Re: [FRIAM] Celebrating the Higgs - explaning and predicting
Good points, Saul. If I remember correctly, before the Bohr model, people looking at the hydrogen emission spectrum had already discovered an empirical formula for the frequencies of the emission lines: f = constant*(1/n1^2 - 1/n2^2) Bohr's model yielded the same formula, with Planck's constant times f being the energy of an emitted photon when the atom's quantum number changed from n2 to n1, and the model also provided an evaluation of the constant in terms of known quantities such as the mass of the electron. I should mention that the ten-pin diagram is a graphical representation of a particular structure in group theory. Bruce On Tue, Jul 10, 2012 at 11:05 PM, Saul Caganoff scagan...@gmail.com wrote: It seems that many scientific fields go through a phase of observation (derisively called stamp collecting) followed by a phase of classification. If you're lucky then patterns can be picked out of the classification scheme to predict where to look for new entities or new interesting phenomena. The Periodic Table is one of the cited examples. Another example (though perhaps not as good) is the Hertzprung-Russell diagram used in astronomy where stars are plotted onto a graph with luminosity and colour as the two axes. They form a characteristic pattern which had to be explained by any theory of stellar evolution. I also recall many years ago picking up a book on atomic spectra published in 1901 - some 12 years before the Bohr theory of the atom - which illustrated hundreds of different emission spectra and talked about the relationships between spectral line frequencies in terms of waves and resonances. It reflected a very interesting point in the science where patterns were emerging and calling out for an explanation. So it seems that a classification model can be used to make predictions - to see if the pattern extends to unobserved areas - and that this can be independent of an underlying explanatory theory. I think Gell-Mann's QCD models probably fit this idea. The image of the ten-pin owling skittles pattern and the mystery of what lies at the tip is very evocative. Regards, Saul On 11 July 2012 06:56, Bruce Sherwood bruce.sherw...@gmail.com wrote: For Engineers perhaps, predictive models are sufficient, they may not be (very?) interested in explaining *why* a particular material has the properties it does, merely *what* those properties are and how reliable the properties might be under a variety of conditions. I don't think this currently true. A big chunk of what used to be labeled physics is now in academic engineering departments with the name material science. This consists of exploiting models that explain observed properties of materials, with the goal of looking for opportunities to change parameters to get improved behavior. In the early 1990s I heard a talk by an engineering professor at the science museum in Toronto, where he explained how such research had led to concrete many times stronger than it had been, and that the iconic tall tower in Toronto could not have been built not many years before it was built, as it relied on much stronger concrete. In some cases someone sees how, starting from fundamental physics principles, one can predict that such and such should happen or be. In other cases an observed phenomenon gets explained in terms of fundamental physics principles (post-diction), which then suggests how changes in the situation might yield an improved behavior. Pre-diction and post-diction both require a deep understanding of how to go from underlying fundamental principles to the behavior, but pre-diction in addition requires the imagination to run the argument forward, not already knowing the answer. That's why I claim that post-diction (explanation) is more common than pre-diction. There's a fruitful interplay between pre-diction and post-diction. An example I've mentioned some time ago, from our intro physics textbook: When searching for an explanation for spark formation in air (we see the spark and ask how it occurs, which is post-diction or explanation) there are a couple of tentative explanations that one can rule out. Another explanation seems to explain the phenomenon, and the validity of this post-diction is greatly strengthened by noting that it (and not the other explanations) pre-dicts that it takes twice the critical electric field to trigger a spark if the air density is doubled, a pre-diction that is consistent with observations. Bruce FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College lectures, archives, unsubscribe, maps at http://www.friam.org -- Saul Caganoff Enterprise IT Architect Mobile: +61 410 430 809 LinkedIn: http://www.linkedin.com/in/scaganoff FRIAM Applied Complexity Group listserv Meets Fridays
Re: [FRIAM] Celebrating the Higgs - explaning and predicting
