetically donate some of their survey reward to a charity. If they donate and that donation is smaller or larger than average, you the researcher found out if they donate more than others without actually having had to ask. By replacing hypothetical questions with costly behavior you get honesty.
Another strategy is to ask questions with verifiable answers. Instead of asking “What is your height and weight?” you might say “What is your height and weight? We will measure you after this and you’ll only get paid for participation if the difference is 0.” But if you’re verifying then why ask in the first place? And what if verification is impractical? And, most relevant for us, what if it’s impossible, such as with subjective self-evaluations (“Are you kind to others?”)?
Where it matters most, incentive compatible survey design is actually a real can of worms. The problem there is clearest if we hit pause on saving democracy and take a moment to try and save dictatorship. As a longtime scholar and organizer of self-governing communities, I’m comfortable saying that many communities could do worse than structure their governance under a benevolent dictatorship. A lot of groups, organizations, and communities that I admire do. In its most ideal form, benevolent dictatorship is not that different from democracy, because the dictator, being benevolent, is caring, curious, and motivated to understand and integrate everyone’s needs. As a result, the dictator will generate just the kinds of solutions that a healthy democracy would, and they’ll probably so it much more efficiently than a large governing body.
So why not replace everything with benevolent dictatorship? The main problem is fragility. Nothing systemic keeps the “benevolent” in there. If your competent leader was replaced by another competent leader, it’s generally luck. And you have to keep getting lucky because your first dictator won’t be your last. Benevolent dictatorship slips very easily into the non-benevolent kind that has reliably attended humanity’s darkest moments. Whether it’s through bad succession or the corrupting influence of power, no tool we have can reliably keep a benevolent dictatorship benevolent.
Incentive compatible survey design
Well there might be one tool. What if we had incentive compatible personality tests? It’s easy to imagine the important questions you would want to ask a candidate for dictator.
“How likely are you to abuse power?”
“How do you respond to disagreement?”
“How do you respond to insults?”
“If a brakeless trolley is hurtling toward a loved one, and you’re at the switch that can divert it on to another track with n people you’ve never met, what is the largest n you’ll tolerate.”
You’re infuriated after reading a personal attack by a journalist in a major newspaper. Will you act on that journalist? If so, what will you do?
Asking is easy; what’s hard is to know if their answer is honest. If there was a way to know what someone really thinks, you’d just disqualify the people who give bad answers and appoint the people who give good answers.
I have lots of bad ideas on how to solve this, such as what I call “double-blind policy“, and is based on the premise that you can’t lie about a question if nobody knows what was asked.
But generally this is more likely to remain the name of a major challenge rather than the name of a class of solutions. Still: if we could solve it, I’m not entirely positive that I’d remain a scholar of classic democratic systems. I mean I would, but it would be harder not to admire the green grass on the other side.
or Will your transformative technology just entrench the status quo?
Things have come a long way since I was first exposed to cryptocurrency. Back in 2011 it was going to undermine nation-states by letting any community form its own basis of exchange. A decade later, crypto has little chance of fulfilling its destiny as a currency, but that’s OK because it’s proven ideal for the already wealthy, as a tool for tax evasion, money laundering, market manipulation, and infrastructure capture. States like it for the traceability and conventional banks incorporate it to chase the wave and diversify to a new high risk asset class.
This is not what crypto imagined for itself.
But it’s not a surprise. You can see the same dynamic play out in Apple Music, YouTube, Substack, and the post-Twitter scramble for social media dominance. These technologies are sold to society on their ability to raise the floor, but they cash out on their ability to raise the ceiling. The debate on this played out between Chris Anderson (a founder of Wired) and Anita Elberse (in her 2013 book Blockbusters). In response to Anderson’s argument that social media technologies empower the “fat tail” of regular-people contributors, Elberse countered with evidence of how it has increased market concentration by making the biggest bigger.
To skip to the end of that debate, the answer is “both”. Technologies that make new means available to everyone make those means available to the entrenched as well. The tail gets fatter at the same time as the peaks get taller. It’s all the same process.
So the question stops being “will this help the poor or the rich?” It becomes “who will it help faster?” The question is no longer transformative potential, but differential transformative power. Can this technology undermine the status quo faster than it bolsters it?
And for most of these technologies, the answer is “no”. Maybe, like crypto, a few people fell up and a few fell down. That is not transformation.
