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.

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.

Useful framing of quantification. A few excerpts:

  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.

https://en.wikipedia.org/wiki/Seeing_Like_a_State

> 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.

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".

[0] https://en.wikipedia.org/wiki/Music_Genome_Project

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)

https://www.bitsaboutmoney.com/archive/seeing-like-a-bank/

https://news.ycombinator.com/item?id=38180477

Also, Seeing Like a State seems to be something of an cult classic on this forum: https://www.google.com/search?q=seeing+like+a+state+site%3Ay...

Ted Nelson was talking about this decades ago. Prophetic.

I can't handle the way this is written at all - it uses a bunch of jargon that I find meaningless. LLMs to the rescue!