—“Most people won’t understand the basis for [the Propertarian] legal theory, and it will need explanation in mythological terms. To the people who require this form of explanation it will essentially be a religion.”– Eric Orwoll You know, sometimes you just need someone to reframe it for you. Thanks Eric. That’s smart. You could ahve told me that three years ago and saved me six months… lol
Theme: Measurement
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Bottom Up, Top Down
BOTTOM UP, TOP DOWN Sometimes operational before descriptive, and sometimes descriptive before operational. by Dan Fodor I sometimes get ‘operational’ before I get ‘descriptive’ : I can spend hours running “simulations” of the math problem I’m trying to solve in my head (simple ex: visualize a cube to deduce its properties). This gets problematic if I forget to eat or forego attention to various mundane details around me. Anyway, the point is, when getting descriptive (or when passing from operational to descriptive), I need the lenience to speak vaguely (even if only to myself) before I can speak clearly.I suspect this is true for any new concept. Something must first be thought of before it can be spoken of. (a subtle bit of genius)
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Bottom Up, Top Down
BOTTOM UP, TOP DOWN Sometimes operational before descriptive, and sometimes descriptive before operational. by Dan Fodor I sometimes get ‘operational’ before I get ‘descriptive’ : I can spend hours running “simulations” of the math problem I’m trying to solve in my head (simple ex: visualize a cube to deduce its properties). This gets problematic if I forget to eat or forego attention to various mundane details around me. Anyway, the point is, when getting descriptive (or when passing from operational to descriptive), I need the lenience to speak vaguely (even if only to myself) before I can speak clearly.I suspect this is true for any new concept. Something must first be thought of before it can be spoken of. (a subtle bit of genius)
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BOTTOM UP, TOP DOWN Sometimes operational before descriptive, and sometimes desc
BOTTOM UP, TOP DOWN
Sometimes operational before descriptive, and sometimes descriptive before operational.
by Dan Fodor
I sometimes get ‘operational’ before I get ‘descriptive’ : I can spend hours running “simulations” of the math problem I’m trying to solve in my head (simple ex: visualize a cube to deduce its properties).
This gets problematic if I forget to eat or forego attention to various mundane details around me. Anyway, the point is, when getting descriptive (or when passing from operational to descriptive), I need the lenience to speak vaguely (even if only to myself) before I can speak clearly.I suspect this is true for any new concept.
Something must first be thought of before it can be spoken of.
(a subtle bit of genius)
Source date (UTC): 2018-06-10 10:34:00 UTC
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“Curt do you believe in the notion of a universally verifiable truth?”—Mark Jo
—“Curt do you believe in the notion of a universally verifiable truth?”—Mark Joyner
(FWIW apparently this post was interpreted by mark as offensive. I didn’t mean it to be.)
Um. You probably can’t comprehend how …. sophomoric that question is, because it’s so common a sophomoric question that like belief in flying donkeys it’s a given.
1) A person may speak truthfully… if you know what that means:
For every phenomenon there exists a most parsimonious description possible in a language that can be uttered by man.
To state the most parsimonious description of possible one needs perfect knowledge.
We are rarely if ever possessed of perfect knowledge. When we are, it is all but certain we speak of a tautology or a triviality (reductio) – and meaningless.
So even if we speak the most parsimonious description possible we may not know we do, and as such must assume our description is forever contingent.
Ergo all *testimony* (truth claim) of any substance is forever contingent.
2) We can speak in at least three categories: axiomatic, theoretic, and fictional(analogistic).
We can verify the internal consistency of an axiomatic statement, and we can attempt to construct of proof of such an axiomatic statement – assuming that the axioms themselves are internally consistent. We can declare axioms. We call internally consistent tests ‘true’ but they are merely proofs, not truths. Mathematics is axiomatic. They are only contingent upon the declared axioms.
We can only try to falsify the theoretical, and see if it survives falsification. We cannot declare laws, only discover them. We call theories (descriptions) true if they are consistent, correspondent, possible, complete, and coherent. This is a far higher standard that the must ‘simpler’ axiomatic. Real world phenomenon are theoretic.
We do not recognize the need to test the internal consistency or external correspondence (operational possibility) or coherence of fictions (analogies). Imaginary phenomenon only need be meaningful, nothing else.
One can verify the existence of evidence. But this tells us only that the evidence exists and therefore claims are not false. It does not tell us that the theory is true.
So, one does not ‘verify’ a truth proposition, only a test of internal consistency of axioms. One tests the survivability of a theory. Because it is forever contingent.