Why do people miss this? Because they stop at
“centralization = bad for the people; decentralization = good for the people”.
We forget it’s dual, that
“centralization = good for the entrenched; decentralization = good for the entrenched”
Centralization increases the efficiency of an already-dominant system, while decentralization increases its reach.
This all applies just fine to the latest technology that has people looking for transformative potential: decentralized identity (DID). It’s considered important because so many new mechanisms in web3 require that an address has an onto and 1-1 mapping to a human individual. So if identity can be solved then web3 is unleashed. But, thinking for just a second, decentralized identity technologies will fall into the same trap of entrenching the status quo faster than they isolate their transformative potential. Let’s say that DID scales privacy and uniqueness. If that happens then nothing keeps an existing body from running with uniqueness features and dropping privacy features.
If you’re bought into my argument so far, then you see that it’s not enough to develop technologies that have the option of empowering people, because most developers won’t take that option. You can’t take over just by growing because you can’t grow faster than the already grown. What is necessary is systems that are designed to actively counter accumulation and capture.
I show it in this paper looking at the accumulation of power by US basketball teams. For over a century, American basketball teams have been trying to gain and retain advantages on each other. Over the same time period, the leagues hosting them have served “sport over team,” exercising their power to change the rules to maintain competitive balance between teams. By preventing any one team from becoming too much more powerful than any other, you keep the sport interesting and you keep fans coming.
But what we’ve actually seen is that, over this century, basketball games have become more predictable: if Team A beat Team B and Team B beat Team C, then over a century Team A has become more and more likely to beat Team C. This is evidence that teams have diverged from each other in skill, despite all the regulatory power that leagues have been given to keep them even. If the rich get richer even in systems with an active enduring agency empowered to prevent the rich from getting richer, then concentration of power is deeply endemic and can’t just be wished away. It has to be planned for and countered.
This is why redistribution is a core principle of progressive and socialist politics. You can’t just introduce a new tweak and wait for things to correct. You need a mechanism to actively redistribute at regular intervals. Like taxes.
In web3, there aren’t many technologies that succeed at the higher bar of actively resisting centralization. One example might be quadratic voting, which has taken off probably because it’s market-centric branding has kept it from being considered redistributive (it is).
So for now my attitude toward decentralization is “Wake me up when you have a plan to grow faster than you can be co-opted.” Wake me up when you’ve decentralized taxation.
I’m often surprised at how casual so many communities are about who they let in. To add people to your membership is to steer your community in a new direction, and you should know what direction that is. There’s nothing more powerful than a group of aligned people, and nothing more difficult than steering a group when everyone wants something different for it. I’ve seen bad decisions on who to include ruin many communities. And, on the other hand, being intentional about it can have a transformative effect, leading to inspiring alignment and collaboration. The best collaborations of my life have all been in discerning communities.
So what does it mean to be intentional about membershipping? You could say that there are two overall strategies. One is to go slow and really get to know every prospective member before inviting them fully into the fold. The other is to be very explicit and providing narrow objective criteria for membership. These both have upsides and downsides. If you spend a lot of time getting to know someone, there will be no surprises. But this can produce cliqueishness and cronyism: who else have you spent that much time with than your own friends? On the other hand are communities that base membership on explicit objective criteria can be exploited. A community I knew wanted tidy and thoughtful people, so would filter people on whether they helped with the dishes and brought desert. The thinking was that a person who does those things naturally is certainly tidy and thoughtful. But every visitor knew to bring desert and help with the dishes, regardless of what kind of person they were, so the test failed as an indicator.
We need better membershipping processes. Something with the fairness and objectivity of explicit criteria, but without their vulnerability to being faked. There are lots of ways that scholars solve this kind of problem. They will theorize special mechanisms and processes. But wouldn’t it be nice if we could select people who just naturally bring desert, help with dishes, ask about others, and so on? Is that really so hard? To solve it, we’re going to do something different.
The mechanism: the double-blind policy process with collective amnesia
Amnesia is usually understood as memory loss. But that’s actually just one kind, called retrograde amnesia, the inability to access memories from before an event. The opposite kind of amnesia is anterograde. It’s an inability to form new memories after some event. It’s not that you lost them, you never got them in the first place. We’re going to imagine a drug that induces temporary anterograde amnesia. It prevents a person from forming memories for a few hours.
