Right, so we can understand and predict the motion of the Earth without needing to specify if it orbits the Sun or if the Sun orbits the Earth, but we will need to change our answer if someone swaps in a slightly larger Sun far more than we would need to if someone swapped in a slightly larger Earth! Taking that as evidence that the Earth orbits the Sun is to adopt a slightly different meaning for "orbit", that the causal agent "deserves" to occupy a special place in the coordinates. That is often done, just as we say the Earth spins rather than saying the stars go around us once a day. But the equations we use as "laws of nature" don't know the difference-- we view it as quite fundamental that those equations are coordinate independent.
But again the "specialness" of the Earth in that scenario stems entirely from the fact that it is the source of the gravity, so the action of the gravity respects the location of the Earth. If that is used to justify choosing coordinates that also respect that location, fine, but there is no automatic need to choose a coordinate that is built around the gravitational source. Coordinates are all about convenience, not philosophy. There is no more fundamental statement of what "relativity" means.

__________________
Logic is the grammar of truth.

Meaning and absolute certainty are incompatible, and profound meaning and absolute certainty are profoundly incompatible.

The only thing intelligence is capable of is recognizing itself.

Agreed, and the fact that we say the Sun rises and sets doesn't help my "preferred frame" logic.

Nevertheless, both theories of gravity (Newton and Einstein), from the use of the scientific method, predict a coordinate change if the mass is altered. Yet, conversely, it does not predict a mass change if we change the coordinate framework, at least I assume so. I'm probably wording this poorly, but I do see a difference that is a cause and effect issue regardless of the fact that the interlock between the math and the behavior are unchanged, like a horse and cart analogy; one leads the other. The larger bodies will be seen to not orbit the smaller bodies, so Geocentricity fails though the math is equivalent. Similarly the cart does not push the horse, though the horse's sweat and temperature makes the physics easier.

Using the universe as my reference frame, or at least space near the Solar system in this case, and claiming it as a prefered view that favors a near-Sun barycenter model may be my downfall, but there must be some logical or objective advantage that shows it is more than a matter of philosophy. Perhaps it is an Ocaham's Razor for reference frames as to which is the most elegant with greatest utility (ignoring all those Earthling's desires to use geocentricity for their own selfish wish for easier calculations, of course).

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Lighten up! This is a stellar board! Author: duh.

"The mean of five measures each of which is not worth a dang (sinc), has a maximum value of only five dangs (sinc)". Heber Curtis

"(sinc)" - spelling is not correct (in its orginal form) :)

I wouldn't say they predict a coordinate change, but I think you mean that they predict the motion within any set coordinate system would be different.In a sense they do-- if the Earth's motion suddenly changed as though the Sun had more mass, we might have to conclude that in fact the Sun's mass did change. We would always say the mass caused the motion change, regardless of which we noticed first, but again the coordinates need not be slave to causation, they merely must respect the connection between the mass and the motion.

Causation is indeed viewed as a principle of physics, but the question here is, shall we impose a connection between it and coordinatization, or may the two be viewed entirely independently (as in, "the Sun set"). That relativity works is trying to tell us something, but that cause-and-effect works is saying something too. Perhaps an open mind is the best course at this stage.

To me, the failure of geocentricity is not in how the motion gets described, it is in how we attribute that motion. If one merely states that one has chosen to center the coordinates on the Earth for no reason but personal preference or convenience, no fault can be laid. If one claims that this is somehow a special coordinate singled out by nature, rather than purely a choice by man, then one is not using the physical principles that have been demonstrated. But the same can be said about any coordinate system-- I think if one becomes adept at GR (I'm not), one finds that nature simply does not single out coordinates-- it's the same "crank" no matter what coordinates you use, sometimes the math is just easier.

Indeed it is-- but which one is most elegant may vary from problem to problem, moreso than from law to law.

__________________
Logic is the grammar of truth.

Meaning and absolute certainty are incompatible, and profound meaning and absolute certainty are profoundly incompatible.

The only thing intelligence is capable of is recognizing itself.

Though you're view is correct, I actually was trying to push the envelope a little, since I can't seem to open it. I meant that regardless of what mapping system is thrown upon an orbital body, changing the coordinates in any fashion will not alter the fixed amount of mass. Yet the converse does, even though the invariance of physics is unchanged, of course.

Nicely said. Though inexorably linked, I still this connection as one way; a diode is in this circuit, or a horse is pulling the cart.

Does a hollow mind count as open? Although the coordinate view from the cart is just as valid as the coordinate view from the horse or the on-watcher, the erratic movement of the horse will cause coordinate changes.

Agreed. I probably should not have used a capital G in Geocentricity, which attempts to represent the absolute center. For me, those that favor the Geocentric model don't appreciate what you've said, so I like the fact that the Earth jiggles as it rotates. Air mass movements, primarily, and other mass shifts cause the Earth to speed-up or slow down enough to be measurable (using quasar alignments, interestingly). For one to argue that the universe causes these changes, then they will find themselves in Sillyville trying to compare their causal arguments against a rotating Earth with its observable changes in air mass.

I'm plowing through the brush here, at y'all's expense, as I try to wrestle with how subjective I am willing to make the causal arguments. I sense an indifference intrinsic to GR on this kind of issue and it seems counter to how the scientific method has worked before in developing prior theories. GR seems beautiful but cold (ok, that's subjective. ).

Yes, and geocentricity is a good example as it is the one used the most, I suspect. Yet, I go a little further than the view Geocentrists are wrong with the superior view, I say that it is inferior for the causal reason given above. Not inferior in its utility or mathematical equivalence, but inferior when it attempts to interface, as Len Moran stated, with reality. The broader universe, perhaps, is what I am trying to embrace and certain models are superior to others. I think the scientific method helps identify this in an objective way, though the final conclusions require some subjective view, too. This is some tricky stuff for me, as you can guess, as I am uncomfortable about allowing subjective views imposing on science, so I am trying to be sure this superiority idea is not beyond the purview of science.

__________________
Lighten up! This is a stellar board! Author: duh.

"The mean of five measures each of which is not worth a dang (sinc), has a maximum value of only five dangs (sinc)". Heber Curtis

"(sinc)" - spelling is not correct (in its orginal form) :)

If the coordinates are the coordinates of the horse, then there is no coordinate change-- the erraticness (is that a word?) is manifested in the motion of everything else, but not in the coordinates, they are just as smooth as silk (perhaps generated by radar ranging experiments from horseback). Now it must be granted that a coordinate system that suggests everything in the universe is moving in an erratic way except the horse does great violence with causality concepts, but that is not a problem if one does not expect the coordinates to respect causality-- the only duty of the coordinates is to keep track of what is happening and provide a mathematical scheme for understanding and predicting what will happen next. They are not there to answer issues of causality, and whosoever uses them for that purpose is likely to be let down.