Hence why we have juries.
Source date (UTC): 2018-06-09 20:28:00 UTC
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The Trick to Understanding Statistics Isn’t Math – Its ‘markets’ (competition in Equilibration)
THE TRICK TO UNDERSTANDING STATISTICS ISN’T MATH – ITS ‘MARKETS’ (COMPETITION IN EQUILIBRATION) There is nothing in genetic charts that requires mathematics to understand, just like there are no mathematical statements that cannot be expressed in ordinary (natural) language, and therefore understandable. The vast majority of genetics is nothing other than statistical analysis. The vast majority of statistical analysis is a list of single-regression analysis (set of variables), and then organizing those ‘lines’ into supply demand curves. It’s the second part – supply demand curves – rather than trying to produce a single line (distribution) using complex mathematics that (a) leads to errors and (b) is so prominent in the data. Some of us intuitively understand this, or have been educated in markets or economics or the competition of life, or the competition of evolution such that we are not so easily fooled. But the average person still operates by intuition considering himself as the standard unit of measure when interpreting data – which is precisely the same as creating a complex series of regression analysis in an attempt to produce a single statement. Think about that a bit.
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The Trick to Understanding Statistics Isn’t Math – Its ‘markets’ (competition in Equilibration)
THE TRICK TO UNDERSTANDING STATISTICS ISN’T MATH – ITS ‘MARKETS’ (COMPETITION IN EQUILIBRATION) There is nothing in genetic charts that requires mathematics to understand, just like there are no mathematical statements that cannot be expressed in ordinary (natural) language, and therefore understandable. The vast majority of genetics is nothing other than statistical analysis. The vast majority of statistical analysis is a list of single-regression analysis (set of variables), and then organizing those ‘lines’ into supply demand curves. It’s the second part – supply demand curves – rather than trying to produce a single line (distribution) using complex mathematics that (a) leads to errors and (b) is so prominent in the data. Some of us intuitively understand this, or have been educated in markets or economics or the competition of life, or the competition of evolution such that we are not so easily fooled. But the average person still operates by intuition considering himself as the standard unit of measure when interpreting data – which is precisely the same as creating a complex series of regression analysis in an attempt to produce a single statement. Think about that a bit.
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THE TRICK TO UNDERSTANDING STATISTICS ISN’T MATH – ITS ‘MARKETS’ (COMPETITION IN
THE TRICK TO UNDERSTANDING STATISTICS ISN’T MATH – ITS ‘MARKETS’ (COMPETITION IN EQUILIBRATION)
There is nothing in genetic charts that requires mathematics to understand, just like there are no mathematical statements that cannot be expressed in ordinary (natural) language, and therefore understandable.
The vast majority of genetics is nothing other than statistical analysis.
The vast majority of statistical analysis is a list of single-regression analysis (set of variables), and then organizing those ‘lines’ into supply demand curves.
It’s the second part – supply demand curves – rather than trying to produce a single line (distribution) using complex mathematics that (a) leads to errors and (b) is so prominent in the data.
Some of us intuitively understand this, or have been educated in markets or economics or the competition of life, or the competition of evolution such that we are not so easily fooled.
But the average person still operates by intuition considering himself as the standard unit of measure when interpreting data – which is precisely the same as creating a complex series of regression analysis in an attempt to produce a single statement.
Think about that a bit.