To solve the problem of bad membershipping, we’re going to artificially induce it in everyone. Here’s the process:
A community’s trusted core group members sit and voluntarily induce anterograde amnesia in themselves (with at least two observers monitoring for safety).
In a state of temporary collective amnesia, the group writes up a list of membership criteria that are precise, objective, measurable, and fair. As much as possible, items should be the result of deliberation rather than straight from the mind of any one person.
They then seal the secret criteria in an envelope and forget everything.
Later, the core group invites a prospective new member to interview.
The interview isn’t particularly well structured because no one knows what it’s looking for. So instead it’s a casual wide-ranging affair involving a range of activities that really have nothing to do with the community’s values. These activities are diverse and wide-ranging enough to reveal a variety of dimensions of the prospectives personality. An open-ended personality test or two could work as well. What you need is a broad activity pool that elicits a range of illuminating choices and behaviors. These are being observed by the membership committee members, but not discussed or acted upon until ….
After the interview, a group of members sits to deliberate on the prospective’s membership, by
collectively inducing anterograde amnesia,
opening the envelope,
recalling the prospective’s words and choices and behavior over the whole activity pool,
judging all that against the temporarily revealed criteria,
resealing the criteria in the envelope,
writing down their decision, and then
forgetting everything
Later this membership committee reads the decision they came to to find out if they will be welcoming a new peer to the group.
The effect is that the candidate got admitted in a fair, systematic way that can’t be abused. Why does it work? No one knows how to abuse it. In a word, you can’t game a system if literally nobody knows what its rules are. Not knowing the rules that govern your society is normally a problem, but it seems to be just fine for membership rules, maybe because they are defined around discrete intermittent events.
Psychoactives in decision-making
If this sounds fanciful, it’s not: the sedatives propofol and midazolam both have this effect. They are common enough in the cocktails of sedatives, anesthetics, analgesics, and tranquilizers that anaesthesiologists administer during surgical procedures.
If this sounds feckless or reckless, it’s not. There is an actual heritage of research that uses psychoactives to understand decision-making. I’m a cognitive scientist who studies governance. I learned about midazolam from Prof Richard Shiffrin, a leading mathematical psychologist and expert in memory and decision-making. He invoked it while proposing a new kind of solution to a social dilemma game from economic game theory. In the social dilemma, two people can cooperate but each is tempted to defect. Shiffrin suggests that you’ll cooperate if the person is so similar to you that you know they’ll do whatever you do. He makes the point by introducing midazolam to make it so the other person is you. In Rich’s words:
You are engaged in the simple centipede game decision tree [Ed. if you know the Prisoner’s Dilemma, just imagine that] without communication. However the other agent is not some other rational agent, but is yourself. How? You make the decision under the drug midazolam which leaves your reasoning intact but prevents your memory for what you thought about or decided. Thus you decide what to do knowing the other is you making the other agent’s decision (you are not told and don’t know and care whether the other decision was made earlier or after because you don’t remember). Let us say that you are now playing the role of agent A, making the first choice. Your goal is to maximize your return as agent A, not yourself as agent B. When playing the role of agent B you are similarly trying to maximize your return.
The point is correlation of reasoning: Your decision both times is correlated, because you are you and presumably think similarly both times. If you believe it is right to defect, would you nonetheless give yourself the choice, knowing you would defect? Or knowing you would defect would you not choose (0,0)? On the other hand if you think it is correct to cooperate, would it not make sense to offer yourself the choice? When playing the role of B let us say you are given the choice – you gave yourself the choice believing you would cooperate – would you do so?
— a 2021/09/15 email
The upshot is that if you know nothing except that you are playing against yourself, you are more likely to cooperate because you know your opponent will do whatever you do, because they’re you. As he proposed it, it was a novel and creative solution to the problem of cooperation among self-interested people. And it’s useful outside of the narrow scenario it isolates. The idea of group identity is precisely that the boundaries of our conceptions of ourselves can expand to include others, so what looks like a funny idea about drugs is used by Shiffrin to offer a formal mechanism by which group identity improves cooperation.
Research at the intersection of drugs and decision-making isn’t restricted to thought experiments. For over a decade, behavioral economists in the neuroeconomics tradition have been piecing together the neurophysiology of decision-making by injecting subjects with a variety of endogenous and exogenous substances. For example, see this review of the effects of oxytocin, testosterone, arginine vasopressin, dopamine, serotonin, and stress hormones.
