Well written and engaging, but I think there is a lot more room to really explore what the sociological, techno-economic, power dynamic etc ramifications are of specific data structures. This doesn’t go too far beyond the surface level notion of data, and there are plenty of discussions of the historical significance of taxonomizing and “objectifying” and archiving and surveilling the world into data from Foucault to Gitelman to Hui to Galloway and more.
With a title like the one given, it would be nice to see the authors try to tease out what the political implications of a linked list, a heap, a stack a directed acyclic graph or a cyclic one, a tree, a FIFO queue, a hash table, a point cloud, a data lake, actually are… it can be difficult to really wrestle with these on an interdisciplinary and simultaneously technical & sociological/organizational level, but seems worth doing.
In my PhD thesis I am doing this, but at an organizational level, how algorithms are managed and organized, i.e., via task lists, inside of the system, e.g., the debugger, or virtual meetings. There was a clear discovery of the movement away from the code, but also that we should really think about how the algorithm sees things, and how it is taking on a subjectivity on its own, as an actor.
This approach is postmodern, by describing hyper-reality, so taking in things that management theory would approach as only superficial, and putting this into the foreground. Your idea is actually brilliant, to go one step further and check the individual implementation details. I think there is some work about algorithms, but mostly for the AI case.
Feels that if anything, the author is describing computational abstractions, not data structures per se. They mention the term in the text, but then continue to say "data structure" while talking about everything except data structures. For example:
"a local cafe is no longer a community hangout but a data structure containing a menu, a list of reservation options, and a hundred 5-star ratings"
This description is focusing on the "not data structure" parts like what is there, what it means, and what can be done with it; the data structure part would be, how it's arranged and what the impact of that is.
It's still clear what the author means, and they are using the relevant terminology, just the irony is in details being named opposite to what they should be.
It's a general-audience essay, not one targeted towards the HN community. So unfortunately there's little opportunity to delve any deeper into what specific data structures are involved in holding the data and the difference that might make. There are data structures underneath in the excerpt you pulled out and they're so common in code that we don't even notice it. (Even something as simple as this: certain data structures are better for finding recent / first items and others are better for finding "top" / largest items. That has implications that ripple upward and can skew what users are shown.) It would be nice to consider the differences in how different data structures store data and their broader implications.
Sorry, my comment was harsher than it needed to be. I've struggled with belonging where most people seem to fit in, but on the other hand I've benefited greatly, I think, from the processes that make me a taxable citizen rather than a member of a community.
What we're hoping for (and is the theme of the piece) is that we are and can and should be both and more -- taxable citizens, members of illegible communities, and many more things. It's a both-and perspective -- life is and should be composed of many overlapping systems.
That sounds like a database, how is that not a data structure? And to that data structure the cafe is just a bunch of values, which he listed. That is how you see things like a data structure would.
Definition of data structure: "More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data".
Maybe you would want more generic data structures, but it is used accurately here.
Sorry! More succinctly: the authors explore politics of data, as opposed to politics of data structures. I’m curious what the latter would really look like.
The one data structure that crops up again and again when looking at complex systems is the graph. Nodes on the graph represent the components and elements like state variables (e.g. population, resources, temperature, servers and clients, etc.) of the system, and edges between nodes represent interconnections, e.g. flows of matter, energy or information between nodes.
A good introduction to this way of looking at the world is "Thinking in Systems: A Primer" by Donella Meadows. Important concepts include analyzing systems to determine their relative robustness or fragility under stress, the nature of the feedback loops in the system (possibly some nodes are connected by partially directed edges, so flows in one direction are easy but not in the other), what kind of temporal delays matter the most (e.g. how long does it take between creating a change and seeing the result of that change), and so on.
Given the natural utility of graphs in modeling systems, it's really a bit strange that graph theory really only developed in the 20th century, with some minor exceptions like Euler and Kirchhoff. It's interesting to think about an alien civilization whose mathematics began with graphs and how it might have developed.
The power to see like a state was intoxicating for government planners, corporate efficiency experts, and adherents to high modernism in general. But modern technology lets us all see like a state. And with the advent of AI, we all have the power to act on that seeing.. like a data structure.. built for and within a set of societal systems—and stories—that can’t cope with nebulosity.. things are continuous spectra, not discrete categories.. avoid being fragmented into nanogenres.
While large organizations can exist, they can’t be the only ones with access to, or ability to, afford new technologies.. We can create new “federated” networks of organizations and social groups, like we’re seeing in the open social web of Mastodon.. where local groups can have local rules that differ from, but do not conflict with, their participation in the wider whole..