Eric, I have not read the original article, but still your comments caught my attention. As argued in this article http://jasss.soc.surrey.ac.uk/12/1/9.html , the disassociation of predictive and explanatory power seems misguided. I suppose a statistical function of many observational variables could have no explanatory power beyond the many variables on which it is based, but then it would only predict what it already knew. It would “just” be an intervening variable, and not a hypothetical construct, at all. As we have agreed, some explanations can have “facetious” content, that is not predictive, but that content is not really explanatory, either. Darwin certainly did not believe that Nature was a breeder who chose the better adapted individuals for breeding. Further, the idea of a distinction between that which can be directly or indirectly measured also seems a bit strange. Every measurement is based on a “measurement theory” that tells you that the reading you make on the dial is a valid measure of the thing you actually care about. Measurement theories fail all the time. So, what then is a “direct” measure? One possible response to this comment might be to just tell me to piss off until I have read the article you are referring to. Nick PS And isn’t “real existence” the ultimate hypothetical construct? From: friam-boun...@redfish.com [mailto:friam-boun...@redfish.com] On Behalf Of ERIC P. CHARLES Sent: Tuesday, July 10, 2012 2:41 PM To: Steve Smith Cc: The Friday Morning Applied Complexity Coffee Group Subject: Re: [FRIAM] Celebrating the Higgs - explaning and predicting Steve, Interesting paper, but I'm not sure if I follow. The basic argument seems to be that we often explain things by imagining (with the help of statistics) hypothetical constructs that cannot be directly measured. As those constructs can't be measured directly, they don't help us predict things. Thus, predictive models are limited to using things that actually exist, while explanatory models are not so limited. That seems like a really good argument for coming up with better explanations, not an argument for distinguishing and reifying two distinct modeling tasks. This is a topic I am quite interested in. I would presume that an ideal explanatory model would be identical to an ideal predictive model, though I grant that non-ideal cases might differ. What am I missing? Eric On Tue, Jul 10, 2012 12:37 AM, Steve Smith sasm...@swcp.com wrote: Bruce - I second the motion (very good post)! Mendeleev's Periodic Chart was *my* first introduction (back when) to the very concept of having a predictive model that was (almost) entirely void of explanatory ability (as it stood when constructed). I found the notion *fascinating* and it drove me into the field of Visual (Perceptual) Analytics many years later... seeking patterns that yield useful prediction without necessarily waiting for an explanatory model. For those vaguely interested in the philosophical underpinnings of science, it's methods and utility, I recommend Galit Schmueli's (George Washington U's) paper on Predictive vs Explanatory Models (as well as *Descriptive* models)... arxiv.org/pdf/1101.0891 Thanks! Glad you liked it! I have long been bemused by the strong parallels among the various tales I was able to tell in that post. Bruce On Mon, Jul 9, 2012 at 4:43 PM, Pamela McCorduck pam...@well.com wrote: Bruce, that blog post is marvelous in its simplicity and power. Pamela On Jul 9, 2012, at 2:39 PM, Bruce Sherwood wrote: See my blog: http://matterandinteractions.wordpress.com/2012/07/09/the-higgs-boson-and-prediction-in-science/ Bruce On Sun, Jul 8, 2012 at 10:18 PM, Owen Densmore o...@backspaces.net wrote: Lets chat about the Higgs discovery, its likely-hood of being correct, and the impact it will have going forward .. at the next Friam @ St Johns. Could someone see if Hywel White is available .. or anyone you know who'd like to hold forth on the topic! -- Owen FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College lectures, archives, unsubscribe, maps at http://www.friam.org FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College lectures, archives, unsubscribe, maps at http://www.friam.org Im Deutschen lügt man, wenn man höflich ist. In German, if one is polite, one lies. Goethe, Faust FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College lectures, archives, unsubscribe, maps at http://www.friam.org FRIAM Applied Complexity Group listserv Meets
Re: [FRIAM] Celebrating the Higgs - explaning and predicting
Excellent series of explanations, Bruce. Do you by chance have a specific reference to the ten-pin structure and its relation to group theory? Thanks ... Dean Gerber From: Bruce Sherwood bruce.sherw...@gmail.com To: The Friday Morning Applied Complexity Coffee Group friam@redfish.com Sent: Wednesday, July 11, 2012 9:38 AM Subject: Re: [FRIAM] Celebrating the Higgs - explaning and predicting Good points, Saul. If I remember correctly, before the Bohr model, people looking at the hydrogen emission spectrum had already discovered an empirical formula for the frequencies of the emission lines: f = constant*(1/n1^2 - 1/n2^2) Bohr's model yielded the same formula, with Planck's constant times f being the energy of an emitted photon when the atom's quantum number changed from n2 to n1, and the model also provided an evaluation of the constant in terms of known quantities such as the mass of the electron. I should mention that the ten-pin diagram is a graphical representation of a particular structure in group theory. Bruce On Tue, Jul 10, 2012 at 11:05 PM, Saul Caganoff scagan...