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.
The traditional definition, which focuses on longitudinal contingencies (and is likely also the definition Barnard had in mind), is the traditional purview of production – in today's parlance: supply chain, typically captured in machine scheduling models.
The modern definition, ascribed to Schelling, focuses on synchronous (mis)alignment in the absence of communication. There is a standard game known as "Pure Coordination", but it comes with variants usually summarized as coordination games.
Just to name some of the most common variants:
Battle of the sexes is coordination with goal conflict.
Stag hunt is coordination under nonparticipation risk.
Tragedy of the commons is anti-coordination or crowding game.
The world of coordination games is rich and fascinating, but most of it hinges on understanding the context. The original building block is actually quite boring. Written in a particular way, it is a Prisoner's Dilemma with the temptation of selfish motives removed ("slashed").
The reason why it's boring is that as a one-shot game it has an obvs dominant, symmetric, Nash, Pareto-optimal strategy set CC. It only becomes interesting if we add context, e.g. how do we get from DD to CC without communicating?
In the world of operations research, the problem of "D-to-C" is known as machine replacement problem. In economics, the problem of coordinated machine replacement has an even wider scope. It's also known as Innovation.
A reason for this is that coordination underpins the important concept of mutuality (of the OEW trifecta conflict-mutuality-order). Mutual action is always preferred by all parties. Except the risks and rewards of finding such a mutual solution are not always equally distributed.
Sometimes symmetry or asymmetry of risks and rewards can be the driver, and sometimes an obstacle to this process of reaching a higher metastable state called "innovation". See the penguins dilemma.
From this it is just a small step to realizing that timing is a major driver of coordination success or failure, and thus we're back to the Barnard-type coordination as intertemporal concatenation of activities.
But what is a Barnard concatenation failure? We experience it every day when we take multiple buses or trains to reach a destination. If we have a sequence of buses 1-2-3-4, only one has to be late for our whole trip to be out of whack. Another word for this is a Forrester shock.
A key point is that the cost of such a system failure can dwarf the individual cost of a single failure due to accelerating upstream and downstream knock-on effects (externalities). This gave rise to concepts like opportunism and holdup. And a resolution concept called contract.
At the juncture of Barnard's longitudinal concatenation and Schelling's simultaneous synchronization we have a different set of ideas on how to coordinate: The Hayek-Coase spontaneous, polycentric or decentralized coordination. In OR we would call it multi-machine scheduling.
Whether it was expanding industrialization or transportation or the emerging socialist calculation debate, the folks at LSE realized that coordination happens at the intersection between contingent (concatenated) and simultaneous action. Tom Schelling would've agreed.
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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.
This is basically it. "Capital stock" in a production context means a jumble of machinery that needs to be put into a certain shape in order to be productive. The discipline for that is called machine scheduling. That's combinatorial optimization. That's operations research.
In order to translate "capital" from the production domain ("capital stock") to the finance domain ("capital structure") you need a transformation from the physical object to a fair expression of the value of the physical object. The name of that transformation is accounting.
So there is basically nothing that needs to be resolved. This is undergrad knowledge for anyone who has ever taken business and economics or operations research and economics together. The only way this could create any confusion is if your knowledge is so compartmentalized...