The idea of having multiple identities.. some of us have gotten used to having a “portfolio career” that is not defined by a single hat that we wear. While today there is often economic precarity involved with this way of living, there need not be, and the more we can all do the things that are the best expressions of ourselves, the better off society will be.
Think of how we use weather apps, fitness apps, or self-guided museum tour apps to improve our lives. We need more tools like this in every context to help us to understand nuance and context beyond the level we have time for in our busy lives.. A tool is controlled by a human user, whereas a machine does what its designer wanted. As technologists, we can build tools, rather than machines, that flexibly allow people to make partial, contextual sense of the online and physical world around them.
> Unlike epistemic knowledge, which tends to be standardized and centralized, metis is characterized by its adaptability and diversity. It arises from the accumulated experiences of individuals within specific contexts, leading to a rich tapestry of localized knowledge systems. This inherent flexibility allows metis to evolve and respond to changing circumstances, making it highly relevant in various practical domains.
> Two decades ago, in his book Seeing Like a State, anthropologist James C. Scott explored what happens when governments, or those with authority, attempt and fail to “improve the human condition.” Scott found that to understand societies and ecosystems, government functionaries and their private sector equivalents reduced messy reality to idealized, abstracted, and quantified simplifications that made the mess more “legible” to them. With this legibility came the ability to assess and then impose new social, economic, and ecological arrangements from the top down: communities of people became taxable citizens, a tangled and primeval forest became a monoculture timber operation, and a convoluted premodern town became a regimented industrial city.
One thing that I remember from Seeing Like a State is that people used to be judged by village tribunals, and now we have fair trials at the state level. People used to live in the same place all their life, now we can go in many places. Making things more legible can mean destroying a forest by making it into a monoculture timber operation. It can also mean allowing all kind of people to live as long as they pay taxes, offering them freedom that they couldn't find in a smaller structure.
I think it's very important to remember that the map is not the territory, that unknown unknowns exist as well as known unknowns, that trying to impose to people a specific way of life will often not make them happier. But also that technology has meant better lives for most people on this planet.
This is a general cognitive problem in reducing a higher dimensional object to a lower dimensional representation for processing. For a complex entity like society and/or human wellbeing, it seems inevitable that the representation will mask important non-tangible/non-measurable dimensions of the object being considered.
Some dimensional reduction is always possible, due to the Johnson-Lindenstrauss lemma. For example, for 8 billion data points, reducing to 1400 dimensions enables preserving distances within +- 50% (that can probably be tightened a bit) regardless of what the uncompressed data is.
I get the idea, but a lot of these examples seem dubious to me. Between Facebook's advertiser interest groups and Tiktok's "uncanny" subconscious-tapping For You page, the Spotify example is pretty benign?
> Spotify sees us like a data structure when it tries to play music it thinks we will like based on the likes of people who like some of the same music we like.
I see how data structures figure into the implementation, but it's also easy to see how "music recommendations crowdsourced from people with similar taste" is a desirable goal. I'd assume that Spotify had to "restructure" its data to get this to provide better recommendations and run more efficiently.
I think you'd have a much easier time selling the dystopian/soulless vibe looking at Pandora and their Music Genome Project [0] (even if it actually provides really good recommendations, in my experience anyway).
Other examples I just don't see where the data structure is. "Thai Food Near Me" is SEO-optimized or whatever, but in the end it's just a catchy name, not really materially different from calling your shop "World's Best Cup of Coffee".
How much you want to bet one or both of these folks saw patio11's Seeing Like a Bank here on Hacker News a few months ago, read it, then read Seeing Like a State, and then wrote this?
Because I did the same thing (my version for my problem still in draft)
We started writing this essay about 3 years ago (and first read Seeing Like a State about 15 years ago -- it's a book that should be read and re-read many times). It takes time to write something this long, and if I could have I would have kept editing it for another year.
early stringent covid responses(such as in China) probably saved millions of lives in the responsive countries, but i guess post-covid will be litigated with vibes not numbers
> cross-sectional variation in response and results that disprove your claim
What do you mean? The spread was low enough during the period where it was high mortality that in China, we can lower bound at 1 million even with best case assumptions about mortality compared to the US that likely wouldn't have been true in a still developing country.
as much as I enjoy Patrick's writing, i was assuming it was solely a reference to Seeing Like a State. and yes it's a cult classic among the bay area rat/libertarian-esque folks, had never heard of it before coming here
With a title like the one given, it would be nice to see the authors try to tease out what the political implications of a linked list, a heap, a stack a directed acyclic graph or a cyclic one, a tree, a FIFO queue, a hash table, a point cloud, a data lake, actually are… it can be difficult to really wrestle with these on an interdisciplinary and simultaneously technical & sociological/organizational level, but seems worth doing.