Why books don’t work, by Andy Matuschak

Source: Why books don’t work, by Andy Matuschak

for non-fiction books, one implied assumption at the foundation: people absorb knowledge by reading sentences. This last idea so invisibly defines the medium that it’s hard not to take for granted, which is a shame because, as we’ll see, it’s quite mistaken.

Have you ever had a book like this—one you’d read—come up in conversation, only to discover that you’d absorbed what amounts to a few sentences? … I suspect this is the default experience for most readers. … Now, the books I named aren’t small investments. Each takes around 6–9 hours to read. Adult American college graduates read 24 minutes a day on average, so a typical reader might spend much of a month with one of these books. Millions of people have read each of these books, so that’s tens of millions of hours spent. In exchange for all that time, how much knowledge was absorbed? How many people absorbed most of the knowledge the author intended to convey? Or even just what they intended to acquire?

All this suggests a peculiar conclusion: as a medium, books are surprisingly bad at conveying knowledge, and readers mostly don’t realize it.

You’ve probably internalized the notion that lectures have this problem, even if the parallel claim for books feels more alien.

Books don’t work for the same reason that lectures don’t work: neither medium has any explicit theory of how people actually learn things, and as a result, both mediums accidentally (and mostly invisibly) evolved around [an implicit] theory that’s plainly false. … that model is transmissionism

Rather than “how might we make books actually work reliably,” we can ask: How might we design mediums which do the job of a non-fiction book—but which actually work reliably?

Cognition all the way down | Aeon

Source: Cognition all the way down | Aeon, by Michael Levin and Daniel C Dennett, edited by Nigel Warburton

Biology’s next great horizon is to understand cells, tissues and organisms as agents with agendas (even if unthinking ones)

Isaac Newton’s laws are great for predicting the path of a ball placed at the top of a hill, but they’re useless for understanding what a mouse at the top of a hill will do. So, the other way to make a mistake is to fail to attribute goal-directedness to a system that has it; this kind of teleophobia significantly holds back the ability to predict and control complex systems because it prevents discovery of their most efficient internal controls or pressure points.

In a phrase that will need careful unpacking, individual cells are not just building blocks, like the basic parts of a ratchet or pump; they have extra competences that turn them into (unthinking) agents that, thanks to information they have on board, can assist in their own assembly into larger structures, and in other large-scale projects that they needn’t understand.

Agents, in this carefully limited perspective, need not be conscious, need not understand, need not have minds, but they do need to be structured to exploit physical regularities that enable them to use information (following the laws of computation) to perform tasks, beginning with the fundamental task of self-preservation, which involves not just providing themselves with the energy needed to wield their tools, but the ability to adjust to their local environments in ways that advance their prospects.

the point is not to anthropomorphise morphogenesis – the point is to naturalise cognition. There is nothing magic that humans (or other smart animals) do that doesn’t have a phylogenetic history. Taking evolution seriously means asking what cognition looked like all the way back. Modern data in the field of basal cognition makes it impossible to maintain an artificial dichotomy of ‘real’ and ‘as-if’ cognition. There is one continuum along which all living systems (and many nonliving ones) can be placed, with respect to how much thinking they can do.

It’s all about goals: single cells’ homeostatic goals are roughly the size of one cell, and have limited memory and anticipation capacity. Tissues, organs, brains, animals and swarms (like anthills) form various kinds of minds that can represent, remember and reach for bigger goals. This conceptual scheme enables us to look past irrelevant details of the materials or backstory of their construction, and to focus on what’s important for being a cognitive agent with some degree of sophistication: the scale of its goals. Agents can combine into networks, scaling their tiny, local goals into more grandiose ones belonging to a larger, unified self. And of course, any cognitive agent can be made up of smaller agents, each with their own limits on the size and complexity of what they’re working towards.

How “Money Printing” Works, and how to Spot Inflation, by Lyn Alden

Source: How “Money Printing” Works, and how to Spot Inflation, by Lyn Alden

The crux of this article is that quantitative easing on its own, and quantitative easing combined with massive fiscal deficits, are two very different situations to consider when it comes to analyzing the possibilities between inflation and deflation, and what constitutes “money printing”.

Base money vs. broad money supply
Bank lending, bank reserves, and QE (quantitative easing)
Deflationary forces

QE alone, where the Fed buys existing assets mostly from banks, is simply anti-deflationary, to recapitalize a banking system and fill it up with excess reserves. It’s not outright inflationary because it doesn’t directly increase the broad money supply. If the Fed buys existing assets from non-banks, it only increases broad money a bit, around the margins.

Meanwhile, large fiscal deficits funded by QE (the central banks monetizing deficit spending by buying any of the excess Treasuries over the real demand for them), actually is pro-inflationary, because it gets money directly into the economy, into the broad money supply, and can be done with no limit except for inflation that it would eventually cause when done to excess.