@gmail.com wrote: It seems that many scientific fields go through a phase of observation (derisively called stamp collecting) followed by a phase of classification. If you're lucky then patterns can be picked out of the classification scheme to predict where to look for new entities or new interesting phenomena. The Periodic Table is one of the cited examples. Another example (though perhaps not as good) is the Hertzprung-Russell diagram used in astronomy where stars are plotted onto a graph with luminosity and colour as the two axes. They form a characteristic pattern which had to be explained by any theory of stellar evolution. I also recall many years ago picking up a book on atomic spectra published in 1901 - some 12 years before the Bohr theory of the atom - which illustrated hundreds of different emission spectra and talked about the relationships between spectral line frequencies in terms of waves and resonances. It reflected a very interesting point in the science where patterns were emerging and calling out for an explanation. So it seems that a classification model can be used to make predictions - to see if the pattern extends to unobserved areas - and that this can be independent of an underlying explanatory theory. I think Gell-Mann's QCD models probably fit this idea. The image of the ten-pin owling skittles pattern and the mystery of what lies at the tip is very evocative. Regards, Saul On 11 July 2012 06:56, Bruce Sherwood bruce.sherw...@gmail.com wrote: For Engineers perhaps, predictive models are sufficient, they may not be (very?) interested in explaining *why* a particular material has the properties it does, merely *what* those properties are and how reliable the properties might be under a variety of conditions. I don't think this currently true. A big chunk of what used to be labeled physics is now in academic engineering departments with the name material science. This consists of exploiting models that explain observed properties of materials, with the goal of looking for opportunities to change parameters to get improved behavior. In the early 1990s I heard a talk by an engineering professor at the science museum in Toronto, where he explained how such research had led to concrete many times stronger than it had been, and that the iconic tall tower in Toronto could not have been built not many years before it was built, as it relied on much stronger concrete. In some cases someone sees how, starting from fundamental physics principles, one can predict that such and such should happen or be. In other cases an observed phenomenon gets explained in terms of fundamental physics principles (post-diction), which then suggests how changes in the situation might yield an improved behavior. Pre-diction and post-diction both require a deep understanding of how to go from underlying fundamental principles to the behavior, but pre-diction in addition requires the imagination to run the argument forward, not already knowing the answer. That's why I claim that post-diction (explanation) is more common than pre-diction. There's a fruitful interplay between pre-diction and post-diction. An example I've mentioned some time ago, from our intro physics textbook: When searching for an explanation for spark formation in air (we see the spark and ask how it occurs, which is post-diction or explanation) there are a couple of tentative explanations that one can rule out. Another explanation seems to explain the phenomenon, and the validity of this post-diction is greatly strengthened by noting that it (and not the other explanations) pre-dicts that it takes twice the critical electric field to trigger a spark if the air density is doubled, a pre-diction that is consistent with observations. Bruce
Re: [FRIAM] Celebrating the Higgs - explaning and predicting
Nick (aka NST) - One possible response to this comment might be to just tell me to piss off until I have read the article you are referring to. I think you caught the implications without having read the specific article that I recommended. We are obviously wandered into territory which you have explored before... I definitely welcome your weighing in here. As argued in this article http://jasss.soc.surrey.ac.uk/12/1/9.html, Which I just read. I was previously not familiar with JASS. http://jasss.soc.surrey.ac.uk/12/1/9.html the disassociation of predictive and explanatory power seems misguided. http://jasss.soc.surrey.ac.uk/12/1/9.html I suppose a statistical function of many observational variables could have no explanatory power beyond the many variables on which it is based, but then it would only predict what it already knew. It would just be an intervening variable, and not a hypothetical construct, at all. As we have