Source date (UTC): 2018-06-07 07:30:00 UTC
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SMARTEST CITIES BY ‘DEGREES’ (note: degrees are a terrible proxy for IQ unless l
SMARTEST CITIES BY ‘DEGREES’
(note: degrees are a terrible proxy for IQ unless limited to STEM+LAW degrees. Most of these cities have high numbers of women with ‘fake degrees’, which is what skews the numbers. )
#1, Boston, Mass. Daily Beast IQ Score: 176.68 2009 rank: 3 Metropolitan area population: 4,588,680 Bachelor’s degrees: 24% Graduate degrees: 18% Year-to-date adult nonfiction booksales: 7,031,000
#2, Hartford, Conn. Daily Beast IQ Score: 159.98 2009 rank: 6 Metropolitan area population: 2,044,004 Bachelor’s degrees: 19% Graduate degrees: 15% Year-to-date adult nonfiction booksales: 2,263,000 (This is the most laughable here, since hartford is a “sh—hole city”, the seat of government, and full of ‘education’ and other ‘degrees’ (trade certificates) )
#3, San Francisco Bay Area, Calif. Daily Beast IQ Score: 156.69 2009 rank: 2 Metropolitan area population: 6,157,736 Bachelor’s degrees: 26% Graduate degrees: 17% Year-to-date adult nonfiction booksales: 7,785,000
#4, Raleigh/Durham, N.C. Daily Beast IQ Score: 148.36 2009 rank: 1 Metropolitan area population: 1,627,055 Bachelor’s degrees: 27% Graduate degrees: 16% Year-to-date adult nonfiction booksales: 1,913,000
#5, Denver, Colo. Daily Beast IQ Score: 146.70 2009 rank: 5 Metropolitan area population: 2,554,474 Bachelor’s degrees: 25% Graduate degrees: 13% Year-to-date adult nonfiction booksales: 4,040,000
#6, Seattle, Wash. Daily Beast IQ Score: 141.69 2009 rank: 7 Metropolitan area population: 3,407,848 Bachelor’s degrees: 24% Graduate degrees: 13% Year-to-date adult nonfiction booksales: 5,154,000
#7, Austin, Texas Daily Beast IQ Score: 140.01 2009 rank: 12 Metropolitan area population: 1,705,075 Bachelor’s degrees: 26% Graduate degrees: 13% Year-to-date adult nonfiction booksales: 1,553,000
#8, Minneapolis-St. Paul, Minn. Daily Beast IQ Score: 138.34 2009 rank: 4 Metropolitan area population: 3,269,814 Bachelor’s degrees: 25% Graduate degrees: 12% Year-to-date adult nonfiction booksales: 3,275,000
#9, Washington, D.C. Daily Beast IQ Score: 130.05 2009 rank: 7 (tie) Metropolitan area population: 5,476,241 Bachelor’s degrees: 25% Graduate degrees: 23% Year-to-date adult nonfiction booksales: 7,356,000
#10, Rochester, N.Y. Daily Beast IQ Score: 126.65 2009 rank: 26 Metropolitan area population: 1,035,566 Bachelor’s degrees: 19% Graduate degrees: 13% Year-to-date adult nonfiction booksales: 745,000
#11, Portland, Ore. Daily Beast IQ Score: 125.02 2009 rank: 9 Metropolitan area population: 2,241,913 Bachelor’s degrees: 22% Graduate degrees: 12% Year-to-date adult nonfiction booksales: 2,936,000
#12, Kansas City, Missouri Daily Beast IQ Score: 124.98 2009 rank: 17 (tie) Metropolitan area population: 2,066,732 Bachelor’s degrees: 21% Graduate degrees: 11% Year-to-date adult nonfiction booksales: 1,455,000
#13, Salt Lake City, Utah Daily Beast IQ Score: 123.36 2009 rank: 14 Metropolitan area population: 1,130,293 Bachelor’s degrees: 20% Graduate degrees: 10% Year-to-date adult nonfiction booksales: 2,699,000
#14, Philadelphia, Penn. Daily Beast IQ Score: 123.34 2009 rank: 11 Metropolitan area population: 5,968,252 Bachelor’s degrees: 19% Graduate degrees: 13% Year-to-date adult nonfiction booksales: 6,151,000
#15, Milwaukee, Wisc. Daily Beast IQ Score: 121.66 2009 rank: 15 Metropolitan area population: 1,559,667 Bachelor’s degrees: 20% Graduate degrees: 11% Year-to-date adult nonfiction booksales: 1,340,000
#16, New York, N.Y. Daily Beast IQ Score: 120.02 2009 rank: 13 Metropolitan area population: 19,069,796 Bachelor’s degrees: 21% Graduate degrees: 15% Year-to-date adult nonfiction booksales: 18,831,000
#17, Cleveland, Ohio Daily Beast IQ Score: 119.99 2009 rank: 31 (tie) Metropolitan area population: 2,091,286 Bachelor’s degrees: 17% Graduate degrees: 10% Year-to-date adult nonfiction booksales: 2,024,000
#18, San Diego, Calif. Daily Beast IQ Score: 116.68 2009 rank: 20 (tie) Metropolitan area population: 3,053,793 Bachelor’s degrees: 22% Graduate degrees: 13% Year-to-date adult nonfiction booksales: 2,624,000
#19, Columbus, Ohio Daily Beast IQ Score: 116.65 2009 rank: 17 (tie) Metropolitan area population: 1,801,848 Bachelor’s degrees: 22% Graduate degrees: 11% Year-to-date adult nonfiction booksales: 1,248,000
#20, Baltimore, Md. Daily Beast IQ Score: 115 2009 rank: 10 Metropolitan area population: 2,690,886 Bachelor’s degrees: 20% Graduate degrees: 15% Year-to-date adult nonfiction booksales: 2,303,000 (OMFG Baltimore?)