You might argue that if we have a breakdown in our understanding of the motion of a horse, it's best that it only affect the one thing, the horse, and allows us to get everything else right, rather than allowing us to get the horse right by default but incorrectly predict the motion of everything else in the universe. That's the advantage of using coordinates that respect causality, once you know what the causality is. But there may also be disadvantages-- such that the "superior" choice may be dependent on the problem of interest.

I agree they don't, I am not defending the way Geocentrism gets applied as a philosophy or theology-- just how geocentrism is used to make Earth-frame calculations of things like weather patterns. For example, geocentrism allows us to speak of "wind" as if it was simply air movement, saving us from calling it air movement relative to the surface of the Earth. We then view hurricanes as caused by the "coriolis effect", rather than simply saying they occur because the air is already hurtling around the Earth at speeds upwards of half a km/s. It serves us to do so, even though it monkeys with the "real" causality rather badly (the coriolis effect is a coordinate force like the one that makes the universe lurch when the horse is erratic). Yes, coordinates don't give us causality, causality stems from something else. That I think is the main flaw in Geocentrism as a philosophy, as opposed to geocentrism in weather forecasting. There are ways of thinking of the gravitational influence of the air mass movements as affecting the rest of the universe (which is after all moving faster than light at large enough distances if one uses coordinates in which the Earth is not rotating), and then the influences are faster than the speed of light. But causality seems to know the difference between a "true" subluminal cause and this type of "ficticious" cause, so I think the issue of the causality is not as invariant as the coordinatization.
It's hard for me to say, not being a GR expert, but my impression is that GR admits two separate types of causality. One is a kind of causal illusion, akin to what are called "coordinate forces" and closely connected to the equivalence principle. For example, if you have straight line motion in Cartesian coordinates and you transform that into polar coordinates, it still qualifies as a geodesic, but it's curved in those coordinates, because azimuthal motion evolves naturally into radial motion without there being any true forces to make that happen. That's a "coordinate force".

Given the equivalence principle, all such coordinate forces are indistinguishable from gravity if you don't look too closely-- where looking closely means looking for the tidal signature of real gravity (the shape of the vase, again). So what is the "cause" of the curved motion in polar coordinates? The coordinates themselves. That's not a "real" cause, as it need not reflect subluminal influences and identifiable agents. Similarly, the coordinates "attached to the horse" will also generate coordinate forces that affect the whole rest of the universe, whose "cause" is the erratic motions of the horse. It's a different kind of causality, but it's allowed in GR. Still, GR does not remove our ability to notice that there is another type of causality at play that obeys the subluminal speed limit and is describable in terms of real forces.

The question then is-- which type is gravity, coordinate force or real force? I think the answer is, only tidal gravitational affects are the "real" forces-- the main affects of gravity on motion that we perceive are actually just coordinate forces, and are just as "imaginary" as the erratic motion of the horse "causing" everything in the universe to lurch in compensating ways.
I agree, and I see the source of that inferiority lying not in the coordinates themselves, but in any associated supposition that the coordinates are anything but arbitrary. Indeed, I've always felt the debate between the Copernican view and the Ptolemaic view (or better, Tycho Brahe's model) was not which object orbits which-- that's just a question of reference frame. It is whether or not the motion of the Earth is special in some way that is built into the fabric of reality, or whether or not reality is ambivalent to the motion of the Earth vis-a-vis the motion of any other similar rocky body in creation. The specialness comes from our choice of coordinates, not from nature, that's the real failing of Geocentrism.

There are pitfalls to avoid. The "superiority" of a coordinate choice can only be staked on convenience and preference of the practitioner, nothing else. The claim that the laws of nature themselves dictate the superiority of a certain coordinatization, simply because it gibes with causality, runs rather counter to the whole spirit of relativity.

For example, any coordinatization that generates what we would normally call a "force of gravity" (not differential stretching or squeezing, which are "real" tidal gravity effects) is similarly at odds with "true" causality, so would have to be labeled "inferior" in your general scheme. As such, a Cartesian coordinate system with origin at the center of mass of the solar system is also of the "inferior" type, as it has the Earth following a curved path, despite the absence of a proper cause for that (it's a "coordinate force" stemming from using coordinates that do not respect the curvature of spacetime, making it appear that the Earth's geodesic curves despite there being no cause for that other than the coordinates themselves).

__________________
Logic is the grammar of truth.

Meaning and absolute certainty are incompatible, and profound meaning and absolute certainty are profoundly incompatible.

The only thing intelligence is capable of is recognizing itself.

Thanks for the welcome back.

I've read this, several times, and I'm sorry to say I still don't understand what you're saying. This is rather embarrassing, because I nearly always find what you write fairly easy to grasp*, not least because you nearly always write very clearly (to me at least).

I'm not sure if it would help to take some specific examples (it might make matters worse!), but here goes. Oh, and I assume it's not a feature of just astronomy meetings and conferences ... they're just where you see this feature/attribute/way of thinking/whatever most clearly; if this assumption is wrong, I'm sure you'll set me straight post haste.

The Millennium Simulation: huge simulation of a CDM-dominated universe, using GR, which aimed to learn something about the growth of large-scale structure (among other things). One of the many research programmes it (or rather its predecessors and previous analytic work, it just 'shrank the error bars') kicked off was a search for (an OOM more) CDM-dominated dwarf galaxies and other research into dwarfs (merger histories, starburst histories, ...).

Exoplanets: do the doppler programmes qualify as elaborate simulations? After all, to find the n-th planet, you first have to nail down the parameters of the first n-1 ones! Also, the use of microlensing to find planets may be described as hot (and full of models), even though it gives only one shot at each planet. And what of transit searches? and the models used to infer something about the atmospheric composition of the transiting planets (once they're actually identified)?

"Dark Energy": two teams almost simultaneously discovered a consistent trend in high-z Ia SNe data; a flurry of activity followed, much of it involving strenuous efforts to ensure 'accelerated expansion' (and 'cosmic jerk') was a consistent conclusion. Much of this effort necessarily involved elaborate models, but for me the key take-away is the robustness of the conclusion, and that robustness depends critically on (at least some) of the models being quite elaborate.

WMAP and Planck: missions designed explicitly and specifically to study the CMB; compared to COBE the amount of modelling and number-crunching is stupendous; more important however is the combination of robustness and smarts that has gone into the 'how' of digging a cosmological signal out of the raw data (compare, for example, how COBE addressed the zodiacal light component).