agreed, some explanations can have facetious content, that is not predictive, but that content is not really explanatory, either. Darwin certainly did not believe that Nature was a breeder who chose the better adapted individuals for breeding. Quoting NST (you) from the article: Modeling is the systematic deployment of the human capacity for metaphor and is central to all scientific activity. Models don't stand or fall on their detailed verisimilitude, but on their capacity to capture the essence what is already known about a phenomenon and to generate expectations concerning what more might be discovered if the scientist were to look where the model pointed. This point is a very key one IMO... it is roughly what I base my own work in the development and application of Metaphor (Complex Metaphors and Metaphor Complexes) in Information/Data Visualization and Visual Analytics. I *think* that what we are discussing here is the role of *explanation* in Science? I think what you are referring to as facetious content above (e.g. Darwin's description of Nature as an animal/human husband, selecting individuals for selective breeding...) is merely an extreme end of the use of analogy to explain. I presume that Darwin's choice of analogy was deliberately extreme to make it as familiar as possible to the totally uninitiated. To those already somewhat on board with the general model, I presume they *all* dispensed with the misunderstandings implied. We should perhaps talk more about his offline as I can already hear poor Doug's eyeballs rolling in his sockets, but I would like to elaborate for/with you what I mean by Metaphor Complexes... as they may be directly responsive to this problem of facetious content. In particular, if we admit to a whole series of layers of explanation from the most fanciful but accessible to the most complete and accurate but mundanely obscure. My interest is to build a scaffold from the most fanciful to the most mundane, or the most accessible to the least in the interest of A) Helping an individual build a mental (and possibly mathematical) model of a phenomena for themselves in the pursuit of exploration and discovery in some phenomenological domain; and B) helping said individual blaze a trail that others can follow from an accessible if fanciful explanation to a more complete and accurate if obscure (and presumably useful one).Perhaps what I'm suggesting is to build a stack of models that span the spectrum from explanatory to predictive, fanciful to mundane. For this I need to retain the distinction. I would prefer to think of my Metaphor Layers as various renderings or projections of aspects of a *single* model which has *all three* Explanatory, Descriptive, and Predictive qualities. - SAS Nick PS And isn't real existence the ultimate hypothetical construct? *From:*friam-boun...@redfish.com [mailto:friam-boun...@redfish.com] *On Behalf Of *ERIC P. CHARLES *Sent:* Tuesday, July 10, 2012 2:41 PM *To:* Steve Smith *Cc:* The Friday Morning Applied Complexity Coffee Group *Subject:* Re: [FRIAM] Celebrating the Higgs - explaning and predicting Steve, Interesting paper, but I'm not sure if I follow. The basic argument seems to be that we often explain things by imagining (with the help of statistics) hypothetical constructs that cannot be directly measured. As those constructs can't be measured directly, they don't help us predict things. Thus, predictive models are limited to using things that actually exist, while explanatory models are not so limited. That seems like a really good argument for coming up with better explanations, not an argument for distinguishing and reifying two distinct modeling tasks. This is a topic I am quite interested in. I would presume that an ideal explanatory model would be identical to an ideal predictive model, though I grant that non-ideal cases might differ. What am I missing? Eric On Tue, Jul 10, 2012 12
Re: [FRIAM] Celebrating the Higgs - explaning and predicting
Thanks, Dean. http://en.wikipedia.org/wiki/Eightfold_Way_(physics) gives a brief overview of what Gell-Mann (and Ne'eman) did, and explains that the octet and decuplet are representations of the group SU(3). The article includes some links to additional details. Bruce On Wed, Jul 11, 2012 at 10:14 AM, Dean Gerber pd_ger...@yahoo.com wrote: Excellent series of explanations, Bruce. Do you by chance have a specific reference to the ten-pin structure and its relation to group theory? Thanks ... Dean Gerber FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College lectures, archives, unsubscribe, maps at http://www.friam.org
Re: [FRIAM] Celebrating the Higgs - explaning and predicting
Steve,
Interesting paper, but I'm not sure if I follow. The basic argument seems to be
that we often explain things by imagining (with the help of statistics)
hypothetical constructs that cannot be directly measured. As those constructs
can't be measured directly, they don't help us predict things. Thus, predictive
models are limited to using things that actually exist, while explanatory
models are not so limited.