Source date (UTC): 2018-06-06 09:41:00 UTC
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TOP 100 SUPPOSEDLY SMART CITIES GIVEN SOME SUSPECT ONLINE DATA…. (NOTE 1: This
TOP 100 SUPPOSEDLY SMART CITIES GIVEN SOME SUSPECT ONLINE DATA….
(NOTE 1: This is a very-very-suspect data set but I think something can be learned from it. The cities that did NOT make the list are the take-away: immigrant cities.)
(NOTE 2: that New York, Washington D.C., Los Angeles, San Francisco, Miami, Houston and Chicago were not on the list. The areas of greater Boston and Seattle were the only ones that made the list.
1. Ithaca, NY
2. State College, PA
3. Lafayette-West Lafayette, IN
4. Iowa City, IA
5. Ames, IA
6. Ann Arbor, MI
7. Bloomington, IN
8. Madison, WI
9. Lawrence, KS
10. Pullman, WA
11. College Station-Bryan, TX
12. Appleton, WI
13. Champaign-Urbana, IL
14. Blacksburg-Christiansburg-Radford, VA
15. Charlottesville, VA
16. Boulder, CO
17. Provo-Orem, UT
18. Harrisonburg, VA
19. Rolla, MO
20. Houghton, MI
21. Muncie, IN
22. Corvallis, OR
23. Boone, NC
24. Logan, UT-ID
25. Stillwater, OK
26. Milwaukee-Waukesha-West Allis, WI
27. Claremont-Lebanon, NH-VT
28. Lebanon, PA
29. Moscow, ID
30. Cedar Rapids, IA
31. Lincoln, NE
32. Bloomsburg-Berwick, PA
33. Minneapolis-St. Paul-Bloomington, MN-WI
34. Starkville, MS
35. Athens, OH
36. La Crosse-Onalaska, WI-MN
37. Brainerd, MN
38. Burlington-South Burlington, VT
39. Fargo, ND-MN
40. Stevens Point, WI
41. Columbia, MO
42. St. Cloud, MN
43. Rochester, MN
44. Auburn-Opelika, AL
45. Waterloo-Cedar Falls, IA
46. Oil City, PA
47. Fort Collins, CO
48. Sheboygan, WI
49. Boston-Cambridge-Newton, MA-NH
50. Keene, NH
51. Oshkosh-Neenah, WI
52. Burlington, NC
53. Pittsburgh, PA
54. Fond du Lac, WI
55. Concord, NH
56. Morgantown, WV
57. Bellingham, WA
58. Mount Pleasant, MI
59. Ottawa-Peru, IL
60. Indianapolis-Carmel-Anderson, IN
61. Laramie, WY
62. Barnstable Town, MA
63. Lancaster, PA
64. Reading, PA
65. Winchester, VA-WV
66. Bozeman, MT
67. Bloomington, IL
68 Gainesville, FL
69. Duluth, MN-WI
70. Michigan City-La Porte, IN
71. South Bend-Mishawaka, IN-MI
72. Des Moines-West Des Moines, IA
73. Omaha-Council Bluffs, NE-IA
74. Oxford, MS
75. Altoona, PA
76. Wooster, OH
77. Bismarck, ND
78. Grand Forks, ND-MN
79. Grand Rapids-Wyoming, MI
80. Albany-Schenectady-Troy, NY
81. Binghamton, NY
82. Harrisburg-Carlisle, PA
83. Wisconsin Rapids-Marshfield, WI
84. Ogdensburg-Massena, NY
85. Kansas City, MO-KS
86. San Luis Obispo-Paso Robles-Arroyo Grande, CA
87. Torrington, CT
88. Trenton, NJ
89. Rochester, NY
90. Seattle-Tacoma-Bellevue, WA
91. Oneonta, NY
92. Eau Claire, WI
93. Warsaw, IN
94. Norwich-New London, CT
95. Eugene, OR
96. Topeka, KS
97. Allentown-Bethlehem-Easton, PA-NJ
98. Mankato-North Mankato, MN
99. Helena, MT
100. Cincinnati, OH-KY-IN
Source date (UTC): 2018-06-06 09:13:00 UTC