Some Galaxy Zoo (GZ) findings: it seems an imbalance of clockwise vs anti-clockwise spirals, reported in some papers, is due to some bias in humans' interpretations of images; NGC3314 has approval for HST time to investigate Hanny's Voorwerp; a paper on 'blue ellipticals' will be coming out soon; 'zooites' (or 'zooties'!) found 'green peas', this is now been investigated; ... to be sure, even a dozen papers from GZ would be a drop in the bucket of astronomy/astrophysics/cosmology papers.

What am I missing? Can you give some specific examples of the 'half empty' glasses? or rather, how some specific glasses are half empty (rather than half full)?

* modulo some clarifying questions; of course, I don't always agree with what you write, but it'd be pretty darn boring if I did, right?

I guess what I'm really saying is that giant simulations have two main purposes-- one is to try and guarantee that the key physics is included (the "fishing net" philosophy of fishing), and the other is to try and obtain highly quantitative results when the situation is very well constrained by the physics and idealized assumptions are either not necessary or well supported in fact. But in neither case are we "finished" when the simulation achieves these ends-- yet that is often how they are treated.

In the first case, the work has just begun-- we have insured that the dominant physics is present in each situation the simulation covers, but we have not yet identified what that physics is. More often than not, in my experience, giant simulations can be broken down into smaller pieces where in each subset there is actually something quite simple happening, something that does not require the full simulation to understand (though it may have required it to find). It is often the case that in hindsight, the simple physics makes perfect sense and feels like it should have been anticipated prior to the simulation (though in practice it often is not). But my point is, if this followup analysis never occurs, as is all too often the case in the literature and at meetings, then this great promise is never actualized. Instead, people think the simulation has accomplished its goals, and nothing more is needed from it. It is a "complete" simulation-- but is it completed?

In the second case, where quantitative accuracy is the motivation for the complexity of the simulation, there is no simpler subset that can achieve that end. However, we are still not done, because even when quantitative accuracy is the goal, there is still a role for approximation. One role is in anticipating how the results will vary as you change the parameters. The "kitchen sink" approach to that issue is a brute-force variation of the input parameters (a so-called "grid of models"), and then interpolate to any actual desired situation.

But a simplified approximate understanding of the dominant physics also informs the process of understanding the sensitivity to input parameters, and the inaccuracy introduced by taking that approach is well compensated by the insight gained. Once the approximate dependences are understood, one can anticipate what combination of parameters will achieve some desired end, and then a full simulation can be run for the new parameters as a final check on that prediction. But the simple fact is, astronomers are forever undertaking calculations that are more accurate in form than they are in substance-- they are grinding out that third decimal place, yet invariably some new physics discovery comes along and changes the first decimal place. Examples are countless.

Granted, this is certainly a good example of how a "complete simulation" can make predictions that observers can then attempt to test, as a check on the assumptions of the simulation. But what I'm saying is, does this really complete the relationship that the theorists and observers should be having? Is it enough for theorists to say "I predict this, never mind why, you can picture this cartoon if you like, but what matters is that you look for it"? Put differently, can you tell me (in physical terms, not cartoon terms-- we get plenty of cartoons) why the Millennium Simulation produces so many dwarf galaxies? What is the key aspect of that simulation that gives you that, and once you understand that key aspect, how could you have obtained that same result without the simulation? As I said, hindsight is better than foresight, which is the main purpose of simulations if you ask me, so I'm not saying we never needed the full simulation. I'm merely saying it should not be viewed as the completed pinnacle of the theoretical effort.

In some situations, the accurate quantitative analysis is all that has value, because you are not trying to understand how something is working-- you already know how it works, and you are pressing for greater and greater detailed accuracy in your model inputs. Such situations are pretty rare in astronomy! But this is indeed one.
Transit searches are observational, I'm talking about the interaction between theorists and observers. So your last point is more relevant-- about what we can theoretically infer about the atmospheres. But this is a perfect example of what I'm talking about-- when we get information about the atmosphere of a transiting planet, do we need a giant "black box" simulation of all the things that might possibly be happening in that atmosphere? Do we really expect to be able to anticipate the possibilities so completely? I'd say we need a simplified understanding of what kinds of observed features map roughly and approximately into what general kinds of physical phenomena, long before we'll be in any position to apply meaningfully a black box simulation to making quantitative predictions (indeed we may never be in that position-- we can hardly even do that in our own atmosphere, just witness global climate simulations).
Another perfect example of what I'm talking about. The discovery was observational, and grossly disagreed with the current best models. One did not need a gigantic "black box" simulation to tell us that acceleration meant gravity was doing something weird! Even now when we do quantitative simulations, we are forced to use a rough treatment of dark energy, via the simplest possible equations of state we can think of. Again we're having at that third decimal place-- yet do we really think that physics 100 years from now will not have discovered something that significantly changes the entire picture of what dark energy is? Only if we are no students of history!

I take a different message-- I claim that if you analyze those elaborate models, you will generally find that something much simpler actually constrains any particular observation you want to understand. It can be different for different scales and different questions, so it's nice to have it "all in one place", i.e., in the kitchen sink somewhere. But that's no reason to ignore that oftentimes physics involves a simple interaction embedded in a much more complex milieu-- and isn't it our job to find that out? We end up with less egg on our face when some new physics means the "kitchen sink" simulation is no longer reliable. (No matter, one nice things about codes is they are as easy to change as a four-day weather forecast...)
I would view these as observational details. Yes many observations require sophisticated data reduction and analysis-- but that's in an effort to isolate and separate the physics of interest, it is not necessarily part of why you did the observation in the first place-- that's usually done to try and isolate a particular physical effect that is embedded in whatever is the complete simulation you are using. Extraction and isolation is a key element of doing physics, that's basically all I'm saying.
This is mixing in issues about analysis of complex datasets. I am certainly not suggesting that reality is not complex-- I'm suggesting that our job is become adept at finding ways that allow us to pretend, in some isolated context, that it isn't.

Right!

__________________
Logic is the grammar of truth.

Meaning and absolute certainty are incompatible, and profound meaning and absolute certainty are profoundly incompatible.

The only thing intelligence is capable of is recognizing itself.

I think one thing I'm getting stuck on is "giant simulations" ...

Let's take the re-analysis of the HIPPARCOS data that was published (fairly) recently*.

The original results involved number-crunching that excluded certain behaviours (or events), even though these were known to have occurred, and even though the physics was at least somewhat understood: the satellite going in and out of the Earth's shadow, and micrometeorite hits (among others); the re-analysis attempted to model these and remove their impact from the data, to produce more accurate proper motion and parallax estimates (as well as better error bars).