Compared this other work, all that’s unusual about this post is the idea of administering to a whole group instead of individuals.
Why save democracy when you can save dictatorship? | The connection to incentive alignment
This mechanism is serious for another reason too. The problem of membershipping is a special case of a much more general problem: “incentive alignment” (also known as “incentive compatibility”).
When people answering a survey tell you what they want you hear instead of the truth
When someone lies at an interview
Just about any time that people aren’t incentivized to be transparent
Those are all examples of mis-alignment in the sense that individual incentives don’t point to system goals.
That’s what’s special about double-blind policy. It’s a step in the direction of incentive compatibility for self-evaluation. You can’t lie about a question if nobody knows what was asked.
Quibbles
For all kinds of reasons this is not a full solution to the problem. One obvious problem: even if no one knows the rules, anyone can guess. The whole point of introducing midazolam into the social dilemma game was that you know that you will come to the same conclusions as yourself in the future. So just because you don’t know the criteria doesn’t mean you don’t “know” the criteria. You just guess what you would have suggested, and that’s probably it. To solve this, the double-blind policy mechanism has to be collaborative. It requires that several people participate, and that a collaborative deliberation process over many members will produce integrated or synergistic criteria that no single member would have thought of.
Other roles for psychoactives in governance design
The uses of psychoactives in community governance are, as far as I know, entirely unconsidered. Some cultures have developed ritualistic sharing of tobacco or alcohol to formalize an agreement. Others have developed ordering the disloyal to drink hemlock juice, a deadly choline antagonist. That’s all I can think of. I’m simultaneously intrigued to imagine what else is out there and baseline suspicious of anyone who tries.
The ethics
For me this is all one big thought experiment. But I live in the Bay Area, which is governed by strange laws like “The Pinocchio Law of The Bay” which states:
“All thought experiments want to go to the San Francisco Bay Area to become real.”
(I just made this up but it scans)
Hypothetically, I’m very pleased with the idea of solving governance problems psychoactives, but I’ll admit that it suffers from being awful-adjacent: It’s very very close to being awful. I see three things that could tip it over: 1) If you’re not careful it can sound pretty bad, especially to any audience that wants to hate it. 2) If you don’t know that the idea has a legitimate intellectual grounding in behavioral science, then it just sounds druggy and nuts. 3) If it’s presented without any mention of the potential for abuse then it’s naive and dangerous.
So let’s talk about the potential for abuse. The double-blind policy process with collective amnesia has serious potential for abuse. Non-consensual administration of memory drugs is inherently horrific. Consensual administration of memory drugs automatically spawns possibilities for non-consensual use. Even if it didn’t, consensual use itself is fraught, because what does that even mean? The framework of consent requires being able and informed. How able and informed are you when you can’t form new memories?
So any adoption or experimentation around this kind of mechanism should provide for secure storage and should come with a security protocol for every stage. Recording video or having observers who can see (but not hear?!) all deliberations could help. I haven’t thought more deeply than this, but the overall ethical strategy would go like this: You keep abuse potential from being the headline of this story by credibly internalizing the threat at all times, and by never being satisfied that you’ve internalized it enough. Expect something to go wrong and have a mechanism in place for nurturing it to the surface. Honestly there are very few communities that I’d trust to do this well. If you’re unsure you can do it well, you probably shouldn’t try. And if you’re certain you can do it well, then definitely don’t try.
A healthy democracy requires a citizenry full of people who have built communities, held office, and started initiatives. You can’t expertly serve and intentionally organized group if you haven’t built and nearly broken several. That is why my strategy for serving democracy is to focus on online communities, which provide this opportunity to more people than ever before. Of course, as strategy it isn’t very strategic: it boils down to “change everybody.” But it’s necessary, so you have to proceed as if it’s possible. I’m still struggling with it. But there are patterns that have pulled it off. Organizations like the Scouts (Boys and Girls Clubs of America, officially the largest paramilitary organizations in the US) happen to require all of these things, and they built a system that lets people learn in flexible mentor-driven ways. Making this poster helped me get these ideas down clearly.
