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Let's Go Surfing
Spent a good portion of the day today in Google Reader, reading feeds and watching embeded vids. While I don't do these marathon Google Reader sessions as much as I used to, it's still nice to veg out on the internet for a whole day every once in a while.
Let's Go Surfing
-- Sent from my Palm Pre
Unless adequate meaning can be conveyed by telecommunications, information exchange will involve the movement of people. An effective transportation network will allow a high proportion of required information exchange to take place via short walks (say < 10 minutes each way) with secondary information exchange; an intermediate proportion to take place by moderate overhead mechanical transport (say < 30 minutes each way); and only a small proportion requiring high overhead mechanical transport (say from 30 minutes to 1 hour each way). Journeys that occupy in total much of a working day will in general be ruled out. The distribution of both pathlengths and journey times should follow an inverse-power scaling law favoring the small scale -- where the number of paths is inversely proportional to their length .
Basically near trips you can make several of, larger distance trips you are likely to only want to make one or two of on a given day, and much larger trips that are lengthy enough (say 2hrs plus) that you are unlikely to do them often.
The above clip is focused on walking around a city. I don't live in a walking city. I live in suburbia, but I don't see any reason the above wouldn't apply to cars as well. It's the time commitment rather than the distance that would seem to be the real principle in play.
Just as an exercise, I used a radius tool page built on Google Maps to plot 5 miles (10 minutes at 40 miles an hour) and 20 miles (30 minutes at 40 miles an hour)
I found it to be a interesting visual, of how far people are likely to travel and what level of effort it represents psychologically.
Also in the same article was a section discussing the working of a city like a brain and not a computer.
There are two possible information architectures for a complex system. One is the von Neumann architecture with a memory/processing separation supporting unambiguous information exchange, in which functionality is explicitly controlled. The other is the recommendation architecture with a clustering/competition separation supporting meaningful yet slightly ambiguous information exchange, in which functionality is defined heuristically [8, 14]. A competition subsystem interprets the outputs of submodules as a range of alternative behaviors, and quickly selects one of the alternatives. This process depends critically on consequence feedback to determine appropriate system behavior.
Since reading the above, I've been noodling around the above concepts comparing the idea of structures that should be established to guide routine non-changing work vs the different structures needed for dynamic constantly evolving work. So this paper has sparked some ideas on that topic for me as well.