{"id":3535,"date":"2016-09-30T17:19:23","date_gmt":"2016-09-30T22:19:23","guid":{"rendered":"http:\/\/synapse9.com\/signals\/?p=3535"},"modified":"2016-10-23T12:31:23","modified_gmt":"2016-10-23T17:31:23","slug":"is-science-coming-full-circle","status":"publish","type":"post","link":"https:\/\/synapse9.com\/signals\/is-science-coming-full-circle\/","title":{"rendered":"Is Science Coming Full Circle??"},"content":{"rendered":"

A change in natural science is emerging\u00a0along with\u00a0“computing”
\nturning\u00a0away from using theory &\u00a0equations<\/em><\/span> as a guide,
\ntoward using data pattern recognition<\/em><\/span>\u00a0for
\nnaturally occurring systems revealed in the data to be a guide.<\/p>\n

Preface<\/p>\n

Note: About 20 Years ago algorithms were developed for selectively extracting differentiable continuities from raw data, making a major step beyond “splines” for true\u00a0mining of natural\u00a0continuities from noisy data without regression<\/span>. \u00a0<\/strong>\u00a0\u00a0The result was quite successful forensic pattern recognition<\/a> of \u00a0discovered natural systems, their forms and behaviors. \u00a0 Combined with a\u00a0general systems \u201cpattern language”\u00a0based only on the constraint of energy conservation<\/a>, that pattern mining has provided a very productive alternative to AI for investigating\u00a0naturally occurring forms and designs. \u00a0The one unusual leap for applying scientific methods was to use it to capture the great richness of natural textures<\/span> available from studying uniquely individual cases and forms found in nature. \u00a0 That is what\u00a0overcomes the worst faults of studying individual cases, and so instead greatly enriches theory with directly observed phenomenology. \u00a0The rudimentary tools successfully developed have been proven useful again and again with\u00a0subjects such as illustrated below. \u00a010\/21\/16<\/em><\/p>\n

_________________________<\/p>\n

A long central principle of modern\u00a0science, relying on defining nature with the\u00a0information we can find, is considered here by way of \u00a0eight\u00a0examples of how important it is for science to also rely on doing the opposite, looking for patterns in the information we are missing somehow. \u00a0\u00a0Doing much the reverse lets us use the information we have to ask better questions about what nature is hiding from us. \u00a0<\/em><\/p>\n

It\u2019s such an odd and obvious mistake to stubbornly\u00a0treat\u00a0nature as our data, as Neils Bohr and Popper insisted on and the QM community has maintained. \u00a0 Being\u00a0limited to analysis and data creates a large blind spot for science, made unable by that limitation\u00a0to learn from observation, and to see clearly how very different the \u201cdata world\u201d (what fits in a computer) is from the \u201cmaterial world\u201d (what doesn\u2019t). The puzzles of found in natural patterns, turning up in \u2018bigdata and various pattern sciences seems to be putting all of these matters into question again. \u00a0 \u00a0<\/em><\/p>\n

So I may take some unfair\u00a0advantage, perhaps, by making a little fun of that\u00a0prior arbitrary constraint on scientific inquiry, insisting that nothing we have no data for can exist. \u00a0 That of course is almost everything when it comes down to. \u00a0It\u2019s no joke, though, that our\u00a0data is decidedly inferior for defining nature. \u00a0Here and\u00a0elsewhere\u00a0I tend to allow that nature defines itself, as I certainly don\u2019t do it.<\/em><\/p>\n

The \u201c<\/em>Impacts Uncounted<\/em><\/a>\u201d article mentioned describes a simply\u00a0enormous worldwide neglect in economic accounting, a huge mismeasure of lasting business\u00a0environmental\u00a0impacts. \u00a0 It’s caused by the traditional\u00a0insistence on trusting the data at hand and refusal to look for what data is going uncounted, as if the fact that we can only study the data we have means nature is not being misrepresented by it, a curiously deep concern for understanding the scientific method. \u00a0In reality there is more to life than the data we have. \u00a0 Treating “science” as whatever our data defines, then,\u00a0actually\u00a0means “flying blind” regarding all the kinds and scales of\u00a0phenomena\u00a0going unmeasured, the difference between nature and data going\u00a0unseen. \u00a0<\/em>For accurate accounting, even older\u00a0scientific\u00a0principles need to apply, such\u00a0as defining units of measure in relation to the whole system or “universe” for that measure, not just the\u00a0part\u00a0easy to measure, and so “Impacts Uncounted” is the effect of counting the global impacts of business using local measures, as is today standard around the world, a big mistake. \u00a0<\/em><\/p>\n