Certainly both analyses involved quite intricate modelling and lots of CPU hours; too, they both seem to have had both of the purposes you mention.

However, in each case we are (were) "finished" ... the objective of the mission^ was to produce parallax and proper motion estimates (together with robust estimates of uncertainty)!

In both cases, there certainly are other physical behaviours or events that have not been simulated or estimated, and sometime in the future someone may develop an even more complex simulation that uses even more CPU hours, and so produce an even better set of outputs. In fact, the known inaccuracies (etc) of the original analysis was one motivation for doing the re-analysis; however, diminishing returns have probably set in, not least because GAIA is likely to fly before too long.

Is this example even within the scope of your original post? If so, in what way(s) do you consider astronomy to be be unfinished (wrt this mission and the analyses)?

Maybe a concrete example might help?

Perhaps simulations of the SgrA* SMBH and its accretion disk? or of SMBH accretion disks in general? They involve a mix of highly non-linear interactions, physical processes and mechanisms from a great many parts of the physics textbook, etc, etc, etc yet the phenomenology ("observables") is all wrapped up in photons from point sources (well, except for certain ingenious attempts to get at it indirectly, like footprints of past flares).

If so, then I doubt anyone would say that the simulations are complete, or completed ... the results serve mainly to show how little is actually understood and as pointers to which of the myriad things not yet considered needs to be worked on next.

Sounds a bit like "google science"!
Critique understood; examples please!

But not a countless number of them; just three would suffice ...
Assuming this is not a rhetorical question, goodness gracious me no!
Maybe this is a bad example ... the excess of predicted dwarf galaxies (over what had been observed up till then) was known well before the Millennium Simulation was run, and how these vast numbers of dwarf galaxies formed was also known (as in, what physics was at work, and why); what was not known (and still isn't, due to the limitations of the Millennium Simulation) was the bounds on the hierarchical clustering; crudely, should the MW have ~100, or ~1000 dwarf satellite galaxies?

And there are plenty of simulations which seek to answer this (and other) question, using a range of techniques and 'nulling out' a range of different physical processes (all already known to exist).
OK, bad example then; I doubt that anyone views the Millennium Simulation as "the completed pinnacle of the theoretical effort"!
Or not ("pretty rare in astronomy"); maybe it's more a matter of degree/point of view?

Anyway, that helps me understand your point, thanks.
Again, this helps ... AFAIK no one has tried to infer something about transiting exoplanet atmospheres from giant simulations that are meta-GCMs!

(to be continued)

* if anyone would like a reference, just ask.

^ of course, the mission also produced other results, such as discovery of new binaries and variables

That's not really what I mean by a "simulation", I am actually only referring to a situation that one might characterize as "a priori modeling of a certain system based on making input assumptions and applying all the known laws of physics that we think could possibly be relevant". Such a system is a "black box" that takes input parameters and spits out output parameters, and what happened in between is that (hopefully) a certain set of equations were correctly solved by a typically long and exhausting process of generating computer code with that intention. Such a process essentially always generates "bugs" (or at least oversights and shortcuts), but hopefully the endeavor reaches a point where none of the bugs introduce any significant errors. This is a description of pretty much everything that gets reported under the heading of "theoretical modeling" or "theoretical simulations" at an astronomy meeting.

No, it's not a simulation of some physical system with the intent of understanding that system, it's merely an application of known physical details to the process of data reduction. I would classify that endeavor as "observational" rather than "theoretical", though I'll admit such distinctions can be blurred and are sometimes more arbitrary than we imagine.

Yes, that's more what I have in mind.

But the problem is the "completeness" of the simulation invariable rests on whether or not it agrees with the observations, rather than on how much we have learned from it. This is my point. I have seen many examples of the types of simulations that say, in effect, "here's what we got that agrees with observations, and here's the part that shows some discrepancies. We're working of finding modifications to the physics that will bring the discrepancies into line as well." As if the work was needed in the area of the discrepancies! I would instead say the more immediate need for work is in the area of the agreement-- for there we actually have the potential to make theoretical progress in understanding what we are looking at.

But this is not what you usually find, generally the observers and theorists seem to simply crave some level of reassurance that theory can recover the observations, and simply substitute the cartoons when anyone asks them why. Then they move on to the next problem! I see a huge hole there, around the question, "what is really going on there, and how can we understand it in a better-than-cartoon way without simply referring to the full simulation?" To me, stopping short of that is like saying, "I don't know but my computer does, so that's good enough".
Exactly. I won't say I don't use google, nor that science cannot benefit from a similar approach to making predictions, but I do feel that it leaves a large gap in what science is capable of accomplishing.

Fair enough. But you can literally pick any theoretical investigation you like, the principle plays out almost everywhere. Instead of going over all the same ground three times, let me just pick one relatively common example and explore it in greater detail: mass-luminosity relationships in stars.

Certainly we have detailed stellar models that can generate mass-luminosity relationships expected in various types of stars. These models contain a vast array of physics, from nuclear physics to radiative transfer to gas dynamics and convection. They make predictions that observers can use for detailed comparisons. And I have seen many examples of observers comparing to the theory and worrying about systematic trends in the theory that are not seen in the observations. But when the theory agrees with the observations, they all go home, that's the end of it! They just say, "now we understand stellar interiors".

The problems with this are myriad. One is that it isn't them who understand, it is the computer. An even worse problem is the process tends to end when agreement is achieved, even though the agreement can certainly have serendipitous aspects that are not physically correct. Two recent developments in stellar interiors of massive stars are the inclusion of magnetic fields and differential rotation. One group recently found that including rotation gives better agreement with the observations-- so everyone is happy. But it turns out they also find that when they include the magnetic fields they expect to be there, the agreement gets worse! What if they just hadn't bothered? Is it that the magnetic fields aren't there, or was the excellent agreement they got from rotation just a complete coincidence?

A second problem is that we often encounter terms like "we included rotation", or "we included magnetic fields". I see it all the time:
observer: "But did you include magnetic fields?"
theorist: "Yes, they are in there too."
Now you may instantly recognize that this kind of thinking ignores the important fact that theorists always make a host of assumptions about how they will choose to "include" something, so it's not like they turned on a switch and God automatically entered the phenomenon in question into their code. How did they include it? Again there's not a need to write out a set of fundamental equations, there's a need to analyze the basic action of the field as it is playing out in that simulation, and what is it really doing to the results. If I don't see that, I basically don't believe anything.

