I am especially interested in modeling how technology makes profound changes to the rules of the game, not just marginal changes in some parameters; So 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 indeed 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 and changed 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.
Innovation networks
A well documented example of this is the “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
This is a cute model of certain stylised types of overhypeing.
By NeedCokeNow, CC BY-SA 3.0, Link
The hype cycle itself is presumably on the plateau of productivity right now since Gartner has a steady business producing diagrams based on it.
This is Gartner’s 2020 hype cycle report
Marginal returns on research
See Kevin Bryan’s paper reviews, especially Models of Innovation I: The Patent Race has a lot of papers discussed which I should file one day > I’ve been going through some old literature on innovation again as part of a current project, so I figured I ought put up a little review of this literature. I’ll cover five strands: the patent race, the partial equilibrium/auction, the quality ladder, sequential innovation a la Scotchmer and Green, and bandit experimentation.
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 and The Singularity is not coming relate this to the question of a technological singularity.
Is science slowing down? Experiencing diminishing returns? Are good ideas getting harder to find? (Bloom et al. 2020)
Eroom’s law: Declining marginal productivity in drug research, via John D Cook
Is it just me, or does this resemble a maximal statistic, or perhaps a rarefaction curve? See Scannell et al. (2012).
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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.”
Danny Crichton, The dual PhD problem of today’s startups:
Software is so democratized today, we forget just how blisteringly difficult almost all other facets of human endeavor are to even start. A middle schooler can build and deploy a web service scalable to millions of people with some lines of code (learned from easily and widely accessible resources on the internet) and some basic cloud infrastructure tools that are designed to onboard new users expeditiously.
Try that with rocketry. Or with pharma. Or with autonomous vehicles. Or any of the interesting new frontiers with green fields that are just sitting there waiting for the taking.
Andreessen Horowitz critiques AI startup hype that claims we have a unicorn factory.
I found that the marginal returns of researchers are rapidly declining. There is what’s called a ‘standing on toes’ effect: researcher productivity declines as the field grows. Because ML has recently grown very quickly, this makes better ML models much harder to find.
His thesis Besiroglu (2020) goes into depth.
Rebranded: Danny Dorling, Slowdown: The End of the Great Acceleration—and Why It’s Good for the Planet, the Economy, and Our Lives..
Other interesting things to look at
Intellectual Property systems
Mathematical Model Reveals the Patterns of How Innovations Arise:
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
“reveal” is excessive here. But it is cute for sure.
Phillip Ball has ideas on What innovation really is
Mariana Mazzucato seems to be interesting
Would a journal of infrastructure complexity be any good?
The Myth of the Myth of the Lone Genius is a little bit angry that narratives about collective endeavour have eclipsed lone genius narratives. There is a lot of terminological wrangling. My opinions, in my own terminology, which might be compatible with the argument here and also with the argument this person is attempting to refute is “geniuses are real, I promise, I have met some. Also, they get stuff done, but in practice they get it done in a social context and sometimes that stuff is crazy and/or useless. Also teams of non-geniuses get stuff done, which can also, to be fair, be crazy and/or useless.” This summary lacks pith and invective, though.
Stagnation
Jason Crawford, Technological stagnation: Why I came around
Thiel, P. A. (2014). Zero to one: notes on startups, or how to build the future:
“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.
Sam Kriss is glum and hyperbolical as always. The Long, Slow, Rotten March of Progress:
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.
Innovative societies
Here is a quirky paper: Brian Castellani and Rajeev Rajaram, How large must a population be to accomplish great things? examine the roles of diversity and institutions in creative cities, with case studies spanning 15th Century Florence, 19th Century Paris, 21st Century San Francisco etc.
I wonder how well supported is Hartmann, Krabbe, and Spicer (2019):
What is driving the declining quality of innovation-driven entrepreneurship? In this paper, we argue the growing entrepreneurship industry is an important yet overlooked explanation. This rapidly growing industry has transformed the nature of entrepreneurship and encouraged a particular form of low-quality entrepreneurship. It has done so by leveraging the Ideology of Entrepreneurialism to mass-produce and mass-market products that make possible what we term Veblenian Entrepreneurship. This is entrepreneurship pursued primarily as a form of conspicuous consumption. Aside from lowering average entrepreneurial quality, Veblenian Entrepreneurship has a range of (short-run) positive and (medium and long-run) negative effects for both individuals and society at large. We argue that the rise of the Veblenian Entrepreneur has contributed to creating an increasingly Untrepreneurial Economy. That is an economy which superficially appears innovation-driven and dynamic, but is actually rife with inefficiencies and unable to generate economically meaningful growth through innovation.
As someone who has worked in what I cannot call a dotcom exit scam (because it did certain necessary tasks to be legally distinct from a scam and yet not ever enough to plausibly succeed) I am sympathetic to rants about this.
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