There’s this old organizer wisdom that freedom is an endless meeting. How awful. Here the sprightly technologist steps in to ask:
“Does it have to be? Can we automate all that structure building and make it maintain itself? All the decision making, agenda building, resource allocating, note taking, emailing, and even trust? We can; we must”
That’s the popular hypothesis, that technology should fix democracy by reducing friction and making it more efficient. You can find it under the hood of most web technologies with social ideals, whether young or old. The people in this camp don’t dispute the need for structure and process, but they’re quick to call it bureaucracy when it doesn’t move at the pace of life, and they’re quick to start programming when they notice it sucking up their own free time. Ideal governance is “the machine that runs itself“, making only light and intermittent demands for citizen input.
And against it is the unpopular hypothesis. What if part of the effectiveness of a governance system is in the tedious work of keeping it going? What if that work builds familiarity, belonging, bonding, sense of agency, and organizing skills? Then the work of keeping the system up is itself the training in human systems that every member needs to have for a community to become healthy. It instills in every member pragmatic views of collective action and how to get things done in a group. Elinor Ostrom and Ganesh Shivakoti give a case of this among Nepali farmers when state-funds replaced hard-to-maintain dirt irrigation canals with robust concrete irrigation canals and farmer communities stopped sharing water equitably. What looked like maintaining ditches was actually maintaining an obligation to each other.
That’s important because under the unpopular hypothesis, the effectiveness of a governance system depends less on its structure and process (which can be virtually anything and still be effective) and more on what’s in the head of each participant. If they’re trained, aligned, motivated, and experienced, any system can work. This is a part of Ostrom’s “institutional diversity”. The effective institution focuses on the members rather than the processes by making demands of everyone, or “creating experiences.”
Why are organizations bad computers? Because that isn’t their only goal.
In tech circles I see a lot of computing metaphors for organizations and institutions. Looking closer at that helps pinpoint the crux of the difference between the popular and unpopular hypotheses. In a computer or a program, many gates or function are linked into a flow that processes inputs into outputs. In this framework, a good institution is like a good program, efficiently and reliably computing outputs. Under the metaphor all real-world organizations look bad. In a real program, a function will compute reliably, quickly, and accurately without having to provide permission or buy-in or interest? In an organization each function needs all those things.
So organizations are awful computers. But that’s not a problem because it’s goal isn’t to compute, but to compute things that all the functions want computed. It’s a computer that exists by and for its parts. The tedium of getting buy-in from all the functions isn’t an impediment to proper functioning, it is proper functioning. The properly functioning organization-computer is constantly doing the costly hygiene of ensuring the alignment of all its parts, and if it starts computing an output wrong, it’s not a problem with the computer, it’s a problem with the output.
If the unpopular hypothesis is right, then we shouldn’t focus on processes and structures—those might not matter at all—but on training people, keeping them aligned with each other, and keeping the organization aligned with them. It supports another hypothesis I’ve been exploring, that all governance is onboarding.
Less Product, more HR?
This way of thinking opens a completely different way of thinking about governance. Through this lens,
Part of the work of governance is agreeing what to internalize
a rule is the name of the thing that everyone agrees that everyone should internalize.
The other part of governing is creating a process that helps members internalize (whether via training, conversation, negotiation, even a live-action tabletop role playing simulation).
once it’s internalized by everyone the rule is irrelevant and can be replaced by the next rule to work on.
In this system, the constraints on the governance system depend on human limits. You need rules because an org needs to be intentional about what everyone internalizes. You’ll keep needing rules because the world is changing and the people are changing and so what to internalize is going to change. You can’t have too many rules at one time because people can’t remember too rules-in-progress at once. You need everyone doing and deciding the work together because it’s important that the system’s failures feel like failures of us rather than them.
With all this, it could be tempting to call the popular hypothesis the tech friendly one. But there’s still a role for technology in governance systems following the unpopular hypothesis. It’s just a change in focus, into technologies that support habit building, skill learning, training, onboarding, and that monitor the health of the shared agreements underlying all of these things. It encourages engineers and designers to move from the easy problems of system and structure to the hard ones of culture, values, and internalization. The role of technology in supporting self-governance can still be to make it more efficient, but with a tweak: not more efficient at arranging parts into computations, but more efficient at maintaining its value to those parts.
Maybe freedom is an endless meeting and technology can make that palatable and sustainable. Or maybe the work of saving democracy isn’t in the R&D department, but HR.