That seems like a really good argument for coming up with better explanations,
not an argument for distinguishing and reifying two distinct modeling tasks.
This is a topic I am quite interested in. I would presume that an ideal
explanatory model would be identical to an ideal predictive model, though I
grant that non-ideal cases might differ. What am I missing?
Eric
On Tue, Jul 10, 2012 12:37 AM, Steve Smith sasm...@swcp.com wrote:
Bruce -
I second the motion (very good post)!
Mendeleev's Periodic Chart was *my* first introduction (back when)
to the very concept of having a predictive model that was (almost)
entirely void of explanatory ability (as it stood when
constructed). I found the notion *fascinating* and it drove me
into the field of Visual (Perceptual) Analytics many years
later... seeking patterns that yield useful prediction without
necessarily waiting for an explanatory model.
For those vaguely interested in the philosophical underpinnings of
science, it's methods and utility, I recommend Galit Schmueli's
(George Washington U's) paper on Predictive vs Explanatory Models
(as well as *Descriptive* models)...
arxiv.org/pdf/1101.0891
Thanks! Glad you liked it!
I have long been bemused by the strong parallels among the various
tales I was able to tell in that post.
Bruce
On Mon, Jul 9, 2012 at 4:43 PM, Pamela McCorduck a
class=moz-txt-link-rfc2396E href=#pam...@well.com/a wrote:
Bruce, that blog post is marvelous in its simplicity and power.
Pamela
On Jul 9, 2012, at 2:39 PM, Bruce Sherwood wrote:
See my blog:
a class=moz-txt-link-freetext
href=http://matterandinteractions.wordpress.com/2012/07/09/the-higgs-boson-and-prediction-in-science/;
onclick=window.open('http://matterandinteractions.wordpress.com/2012/07/09/the-higgs-boson-and-prediction-in-science/');return
false;http://matterandinteractions.wordpress.com/2012/07/09/the-higgs-boson-and-prediction-in-science//a
Bruce
On Sun, Jul 8, 2012 at 10:18 PM, Owen Densmore a
class=moz-txt-link-rfc2396E href=#o...@backspaces.net/a wrote:
Lets chat about the Higgs discovery, its likely-hood of being correct, and
the impact it will have going forward .. at the next Friam @ St Johns.
Could someone see if Hywel White is available .. or anyone you know who'd
like to hold forth on the topic!
-- Owen
FRIAM Applied Complexity Group listserv
Meets Fridays 9a-11:30 at cafe at St. John's College
lectures, archives, unsubscribe, maps at a class=moz-txt-link-freetext
href=http://www.friam.org;
onclick=window.open('http://www.friam.org');return
false;http://www.friam.org/a
FRIAM Applied Complexity Group listserv
Meets Fridays 9a-11:30 at cafe at St. John's College
lectures, archives, unsubscribe, maps at a class=moz-txt-link-freetext
href=http://www.friam.org;
onclick=window.open('http://www.friam.org');return
false;http://www.friam.org/a
Im Deutschen lügt man, wenn man höflich ist.
In German, if one is polite, one lies.
