David,
Wow! Lot or history here. I am looking to hearing what Lee and Owen make of this, since there were around for some of this history. With respect to our long running discussion of metaphor, I think your story is an allegory, not a metaphor. An allegory (said he, improvising) is a story constructed of metaphors. So, “gravel” is a metaphor. “Crushing a matrix” into gravel is a metaphor, and a particularly inspiring one, at that. A story about how evil people lost the Kingdom because they crushed a matrix into gravel, now THAT’S an allegory. A metaphor contains basic and surplus meaning, and some of that surplus meaning is patently facetious. When I say that Nature Selects, the basic meaning is all the ways in barnyard breeding is known to correspond to what goes on in nature, the facetious surplus meaning is all the ways in which its known not to correspond. What remains of the surplus meaning of the metaphor when the facetious implications are identified, is called the positive heuristic. Roughly it’s the “juice” of the metaphor … all the ideas that the metaphor inspires us to explore and test with future science. Your allegory contains a whole bunch of very juicy metaphors. Nick Nicholas S. Thompson Emeritus Professor of Psychology and Biology Clark University <http://home.earthlink.net/~nickthompson/naturaldesigns/> http://home.earthlink.net/~nickthompson/naturaldesigns/ From: Friam [mailto:[email protected]] On Behalf Of Prof David West Sent: Sunday, September 11, 2016 8:57 AM To: [email protected] Subject: Re: [FRIAM] speaking of analytics - data mining Once upon a time there was "information." People loved information and kept abundant amounts of in their heads and used it as a means of commerce among themselves, sharing it and savoring it and finding profit in it. One day a new king, King Codd, conquered the realm and took all the information away from all the people. He dissembled all the information into meaningless pieces, called "data" and locked it away in an impenetrable matrix called a "schema." This required great effort, a process called "normalization," but it was, "worth it, because I can prove, mathematically', that data can be reassembled with the magic incantations of SQL." Information was thrown into the dungeons of thousands of Relational DataBase Management Systems (RDBMS), never to bee seen in its beautiful original form again. Unfortunately, it proved impossible for the people to normalize properly, Codd-Normal-Form, had no algorithm or process to assure it was achieved and no one could master SQL - the logic was simply not something that most people could master. And, if you really did achieve proper normalization, it was so inefficient it was not practical, so everyone "demoralized" their vast stores of data so they could use them, poorly and in a crippled manner, to try and get some of their beloved information back. The worst part of this story came later when the people found that the impenetrable matrix — the schema that held all their information hostage in the form of dissociated data, connected only with predefined "relationships" — made it impossible to retrieve any and all the "information" that they wanted and needed. In anguish, the people invented an entire new profession - Data Mining - that essentially 'crushed' the data stores creating gravel composed of individual datums and put the result in a different, more malleable matrix — live gravel in cement and sand and water (before the matrix dries). From this new medium the people would pluck bits of gravel and place them next to each other an proclaim, "Look! Information!" Alas, this new "information" proved to lack most of the meaning that was intrinsic to the information the people once new and loved. All the semantics had been stripped from the old information when it was first placed in the RDBMS dungeons. The new juxtapositions of datums that data miner's called 'information' rapidly proved to be a pale imitation of the original. Once a video junkie, working as a clerk at the video rental company around the corner, could make accurate and reliable predictions about what movie you might want to view next — because of all the natural information he had in his head. But now, even the great wizard, NetFlix, despite all the algorithmic prowess and all the mined data it possesses, cannot make as accurate a prediction as the teenage clerk. To this day, most of the world suffers from the massive evils perpetrated by the Wicked King Codd. Information, once abundant and freely shared with little more organization than the 'story', remains a rare and precious thing. Nick - this is my metaphor, can you discern my theory and guess how, when, where, and why I utilize that theory? dave west On Fri, Sep 9, 2016, at 12:37 PM, Nick Thompson wrote: And data “mining” is a metaphor. Now people claim to use metaphors “metaphorically”, by which they mean that they mean nothing by them. But it is my “teery”* (and it is all mine) that nobody uses a metaphor but that hizr thinking is influenced by it. The influence can be inexplicit, in which case the user is blind to its effects on himmr, or explicit, in which case the user’s imagination is enhanced by its use and less likely to be misled by its misuse. I would like to explore this “teery” using “Data Mining” as an example. How does thinking of data as encased in a non-dynamic subterranean matrix shape our (your) thinking for good or ill? *cf, Monte Python’s Flying Circus Nick Nicholas S. Thompson Emeritus Professor of Psychology and Biology Clark University <http://home.earthlink.net/~nickthompson/naturaldesigns/> http://home.earthlink.net/~nickthompson/naturaldesigns/ From: Friam [mailto:[email protected]] On Behalf Of Eric Charles Sent: Friday, September 09, 2016 11:31 AM To: The Friday Morning Applied Complexity Coffee Group <[email protected] <mailto:[email protected]> > Subject: Re: [FRIAM] speaking of analytics Marcus, That's an interesting distinction. Is it the case that by "theory" Nick was referring to something verbal and explicitly metaphorical, or would the results of data mining, which one sought to validate on a different sample, count as a "theory". So, for example, if my data mining of Marine data found that tying shoes left-to-right predicted success at Officer Candidate School, and I then went to test for that "prediction" in a later sample of incoming officer candidates, to what extent is my prediction based on "a theory". Of course, "data mining will be a useful way to uncover patterns" is itself a theory, applicable in some domains but not others (i.e., not all domains of inquiry will contain the sought after patterns in a long-term stable form). Eric ----------- Eric P. Charles, Ph.D. Supervisory Survey Statistician U.S. Marine Corps On Fri, Sep 9, 2016 at 10:51 AM, Marcus Daniels <[email protected] <mailto:[email protected]> > wrote: “I know that theories are really useful for making predictions, but can one actually make a prediction without one?” Yes, that’s what data mining is: Take a large corpus of data, find some statistically rare relationships, and then test for their predictive value on another large corpus of data. In this way one can predict things without really having any kind of theory or even domain knowledge. Marcus ============================================================ FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College to unsubscribe http://redfish.com/mailman/listinfo/friam_redfish.com ============================================================ FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College to unsubscribe http://redfish.com/mailman/listinfo/friam_redfish.com
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