A third problem is, even if the code has no important bugs, and even if the physics that is actually occuring is faithfully represented in the code, oftentimes the cartoon that is invented to explain it is either substantially incomplete or completely wrong. The mass-luminosity relationship of stars is a classic example of this-- it is amazing how many seemingly authoritative websites (again no examples needed, it's almost impossible to find a counterexample even if you try) will give bad or false explanations as to why massive main-sequence stars are so much more luminous than low-mass main-sequence stars.

What you will find, I assure you, are cartoons that include reasoning like "the higher mass raises the core pressure, which cause fusion to occur faster", or "the temperature in the core is raised by the strong gravity, causing fusion to occur faster". The faster fusion then results in higher luminosity, so they say-- the only trouble is, that cartoon is completely wrong! First of all, high mass stars are low pressure objects, not high pressure, and secondly, the temperature of the core is set by the luminosity, not the other way around!

The actual reason that high-mass main sequence stars, when you sit down and do the kind of analysis I'm saying is so often lacking, is that a high-mass star does not need to contract as much to reach core fusion temperatures. Thus you end up with a larger "leaky bucket of light", which is what a star is-- and a larger leaky bucket is a more luminous one. That's the reason, there is no need to mention any details about fusion because fusion does not control the luminosity of a main-sequence star (the luminosity controls the fusion), once you have an estimate of the core temperature of the star (fusion simply acts as a thermostat that maintains a relatively fixed core temperature, it is not necessary to know it accurately to understand the luminosity of a star).

If you find yourself having a skeptical reaction to that assertion, ask yourself two things:
1) How did Eddington understand stellar mass-luminosity relations long before anyone even knew there was such a thing as fusion, and
2) Does that reaction not prove my point that this analysis is widely missing, at least in this example?
But it usually does complete the interaction, that's what I'm saying. That interaction is the one that says to the observers "time to move on to new observations" and to the theorists "time to start working on new simulations, we're done here". The grant money is liable to dry up in both areas, as grant agencies begin to focus on newer "unsolved" problems.

Forgive me then, for I cannot discern from this description exactly what was accomplished by the Millennium Simulation, impressive label notwithstanding? I'm partly kidding-- I'm sure it will end up being useful, but what I'm saying is, I predict what will happen is that the physics will be tweaked and bugs will be corrected, until the agreement with observed numbers are achieved. Then an interesting thing will happen-- everyone will label the issue "solved", "paradox resolved", or some such thing, and will immediately move on to something else, after their various prizes and congratulations are accepted.

Maybe someone will also take the time to figure out what simple aspects of the simulation were actually at work in getting the number right, or maybe they won't, it will never be viewed as crucial-- it will just rely on "how good of a talk" the PI is capable of giving at the AAS meeting when it comes up. In the mean time, the "cartoon" will satisfy everyone, even if it's completely wrong (as in the case of mass-luminosity relationships).

__________________
Logic is the grammar of truth.

Meaning and absolute certainty are incompatible, and profound meaning and absolute certainty are profoundly incompatible.

The only thing intelligence is capable of is recognizing itself.

(continued and concluded)I think I'm beginning to see what you're saying; let me play it back as a check:

Conservatively, the best we can do, today, from observation is say that maybe some of the multitude of theories which purport to address "dark energy" are mildly inconsistent (there is "some tension") at the 1 sigma level*, but certain teams are boldly forging ahead, developing dazzling (and dizzying) models that they claim can deliver phenomenological predictions accurate to the third significant figure (or better). That's not such a bad thing; the behaviour you are concerned about has to do with what you see as a possible future acceptance of these models when the observations do, one day, get to that third significant figure.

How far off the mark am I?
We may be at cross purposes; the 'elaborate models' I was referring to are those used in the various chains that ended up constraining (or taming) the multitude of alternative explanations for the observations (dust, grey dust, cosmological evolution of Ia SNe, Ia SNe as a heterogeneous class, etc, etc). This is, as I'm sure you agree, a very necessary part of doing physics/science, but also tends to be very unglamorous^.

Indeed.

Yet these 'observational details' may well involve quite elaborate simulations, and the effort leading up to the 'first light' of something as complex as GLAST chock-a-block full of stuff that I think you'd agree is no less a "kitchen sink" model (or simulation). One difference is that the 'existence proof' of the models/simulations nearly always comes quickly and flaws may have fatal consequences (which was the Mars mission that involved a team using MKS and a team using miles/pounds/etc?). I think one thing you're saying is that in much of astronomy there is little, if any, opportunity for this kind of existence proof.

Also, don't you think WMAP and Planck are examples of (future) observations that are "theory driven" rather than "technology driven"? After all, no one would invest $$$$ to build a Planck if there were no LCDM models! LSST and Pan-STARRS are good examples of "technology driven", yet both are being fine-tuned to align with capability to test hot theories ...

Cool!

* caveat: I am not attempting to be complete or accurate; this is illustrative

^ for every paper tentatively concluding that, say, Ia SNe are heterogeneous (much less the wild exaggerations such a paper elicits in popsci mags, nor the glee with which certain BAUT members seize upon juicy bits quote mined), there are 100 tentatively concluding they are good standard candles; not one of these 100 makes it to any popsci mag article ...

Yes, that's more the issue, though whar I'm also saying is that we may never get to observations that can be meaningfully fit to 3 decimal places in astronomy-- fits at that level may always have a "google science" flavor. By that I mean the model may fit the data, but only at the cost of specifying parameters at a conceptually meaningless level of precision-- given the missing physics that is inevitably involved.
I am not against elaborate models, any more than I'm against using a net to try and catch a great white shark. But once you've caught it in the net, there's a need to isolate it from everything else in the net, and learn about its attributes. Then the next time one ventures near, perhaps a better and more targeted type of net can be designed. But what I see more often is the attitude "we got the shark, everyone can go back in the water".

