If you had Ames, Iowa on your bingo card for hotbeds of innovation research — Bingo. Indeed the technology adoption curve (aka the most important curve in history) was invented by rural sociologists at Iowa State College some 70 years ago.* A little history.
WW2 put pressure on US food supplies, and a variety of technological innovations in agriculture promised improved yields. The rural sociologists were mostly concerned how these improved technologies could be made attractive to farmers, a notoriously skeptical bunch.
In the 1940s two researchers at ISC, Bryce Ryan and Neal Gross, conducted field research on how Iowa famers adopted the novel hybrid seed corn. Their report already contained the key ingredients: risk preferences, social contagion, geographic spread, and the logistic curve.
Early work focused on the individual decision process and mapped it into 5 deliberation stages: Awareness, Interest, Evaluation, Trial, Adoption; summarized in a special report called "The Diffusion Process" by ISC sociologists Joe Bohlen and George Beal in 1957.
An accompanying pamphlet called "How farm people accept new ideas" already proposed to speed up the adoption process by seeding the learning network with a new type of adoption leader, or, as we would call them today: social influencer.
In their report, George Beal and Joe Bohlen already delineated the means of communication by which the various types of adopters, clustered by risk preference into five categories, could be reached: from friends and neighbors to salesmen, mass media, and govt agencies.
One of George Beal's students at ISC, Everett Rogers, became a professor Ohio State. In 1962 he published a book based on his dissertation called "The Diffusion of Innovations", which became one of the most-cited publications in the social science.
Rogers also pointed out the role of the "invisible college", researchers at various institutions sharing a paradigmatic framework, in the diffusion of new knowledge. In the case of the adoption curve itself, that college was mostly made up of Midwestern land grant schools.
Ryan and Gross did their early work under the head of the department of economics and sociology, Theodore W. Schultz, the 1979 Nobelist and still the only agricultural economist to receive the prize. Schultz himself was very interested in how farmers picked up new knowledge.
But Schultz left Iowa State in 1943 over the fallout of what has become known as the oleomargarine wars, when he took on the powerful Iowa dairy industry. He joined the U Chicago econ program and steered it to national fame, in what might be called the "margarine revolution".
So it was left to Schultz's PhD student Zvi Griliches to work out the economic angle of the technology adoption process, which he did with his 1957 Econometrica article "Hybrid corn: an exploration in the economics of technological change", focusing on geographic spread.
Everett Rogers later joined the Stanford faculty, which is likely where Geoffrey Moore discovered the adoption curve and added his own "chasm", all while failing to mention those whose intellectual efforts preceded his own.
*As Valente & Rogers point out in their history of the adoption curve, Ames, Iowa might only be the origin of the US research, and the true inventor was more likely French professor Gabriel Tarde with his 1903 book, "The Laws of Imitation".
A few notes on coordination, adaptation, and the role of institutions.
"Both the organization theorist Chester Barnard and the economist Friedrich Hayek took adaptation to be the main purpose of economic organization, but with differences." — Oliver Williamson Nobel Lecture
Unlike cooperation, which has taken on the meaning of "solving the Prisoner's Dilemma" and collaboration, which just generically means "working together", coordination suffers from being semi-defined, which often means one has to divine the type of coordination implied.
Based on a very useful pointer from @petergklein, it turns out the meaning of coordination has shifted over time, from longitudinal alignment (concatenation) to simultaneous alignment.
While we're killing time I can translate and summarize a podcast talk I gave in 2017 (in German) on how Donald Trump confounded the poll aggregators and won the election. Because clearly the message still hasn't gotten across, or we wouldn't still be waiting. 1/♾
The story started for me in March 2016 when a coworker predicted during lunch that Trump (still mostly a joke then) would win the election. I proposed that the only way for him to do it would be to stay on message and at the same time discourage Clinton voters from voting.
If you remember, a lot of pundits assumed that Trump was playing an outsider role to clear the field during the primaries and then would turn more "presidential" in order to appeal to the centrist establishment voters. As has been the masterplan for decades.
Very interesting thread about the relevance of Coase's Nature of the Firm for the platform age. I've written a few things about how working on a dynamic ridesharing* service in 2009 helped me connect Coase/Williamson to digital business models, so here's a few summary comments.
The current focus of the public debate is the often contentious relationship between drivers and platform, but in order to understand the structure of the industry, it's also important to look at the underlying relationship, namely that between driver and passenger.
A key problem we had to consider when conceptualizing our service was how to safeguard both drivers and passengers. Turns out when you look into the history of taxicab and limo services, it is fraught with what we like to call "contractual hazards"...
Hospital routing, hospitals in crisis, empty hospitals, whole hospital systems facing collapse. A short thread.
There are currently lots of seemingly conflicting news items about hospitals like the ones above. The dissonance is a sign of an information cascade.
Finding the right balance between patient needs: give sick patients the best care available quickly, and hospital needs: have all resources in place to provide this care, is the ultimate determining factor in a successful response to Corona.
It's what I look at first.
The shorthand for this is "hospital routing": the many complex decisions, in the government and on the ground in the hospitals, about which patients to admit, and which patients to send into home quarantine.
A short thread on graph theory and network science. Both have long histories, but I'll focus on two people: Frank Harary, the "godfather of modern (American) graph theory", and Duncan Watts, the "reinventor" of network sociology, which morphed into network science.
The idea that all kinds of bilateral relationships can be simplified to some kind of network structure is quite old (Euler's Königsberg bridge problem is canonical), but for the longest time it was considered "toy mathematics" at best.
Most academics should probably not publish more than 3-5 pieces in their lives. One of the few exceptions was Frank Harary at the University of Michigan, who was on a mission to show the world that everything can be explained with dots and lines: graphs.
Machine scheduling, operations research, production, and machine learning. An anecdote thread for @sidwindc.
Biographical background: from age 15 to about 21, I spent my summers working for the laboratory equipment manufacturer that also employed my dad (in "construction").
After that I moved to prepoduction at Siemens, also for healthcare equipment, mostly coding production and tooling processes in CNC. This is where I stayed until my master's thesis.
For my master's thesis, and this is the topic of this thread, I moved to what is now Novartis in Basel into the scheduling group. This was both moving from a blue collar to a PhD level white collar environment and from the Bundesliga to the Champions League.