I got fascinated trying to find the most critical criticisms of Elinor Ostrom’s work, and went deeper than I’d expected. Overall, there’s a lot of hero worship (me included). For every paper that criticizes her on a point, there’s one that holds her up as conciliating or defending or representing that exact point in an especially nuanced way.
The main criticisms that are available are of two related types,
that the paradigm fails to take into account critical understandings of power and agency, and
that it is too beholden to rational choice theory and methodological individualism, two basic tenets of economics and behavioral science.
The problem with the first criticism in the work I found is that every expression of it is pretty fluffy. I found no really clear and clean example putting this shortcoming in relief, and several papers holding her work up against Econ as an example of the opposite: that her work is valuable because it succeeds at taking into account power and agency.
The problem with the second criticism is that the best expressions of it don’t actually criticize her community’s angle on it (me included), they just rely on old and well-trod criticisms of rational choice generally.
It’s a bit disappointing that after all this digging I found no deeply undermining assumption of her frameworks to shake me to the core. But it makes sense, she was pretty reasonable and hedged her claims a lot. That’s a good reason to be hard to criticize. Still, out of this whole exercise I’ve managed to come out with a third “meta” criticism of the Ostrom scholarship: the hero-worship itself. There’s a tacit hierarchy in the Ostrom community of people who can assert the legitimacy to improve and criticize her work (not just apply it), with former students and collaborators at the top, most comfortable saying she missed this or was wrong about that. It could be worse: they could be closed-circle hero-worshipping keepers of the flame, but even that hierarchy is causing problems
her frameworks change and improve slowly and in a very hard to track way (there used to be 8 design principles, now there are 10),
there’s a lot of uncritical copy/paste application of her frameworks, rather than development of them
there is the tendency to see the Ostrom’s contributions as part of the future rather than part of the past. This makes the community vulnerable to developing blind spots.
Here are the least softball critiques that I was able to find.
Cleaver F (2001) Institutional Bricolage, Conflict and Cooperation in Usangu, Tanzania. IDS Bulletin 32(4): 26–35. DOI: 10/bd765h.
Cleaver F (2007) Understanding Agency in Collective Action. Journal of Human Development 8(2). Routledge: 223–244. DOI: 10/crhdr9.
Kashwan P (2016) Integrating power in institutional analysis: A micro-foundation perspective. Journal of Theoretical Politics 28(1). SAGE Publications Ltd: 5–26. DOI: 10.1177/0951629815586877.
Mollinga PP (2001) Water and politics: levels, rational choice and South Indian canal irrigation. Futures 33(8): 733–752. DOI: 10.1016/S0016-3287(01)00016-7.
Mosse D (1997) The Symbolic Making of a Common Property Resource: History, Ecology and Locality in a Tank-irrigated Landscape in South India. Development and Change 28(3): 467–504. DOI: 10/ftdm7p.
Saravanan VS (2015) Agents of institutional change: The contribution of new institutionalism in understanding water governance in India. Environmental Science & Policy 53. Crafting or designing? Science and politics for purposeful institutional change in Social-Ecological Systems: 225–235. DOI: 10/f7rrw2.
Social-ecological systems, social diversity, and power on JSTOR (n.d.). Available at: https://www.jstor.org/stable/26269693?seq=1#metadata_info_tab_contents (accessed 29 September 2020).
Velicu I and García-López G (2018) Thinking the Commons through Ostrom and Butler: Boundedness and Vulnerability. Theory, Culture & Society 35(6). SAGE Publications Ltd: 55–73. DOI: 10/gfdbbs.
Note to self
I do have a few more substantive critiques of my own that I haven’t developed at all:
One: the design principles seem to work insofar as they create a bubble within which market exchange works (within which CPRs are excludable): so how is that an improvement on “markets for everything” ideology?
Two: she has an alignment with super libertarian public choice people in the municipality/Tiebout space that might open up some avenues for criticism.
Three: blind spot failure to integrate findings from the “soft stuff” in democratic theory, pretty much all of deliberative/participatory democracy.
Vlad Tarko adds “There’s also a critique of the design principles as being applicable only to small scale. https://jstor.org/stable/26268233”
There is a deeply baked-in assumption that when communities succeed or fail, it’s because their governance system was good or bad. Communities fail for other reasons, and other endogenous reasons (not just meteor strikes). A lot of online communities never take off in the first place, because they’re not interesting enough to users to attract the critical mass necessary for governance to be relevant. That’s not a governance failure.