So these\u00a08 examples are \u201cdata visualizations\u201d that neatly expose where important data is very much missing, as a guide to where to go and look. \u00a0 Those hiding places exposed as\u00a0gaps in the data turn our attention to phenomena of perhaps another kind or another scale, or on another plane with material influence perhaps. \u00a0That is then what needs to be discovered and looked into. to really understand what the measures display and the systems or events they refer to.<\/em>\u00a0 \u00a0 That the data available, then, always points to phenomena beyond the scope of the data to define is both\u00a0the oldest and perhaps now the newest of deep scientific principles for interpreting what we see.<\/p><\/blockquote>\n

<\/h3>\n

Is science coming full circle\u2026? \u00a0The answer seems to be YES!<\/h2>\n

 <\/p>\n

Persistent patterns in data generally reflect complex natural forms of design, complex and complicated well beyond what data can define.\u00a0 So we present data in a way to helps show someone what\u2019s missing.<\/p>\n

Data from a natural source is generally biased and incomplete as a result of how it\u2019s collected, and a \u201cproxy\u201d for various things other than what it is said to measure. So not really knowing what it measures, it is best studied as being another way of sampling an undefined universe, to become meaningful by discovering its boundaries<\/p>\n

Patrick Ball\u2019s HRDAG[1]<\/a> methods demonstrate comparing sources for death records in conflict environments, using the differences and overlaps to reveal the true totals. My own research shows environmental impacts of business are undefined, lacking a common denominator to make them comparable as shares of the same universe.\u00a0 Correcting the mismeasure appears to increase the impact scale of business by several orders of magnitude[2]<\/a>.\u00a0 In both cases characterizing the universe the original data is implicitly sampled from serves as common denominator for making the original data meaningful.<\/p>\n

For discussing basic explanatory principles of physics used for forensic systems research[3]<\/a><\/p>\n

1. \u00a0 See where hidden connecting events shifted the flows??<\/strong><\/h3>\n

\"test<\/a>
[The missing data is about the unstable states, the markers of whole system change in design]<\/figcaption><\/figure><\/p>\n

2. See where hidden connecting events shifted the flows??<\/strong><\/h3>\n
[The missing data is in how GDP\u2019s and Energy use of countries coordinated, so the world\u2019s became so smooth]<\/figcaption><\/figure>\n

3. \u00a0See where hidden connecting events shifted the flows??<\/strong><\/h3>\n
<\/a>
[The missing data is in the dramatic collapse of Bronx crack epidemic murder rates long before the inauguration of the politicians taking credit for it]<\/figcaption><\/figure>\n

<\/h3>\n<\/blockquote>\n

4. See where hidden connecting events shifted the flows??<\/strong><\/h3>\n
\"gggggg\"<\/a>
[The missing data is about the sudden whole culture change in how businesses operated at 1970]<\/figcaption><\/figure>\n

5. See where hidden connecting events shifted the flows??<\/strong><\/h3>\n
[The missing data is the nature of these intricate flows, evident in all six statistically independent subsets of the recorded data, assumed to be random.]<\/figcaption><\/figure>\n

6. See where hidden connecting events shifted the flows??<\/strong><\/h3>\n
[The data missing from our ESG \u00a0\u201csource maps\u201d of business environmental impact is ALL the blue dots, human consumption by\u00a0business people not passed up the chain]<\/figcaption><\/figure>\n

7. See where hidden connecting events shifted the flows??<\/strong><\/h3>\n
<\/a>
[The missing data is where the investment money went to be more profitable, dumping local agriculture one place for the next, now being left behind too]<\/figcaption><\/figure>\n

8. See where hidden connecting events shifted the flows??<\/h3>\n
<\/a>
[The missing data is what links the \u201cunhidden pattern of events\u201d]<\/figcaption><\/figure>\n