In your example, we may find that certain kinds of dust are responsible for certain features. We can know that from the kitchen sink simulation simply by noticing the difference with and without that type of dust. But is that really all we want to know? Is it enough to say that dust "is included", and leave it at that? Or do we want to know why that type of dust makes that feature, and are our assumptions about that process really reliable, just because they allow us to fit an observation with enough tweaking? That kind of investigation is carried out by people who are interested in that type of dust, if there are such "shark enthusiasts" in the community-- but they tend to be found in "niche markets" that don't connect with the AAS meeting plenary talk on how nicely the kitchen sink simulation fits the data. Now, I'm not saying all astronomers have to address all questions, but I am saying that just because a kitchen sink simulation can fit some data by "including grey dust", it might not mean a whole heck of a lot by itself.
I think the disconnect there is that you are still referring to a rather different type of "simulation" than I am. Observers do talk about "simulating an instrument" and "modeling their data to remove noise" and that sort of thing, and those simulations and models may need to be quite elaborate. I'm talking about a simulation of a physical system that you are trying to understand by comparing the ramifications of your simulation to your data. Whenever you want to understand the system, rather than simply "treat" it, there is a need to go analyze the simulations to isolate the simple quantitative but approximate physics that is at the core of the phenomenon-- and that is what I find is all too often missing.
Well, I wouldn't say they are driven by the outcomes of complicated "black box" simulations with so much complexity that only a computer could keep track of it-- that's what no one would invest money in, given how fraught with peril such an approach would be! They are indeed theory driven, but it's more missing-theory driven. The missions are not just to add another significant figure to the model parameters, that would again be entering a realm of conceptually meaningless engineering adjustments to parameters that are quite likely to be missing physics at that level of precision. So I'd say the real goal of the missions is to look for any physics that we are missing-- physics that might have very simple and straightforward implications that do not require a mile of code to extract.
But is that fine-tuning done to be able to differentiate between conflicting outcomes of two groups doing black-box simulations, or to be able to distinguish between physical possibilities that have very straightforward, if not downright simple, implications?

__________________
Logic is the grammar of truth.

Meaning and absolute certainty are incompatible, and profound meaning and absolute certainty are profoundly incompatible.

The only thing intelligence is capable of is recognizing itself.

I would have to agree with Kapatin K... Unfortunately being "stuck" is part of what science is... But where definitely on the right track, while this might not be the "golden age" of advancements, it doesn't mean that "theoretical physics" is in "crisis".

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The first principle is that you must not fool yourself - and you are the easiest person to fool.
~~~ Richard Feynman ~~~
It is imperative in science to doubt.
~~~ Richard Feynman ~~~
Common sense is not so common
~~~ Voltaire ~~~

Would you please reference your remarks. Like with a quote, especially when the remark you're referencing is a month old and three pages ago!

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Any day you wake up on "the right side of the dirt" is a good day.

T. Anderson

Sorry about that, I must have accidently started with the 1st page and foregot that there were several pages that followed I'll try not to do that again.

__________________
The first principle is that you must not fool yourself - and you are the easiest person to fool.
~~~ Richard Feynman ~~~
It is imperative in science to doubt.
~~~ Richard Feynman ~~~
Common sense is not so common
~~~ Voltaire ~~~

No problem! If that's the biggest "oops" you make, you're ahead of the game (and most of us old timers)!

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Any day you wake up on "the right side of the dirt" is a good day.

T. Anderson

Am having trouble keeping up with you Ken G; I'm now two posts behind!

No worries-- I'll take a breath!

I'll use the break in the more interesting discussion, then, to answer Len.

Notice that the scientific model is a description of something, but a description is not a substitute or stand-in for for that something. It's that notion of duality that I think is being philosophically imposed on us without justification.

No, and this is the core issue I disagree with. As our golfer improves his putt, he does not become the putt. He just gets better at doing something. Knowledge and understanding are also constituted by doing something well. The difference between description and reality is not the same as between a plastic model of the solar system and the solar system. Refinements in scientific description do not move us closer to reality. That is just a metaphor. Rather, we get better at achieving something. We can make something happen in our lives that we could not do before.

We simplify by acting simply. We use one approach with Newton's recipes and another, more involved process, with Einstein's. We deal with the world in either case; we just take different actions and get different results with each theory.

I see science not as creating a simpler version of reality, but as discovering the simpler aspects of reality. An elliptical planetary orbit has circular aspects. Science discovers those and teaches us how we can in some circumstances treat an elliptical orbit as a circular one.

I don't know enough about QM is know what you mean by “predicted observations of a particle.” I thought QM did a sum-over-histories type of statistical prediction. I thought that QM specifically did not speak to any sort of underlying mechanism. “Shut up and calculate!” as they say.

Our differences consist in nothing more than you taking something literally that I take as a metaphor of speech.

Maxwell first learns to deal with cells and wheels, let's say. Later in life he learns he can successfully act in some of the the same ways with electrical devices. What exists here are: Maxwell, cells, wheels, and electrical devices. There is no model. Maxwell simply pretends that electrical devices are cells and wheels.

"Reality" is a metaphor too. Does understanding what that word means "move us closer to reality"? What do you even mean by that sequence of words? In short: word = metaphor, get over it! (I'm not being critical, just pointed.)

Indeed, we get better at achieving an understanding of our reality via using models. Of course the endeavor has limitations.

What is the essential difference between "discovering" and "creating something that works"? I think you'll be hard pressed to split that hair meaningfully.

But circles and ellipses are both creations of our minds. "Reality" doesn't include either of them.

Huh? Quite a few textbooks are going to be very surprised to hear that they are just paper and ink and nothing else.

__________________
Logic is the grammar of truth.

Meaning and absolute certainty are incompatible, and profound meaning and absolute certainty are profoundly incompatible.

The only thing intelligence is capable of is recognizing itself.

Quite simply that cosmology does not make accurate predictions but explains things after the event by adding new features to its theories. Cosmological parameters, Inflation, dark matter, dark energy, structure at large scales, age of galaxies problem etc.

That's not evidence, just rhetoric.

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"A witty saying proves nothing" Voltaire.
"All your bias are belong to us" Ara Pacis.

Well, an image that illustrates is what I mean by figure of speech or metaphor. I fully agree that it does illustrate exactly what you say. I am not questioning the metaphor but some of it's applications to which it is put in philosophy.

I agree. The terms knowledge and truth have different uses, different roles in our language and our lives. But it doesn't follow from that the knowledge refers to some sort of replica of truth (in the sense that a plastic model of the solar system is a replica, stand-in, or substitute of the solar system).

Let's say you purchase something that costs $17 and hand the clerk a $20 bill. The clerk says, “Thank you, sir. Here is your change of $2.” When you correct the clerk and point out that 20 minus 17 is 3 dollars (to the clerk's surprise that this is the case), you are not correcting a model of financial transaction the clerk privately possesses, but correcting the way the clerk deals with you and the amount of change you are handed. Improving knowledge is improving the way we live.

Scientific theories are not simplifications of data. Theories are not species of data. Rather, science discovers ways to do something using simpler portions of the data. Notice that science does not create a simple copy of the world. It simplifies by focusing on some aspects of the world and ignoring others. That is, science is better seen as a process of discovery and not of creation.

