The Living Thing / Notebooks :


on the invention of self-moving goalposts

Usefulness: 🔧
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Uncertainty: 🤪 🤪 🤪
Incompleteness: 🚧 🚧 🚧

See also Intellectual Property.

I am especially interested in modeling how technology changes the rules of the game, as opposed to marginally changes some parameters; not, say residual stochastic shocks (in the “Real Business Cycle” models), or as the slope of a marginal cost of production curve (in textbook microeconomics). That is, technological innovation that leads to a qualitative, rather than incremental, change in the state of play – respecting that a lot of marginal changes might in fact lead to major qualitative changes.

In recognition of that emphasis, I briefly called this entry “disruptive technology” instead of mere “innovation”, but then I felt like a TED speaker and woke up sweating in the night to change it.

To consider

This is at the very limit of modelability, surely?. The introduction of a new technology has many components, from social uptake, to supply chains, to the discovery process. The unexpected interactions with the other technologies out there. The internal combustion engine changed more than just transit times. The computer network altered more than just mail delivery times.

The cascade of effects from any one alteration is, it is likely, unknowable in advance, but might have some regularities, or at least some kind of underlying set of distributions as a stochastic process – some kind of branching process perhaps? Fixation processes, by analogy with evolutionary theory?

Where did the industrial revolution come from?

Gregory Clark and Julia Galef in podcast conversation: What caused the industrial revolution?:

the timing in 1770 in Britain makes it very, very difficult to explain the industrial revolution. The reason for that is that Britain at that time was institutionally a very stable society, and essentially had very little institutional change in the previous 80 years. When you’re trying to explain this event, it’s occurring against the kind of unchanged background of a society… with stable institutions. Very small government that mainly exists to fight more abroad. You have very stable wages within the society, they’re really not changing, the cost of capital was not changing. [..] It’s an economic environment which just looks very flat. Suddenly, in the middle of all of this, you’ve got this transforming event occurring.

“Product space” model

Due originally to Hidalgo and Hausmann, and made purportedly more rigorous by Caldarelli et al.

Considers products and nations in a bipartite graph, and does various network statistics upon it.

Attempts to be predictive about the “natural level” of a country’s GDP.

(c.f. Felix Reed-Tsochas’ affinity for such graphs, har har) Note that there is an implicit third part in the graph, to whit “capabilities”, which represent infrastructure to manufacture products.

Frank Schweitzer et al have a similar notion of inter-firm R&D networks which may be related? See references.

random idea: Estimating number of SKUs as a surrogate for divisions of a modern economy a la Beinhocker (lots of research into this because of Long Tail theories, though the primary data is rarely included – might chase this.)

Hype cycle

Should mention this, despite nonverifiability etc.

Marginal returns on research

Moore’s law versus Eroom’s law governing trends in marginal research productivity. What does the paucity of new drugs mean? Is this the same as the problem in science?

Is science slowing down? Experiencing diminishing returns?

Eroom’s law, via John D Cook:

Declining marginal productivity in drug research, from the Scanelli et al paper.

Other interesting things to look at

“Creative cities”

Here is a methodooligcally quirky paper: Brian Castellani and Rajeev Rajaram, How large must a population be to accomplish great things? A question for social complexity scholars.


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