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.
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.)
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? The difficulties here form the basis of François Chollet’s arguments against the likelihood of a hard AI singularity.
Is science slowing down? Experiencing diminishing returns?
Eroom’s law, via John D Cook:
Do technological improvements primarily result in lower prices for consumers or in higher profits for producers? If producers are able to capture (or appropriate) most of the social returns to innovation, then profits will rise and prices will fall relatively little.
How much of the profits from a new technology are captured by innovators will vary greatly across industries. For sectors where knowledge is in the public domain, such as weather forecasting, the new knowledge cannot be appropriated and productivity improvements are passed on in lower prices. In other industries with well-defined products and strong patents, such as pharmaceuticals, producers may be successful in capturing a large fraction of social gains in “Schumpeterian profits.”
Other interesting things to look at
So Loreto, Strogatz, and co have modified Polya’s urn model to account for the possibility that discovering a new color in the urn can trigger entirely unexpected consequences. They call this model “Polya’s urn with innovation triggering.” [..they] then calculate how the number of new colors picked from the urn, and their frequency distribution, changes over time. The result is that the model reproduces Heaps’ and Zipf’s Laws as they appear in the real world
Phillip Ball on What innovation really is
Mariana Mazzucato seems to be interesting
Would a journal of infrastructure complexity be any good?
Source of the alarming graphic above.
Is it just me, or does this resemble a maximal statistic, or perhaps a rarefaction curve?
Thiel, P. A. (2014). Zero to one: notes on startups, or how to build the future (First edition.). New York: Crown Business.
“Thiel begins with the contrarian premise that we live in an age of technological stagnation, even if we’re too distracted by shiny mobile devices to notice. Information technology has improved rapidly, but there is no reason why progress should be limited to computers or Silicon Valley. Progress can be achieved in any industry or area of business. It comes from the most important skill that every leader must master: learning to think for yourself.
Doing what someone else already knows how to do takes the world from 1 to n, adding more of something familiar. But when you do something new, you go from 0 to 1. The next Bill Gates will not build an operating system. The next Larry Page or Sergey Brin won’t make a search engine. Tomorrow’s champions will not win by competing ruthlessly in today’s marketplace. They will escape competition altogether, because their businesses will be unique.”
I gather this is re-introducing Austrian economics to the silicon valley age. The sting will be in the policy prescriptions.
The vexing Sam Kriss is back, being glum and hyperbolically amusing as always. The Long, Slow, Rotten March of Progress:
Desperation is everywhere; exhibitors make lunging grabs for any passers-by wearing an “INVESTOR” lanyard, proffer stickers and goodies, scream for attention on their convention-standard signs. These do not, to put it kindly, make a lot of sense. “Giving you all the tools you need to activate and manage your influencer marketing relationships,” promises one. “Leverage what is known to find, manage, and understand your data,” entices another. The gleaming technological future looks a lot like a new golden age of hucksterism. It’s networking; the sordid, stupid business of business; pressing palms with arrogant pricks, genuflecting to idiots, entirely unchanged by the fact that this time it’s about apps and code rather than dog food or dishwashers.
None of these start-ups are doing anything new or interesting. Which shouldn’t be surprising: how often does anyone have a really good idea? What you actually get is just code, sloshing around, congealing into apps and firms that exist simply to exist. Uber for dogs, GrubHub for clothes, Patreon for sex, Slack for death, PayPal for God, WhatsApp for the spaceless non-void into which a blind universe expands.
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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