Yes, theories do not exist in a vacuum. Revision is a process of experimenting in various ways until particular results are achieved. Len's notions of a model on one side and inaccessible absolute reality on the other are nowhere to be found in what you mention here. Instead, we see just people trying to do something better. The high standards to which science is held are standard practices, ways that experience has shown us how science should be done to achieve some result.

Wittgenstein might say here that the differences you mention are grammatical differences, that is, differences in how the language is employed in the circumstances we encounter in life. Observing and theorizing, for example, are two different forms of activity involving both people and the world. In that sense there are not two distinct sides isolated from each other with one side serving as some sort of picture of the other.

That is not my view at all. I am a programmer. When I deliver a buggy program, my phone rings. At that point, I no longer feel isolated from the truth. The truth has my phone number. Our actions have consequences on our lives. How we act changes our lives. Science and all of our other pursuits are a matter of discovering ways to do things better.

Well said. Improving one's golf game is not an additive process either. There is nothing literally adding up somewhere, something whose quantity value is continually increasing with practice. Life is a richer and more complicated process than that.

What about the practicing golfer metaphor (or any analogy to someone improving his craft)?

But this is pure rhetoric. I already challenged you to back it up by making an actual meaningful distinction between these words. I haven't seen any attempt to meet that challenge, so I still view the words as completely empty. Will you cite evidence that scientists make better progress when they visualize their own behavior as a process of discovering what is already there, versus a process of creating a way of modeling what is there? When Galileo discovered with his telescope that lights went around Jupiter, was that fundamentally different from when he created in his head a model that Jupiter had moons orbiting it like our Moon orbits us? And are you not simply creating a model of the process of science yourself, when you claim that "discovery" is a better way to model what science does? Is this something you have discovered, or are you theorizing about it?

Indeed. And I'm sure Wittgenstein also realized that models are perfect examples of such "grammar". So Disinfo Agent mentioning "grammatical differences" is in no way inconsistent with his points about what models are-- it is just the point. How does your identifying that fact bolster your strange objection to the way Disinfo Agent is using the term "model"?

And I'll bet good money that the way you do that, in providing better programs, is by constructing a concept of a "model program" and trying to get your actual programs to approximate it to some degree. Or do you just write some random code and modify it until it works? I doubt it. We need only look at how scientific thinking functions to see the immediate and crucial importance of making models, even if you choose to call them "metaphors", as if that was anything different.

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Logic is the grammar of truth.

Meaning and absolute certainty are incompatible, and profound meaning and absolute certainty are profoundly incompatible.

The only thing intelligence is capable of is recognizing itself.

Language is indeed rich with metaphor. But as for what words mean, we typically learn that from contexts of use. I can wave my arms around to try to show you what “reality” means, but that doesn't capture all its more useful senses. Contexts of use are spread wide and smeared out over time. Would you say that they all collapse down to a singular entity—a “model”--that somehow packages all the senses into a singular something?

You tacked on the word “model” here to what I said, but I am not sure what it adds. Can a person understand something only by forming a model of it? If so, how does he come to understand his model?

The difference I was thinking of was “creating a simpler reality” versus “dealing with reality in a simpler way” or maybe “discovering the simpler aspects of reality.” You might show me how to estimate angles in the sky using my little finger or hand as a measurement device, which is simpler than using scientific instruments to do so. But I would not be creating a simpler sky. I would be measuring the sky using a simpler technique.

Circles and ellipses don't have to exist in the mind or elsewhere. We may be shown (imperfect) drawings in math textbooks that we learn to call circles, then we learn to relate other objects like hula-hoops to them. (“The hula-hoop looks like the circle drawing. The egg looks like the ellipse drawing.”) If talk of circles refers to anything, it is not to “creations of our minds”, but to physical objects like drawings (or rings or hula-hoops, etc.) that serve as a public referents for circles. Later in life, we learn stricter techniques for dealing with the objects we encounter as circular, but all without there being some sort of imaginary perfect circle that serves as a referent for the term.

What I am trying to do here, in case I am not doing it so well, is to look at what a teacher might do to teach a child about circles and ellipses and to identify what exists in those contexts and what takes place.

Whereas that doesn't apply to models and metaphors... how?
No one who knows what models are thinks their purpose is to package "all the senses into a particular something". To understand what models are, one should restrict to their actual purpose.

No one has claimed that models are the "only" way to do anything-- merely that they are an excellent way to do many things that define our intelligence. I think the abiilty to make models is a crucial element of what one might call advanced intelligence.

Here's how. He notices the way his mind thinks, that it seeks unifying characteristics of various seemingly separate things. Presumably this is an instinctive element of intelligence. Then he models those unifying characteristics as a single entity of some kind, like "mother" or "toy". When this unification is applied to the very process of seeking unification, the concept of a "model" emerges. So yes, the process of modeling is what is used to define the meaning of the term-- it's all about noticing what our intelligence is doing.

Well that's your problem right there-- no one who uses models thinks that it involves "creating a simpler reality". It merely involves creating a model, that is what is being "created". The model needs to be simpler than the reality, yes, but it does not need to be mistaken as a "type of reality". You are just adding that part yourself.

Circles and ellipses, in the precise mathematical meanings of the words, do exist in the mind, and they don't exist anywhere else. That's just a fact, and it means that they are both models.
Exactly, but a key step in that process is to notice things about circles, to find their unifying characteristics. That may be done vaguely, as a child might do, or precisely, as a mathematician might. All that is different is the sophistication of the model. If I asked you to draw me a circle, do you think you would call to mind objects that other people have told you are circles, and draw one of them? No, you would simply draw what you carry in your mind as your model of a circle. That's also how you know the size of what you draw doesn't matter-- you have figured out that the size of a circle does not affect its circularity, and thus you include in your model the concept that size doesn't matter.

The mathematicians who have proven all kinds of theorems about circles, entirely using mental reasoning, should be interested to know that. Last I checked, none of them ever proved anything by "referring" to a hula hoop-- they refer to mental constructs like definitions and axioms, which are, of course, the building blocks of models.

That is where you are quite wrong. The "imaginary perfect circle" is a crucial "referent", and mathematicians use that referent all the time. Those who don't use that referent have a much vaguer concept of what a circle is-- they are simply using a cruder model.
Yes, that's fine-- a teacher may indeed teach a child that way. And later, when the child's mind is more sophisticated, it will be time for the child to recognize that what he was doing all along was learning a model. Eventually, the child will understand how to master the models, and will begin to learn mathematics. Or they won't-- the general mathematical abilities of our youths is in many quarters in dismal shape. Maybe it's because they deny the existence and usefulness of models.