Goethe, Faust
FRIAM Applied Complexity Group listserv
Meets Fridays 9a-11:30 at cafe at St. John's College
lectures, archives, unsubscribe, maps at a class=moz-txt-link-freetext
href=http://www.friam.org;
onclick=window.open('http://www.friam.org');return
false;http://www.friam.org/a
FRIAM Applied Complexity Group listserv
Meets Fridays 9a-11:30 at cafe at St. John's College
lectures, archives, unsubscribe, maps at a class=moz-txt-link-freetext
href=http://www.friam.org;
onclick=window.open('http://www.friam.org');return
false;http://www.friam.org/a
FRIAM Applied Complexity Group listserv
Meets Fridays 9a-11:30 at cafe at St. John's College
lectures, archives, unsubscribe, maps at http://www.friam.org
Eric Charles
Professional Student and
Assistant Professor of Psychology
Penn State University
Altoona, PA 16601
FRIAM Applied Complexity Group listserv
Meets Fridays 9a-11:30 at cafe at St. John's College
lectures, archives, unsubscribe, maps at http://www.friam.org
Re: [FRIAM] Celebrating the Higgs - explaning and predicting
Eric - Interesting paper, but I'm not sure if I follow. The basic argument seems to be that we often explain things by imagining (with the help of statistics) hypothetical constructs that cannot be directly measured. As those constructs can't be measured directly, they don't help us predict things. Thus, predictive models are limited to using things that actually exist, while explanatory models are not so limited. I think I have been considering it from the opposite perspective... that predictive models aren't *necessarily* explanatory... I think that *explanatory* models are at least minimally *predictive* (how else can they be validated)? They may not be sufficient to predict (m)any of the things we are interested in, but I think (though I'm not sure exactly why) the are in fact predictive (by definition?). That seems like a really good argument for coming up with better explanations, not an argument for distinguishing and reifying two distinct modeling tasks. I think Science is always seeking models that have more explanatory power. The business of Science is *understanding* (my contention) with *prediction* being merely a useful mechanism for hypothesis testing and even generation. Most of us get excited when Science *predicts* something, but I am not sure it is an end in itself (except for engineering, technology development, business purposes). I think Bruce's description of Mendeleev's Periodic Chart is my easiest example... the model in this case was an almost numerological geometric arrangement of the known elements based on correlations among their properties without any *explanation of the underlying reasons or causes for those properties. It was very effective for predicting elements yet to be discovered (recognized, identified?) as well as properties of known elements not yet explored, and thereby helped to structure the *search* for new elements. This is a topic I am quite interested in. I would presume that an ideal explanatory model would be identical to an ideal predictive model, though I grant that non-ideal cases might differ. What am I missing? I think you are correct that an ideal model has strong predictive and explanatory properties. For Engineers perhaps, predictive models are sufficient, they may not be (very?) interested in explaining *why* a particular material has the properties it does, merely *what* those properties are and how reliable the properties might be under a variety of conditions. I'm sure there are others here with good perspective on this question... Bruce? - Steve FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College lectures, archives, unsubscribe, maps at http://www.friam.org
Re: [FRIAM] Celebrating the Higgs - explaning and predicting
For Engineers perhaps, predictive models are sufficient, they may not be (very?) interested in explaining *why* a particular material has the properties it does, merely *what* those properties are and how reliable the properties might be under a variety of conditions. I don't think this currently true. A big chunk of what used to be labeled physics is now in academic engineering departments with the name material science. This consists of exploiting models that explain observed properties of materials, with the goal of looking for opportunities to change parameters to get improved behavior. In the early 1990s I heard a talk by an engineering professor at the science museum in Toronto, where he explained how such research had led to concrete many times stronger than it had been, and that the iconic tall tower in Toronto could not have been built not many years before it was built, as it relied on much stronger concrete. In some cases someone sees how, starting from fundamental physics principles, one can predict that such and such should happen or be. In other cases an observed phenomenon gets explained in terms of fundamental physics principles (post-diction), which then suggests how changes in the situation might yield an improved behavior. Pre-diction and post-diction both require a deep understanding of how to go from underlying fundamental principles to the behavior, but pre-diction in addition requires the imagination to run the argument forward, not already knowing the answer. That's why I claim that post-diction (explanation) is more common than pre-diction. There's a fruitful interplay between pre-diction and post-diction. An example I've mentioned some time ago, from our intro physics textbook: When searching for an explanation for spark formation in air (we see the spark and ask how it occurs, which is post-diction or explanation) there are a couple of tentative explanations that one can rule out. Another explanation seems to explain the phenomenon, and the validity of this post-diction is greatly strengthened by noting that it (and not the other explanations) pre-dicts that it takes twice the critical electric field to trigger a spark if the air density is doubled, a pre-diction that is consistent with observations. Bruce FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College lectures, archives, unsubscribe, maps at http://www.friam.org
Re: [FRIAM] Celebrating the Higgs - explaning and predicting
It seems that many scientific fields go through a phase of observation (derisively called stamp collecting) followed by a phase of classification. If you're lucky then patterns can be picked out of the classification scheme to predict where to look for new entities or new interesting phenomena. The Periodic Table is one of the cited examples. Another example (though perhaps not as good) is the Hertzprung-Russell diagram used in astronomy where stars are plotted onto a graph with luminosity and colour as the two axes. They form a characteristic pattern which had to be explained by any theory of stellar evolution. I also recall many years ago picking up a book on atomic spectra published in 1901 - some 12 years before the Bohr theory of the atom - which illustrated hundreds of different emission spectra and talked about the relationships between spectral line frequencies in terms of waves and resonances. It reflected a very interesting point in the science where patterns were emerging and calling out for an explanation. So it seems that a classification model can be used to make predictions - to see if the pattern extends to unobserved areas - and that this can be independent of an underlying explanatory theory. I think Gell-Mann's QCD models probably fit this idea. The image of the ten-pin owling skittles pattern and the mystery of what lies at the tip is very evocative. Regards, Saul On 11 July 2012 06:56, Bruce Sherwood bruce.sherw...@gmail.com wrote: For Engineers perhaps, predictive models are sufficient, they may not be (very?) interested in explaining *why* a particular material has the properties it does, merely *what* those properties are and how reliable the properties might be under a variety of conditions. I don't think this currently true. A big chunk of what used to be labeled physics is now in academic engineering departments with the name material science. This consists of exploiting models that explain observed properties of materials, with the goal of looking for opportunities to change parameters to get improved behavior. In the early 1990s I heard a talk by an engineering professor at the science museum in Toronto, where he explained how such research had led to concrete many times stronger than it had been, and that the iconic tall tower in Toronto could not have been built not many years before it was built, as it relied on much stronger concrete. In some cases someone sees how, starting from fundamental physics principles, one can predict that such and such should happen or be. In other cases an observed phenomenon gets explained in terms of fundamental physics principles (post-diction), which then suggests how changes in the situation might yield an improved behavior. Pre-diction and post-diction both require a deep understanding of how to go from underlying fundamental principles to the behavior, but pre-diction in addition requires the imagination to run the argument forward, not already knowing the answer. That's why I claim that post-diction (explanation) is more common than pre-diction. There's a fruitful interplay between pre-diction and post-diction. An example I've mentioned some time ago, from our intro physics textbook: When searching for an explanation for spark formation in air (we see the spark and ask how it occurs, which is post-diction or explanation) there are a couple of tentative explanations that one can rule out. Another explanation seems to explain the phenomenon, and the validity of this post-diction is greatly strengthened by noting that it (and not the other explanations) pre-dicts that it takes twice the critical electric field to trigger a spark if the air density is doubled, a pre-diction that is consistent with observations. Bruce FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College lectures, archives, unsubscribe, maps at http://www.friam.org -- Saul Caganoff Enterprise IT Architect Mobile: +61 410 430 809 LinkedIn: http://www.linkedin.com/in/scaganoff FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College lectures, archives, unsubscribe, maps at http://www.friam.org