__________________
Logic is the grammar of truth.

Meaning and absolute certainty are incompatible, and profound meaning and absolute certainty are profoundly incompatible.

The only thing intelligence is capable of is recognizing itself.

It does apply to models and metaphors. In these types of discussions, when I think that somebody is trying to reduce meaning (or most any mental concept) to a singular something, perhaps in the head, I try to widen the focus to also include the environment and our achievements in that environment. The word "apple" loses its meaning if it doesn't somehow encompass apples and what we do with them (as well as what we think and feel about them).

For purposes of illustration, we might say that the meaning of something is smeared out a bit over space and time. You sometimes counter that is too complex and risks involving too much, whereas I think our talent as understanders is that we can manage such subtle complexities. We can learn from context all the different ways to apply the word "apple" (or "circle"). Experience shapes us into beings that are proficient in the world with such subjects.

Why can't you ever discuss models in a more direct fashion? You obviously see them playing a fundamental role. It seems that we should be able to better identify what they are and to characterize them.

Or, is that my problem here? I think that we should be able to talk about our philosophies, expand on them, and illustrate them with examples. Is that perhaps keeping me from seeing the truly important things philosophy has to show us? Are models (or consciousness, or selves, or perceptions, or anything else you and I bicker about endlessly) something I should be immediately and intuitively grasping through some special faculty of insight?

Instead of detailing models themselves, you just talk about what models supposedly enable people to do. But if all you can really talk about is human action, well, then that pretty much becomes my point that it is the action, and the contexts it occurs in, that matters here.

I agree with that. But I see modeling as, say, learning to drive an automatic car and then leveraging your skills at operating a steering wheel, gas pedal, brake, and transmission shifter to eventually operate a stick shift or maybe an airplane. We can say that we are modeling here without having to have formed a distinct model. We master the two or three different vehicle control interfaces. That's the "substrate," so to speak, of anything we can say about it. If we do choose to introduce the notion of "model", it still refers to achievements in the environment. "Model" is then be being used to illustrate the similarities between automatic, stick shift, and airplane controls and the similarities in the ways you operate them.

See how easy it is for me to talk about my philosophy? The world and all we do in it is available for my use. And a good deal of what I say is straightforward and understandable, even if you strongly disagree with its conclusions. Compare that to your explanation that follows for how someone comes to understand his model. (Don't get mad at me for saying that. These past few posts I have been trying to explain why I take the approach I do and why I think it may be better than the traditional or received approach.):

What I meant was if a person must form a model of "toy" to understand toys, how does he come to understand the model of toy he formed? A plastic model of the solar system must come to be understood if it is to help us understand the solar system it is a model of. How does a unified model make him intelligent about toys? I was also wondering if there are ways one could understand something that did not involve modeling that something. It seems mental models must be understandable without themselves needing yet another model (which would lead to an infinite regress of models.)

For contrast, the way I would put it is that once a person has experience with and develops some proficiency with a toy or some toys, including referring to them as toys, then he can leverage those same skills to talk about and identify, to ask Mommy to buy, and to play with other toys. This works because of both the characteristics of toys (the environment) and because experience has changed him. (Notice me widening the focus here somewhat, smearing your "unified model" a bit over space and time--not too wide, but wide enough to encompass the child, toys, and some of the surrounding circumstances.)

Notice that unification is achieved through the fact that toys share some similar features, have some similar uses, and that we can treat them in similar ways. Unification is a result of us acting economically, of leveraging existing skills, of treating this new toy like those other toys in some ways. Whereas you say a person notices his mind wanting to unify, I look to environmental factors and see how unified action benefits an organism.

A teacher has been teaching her students about circles. She asks Bobby to show her some circles. He draws here a circle on the chalkboard. He puts a ring and a coin on her desk. He points to the clock and to a hole in a sheet of punched looseleaf paper. She says, "Very good, Bobby. Yes, those five items are all circles." Bobby is being taught and encouraged to identify those objects as circles. Or consider art students practicing drawing circles and the teacher encouraging them for drawing good circles.

The point here is that the referents for "circle" are taught as being objects in the environment, but treated in a different way than say, "apple" might refer to apples. When Bobby later learns to use the phrase "circles exist in the mind," he does not discover a sixth item, a "circle in the mind." Rather, saying "circles exist in the mind" is just leveraging our grammatical skills at using the word-->object form to highlight the fact that the word "circle" is not used in quite the same ways the word "apple" is used. "Apples are on trees. Circles are in the mind." That is, we speak as if that in addition to the five items Bobby identified, there was a special sixth one, a "mental circle" that unified all the other five items.

So, I agree that it is correct to say, "circles exist in the mind." But I think it is also the case that such phrases refer to the wider context that includes objects in the environment and the ways we treat them.

To "notice things" and "find their characteristics" is an act that involves both the person and the objects. You haven't identified a more or less sophisticated unifying entity in that, but a more or less sophisticated way, say, a person may group objects together. There can be many reasons involving both the person's physiology, the objects, and the circumstances why he classifies this or that object as a circle. It doesn't all boil down to a singular, unifying entity.

No, when I draw a circle, I don't sketch my inner circle for you. Rather, I was drilled in a repeated process of tracing circles, presenting it to the teacher for critical review, and trying again to do better. Through that I became an organism that can draw a circle when prompted. (I am really being to kind to my drawing skills.) It's just like when I handwrite a note. I don't refer to a mental model of letter shapes and sketch them out on the paper. I learned to handwrite. In fact, if I was really just tracing an inner model of the letters, then I wouldn't properly be said to have learned to write. A person who is tracing is not yet proficient in the art of penmanship.

The reason I know size doesn't matter is that people don't correct me in such cases when I draw differently-sized circles. When I sign my name on the credit card receipt, the size of my signature tends not to draw funny looks from the cashier as long as it fits within the space provided. Again, the environment, my self, the requirements of those circumstances explain what I can get away with.

That's correct. That's another "form of life" that involves circles. But to the student first learning about circles, hula-hoops and the like are instances of circles. They never cease being instances of circles when the student extends his circle repertoire to include academic techniques.

Math teaches formulaic procedures. They are not species of "circle."

Notice that you find their "abilities" in dismal shape. They cannot deal with circles in many of the ways they could be doing so. The solution is going back to the classroom and drilling on new procedures. It's those procedures that concern us here. And that involves both the person and the environment. It doesn't reduce down to some singular unifying entity.

But that is not at all what you are doing in this discussion, ergo the objections you face. Had you actually been doing that, you would have been trying to bring attention to "the environment and achievements" related to making models. You would also have found out dramatically such modeling has assisted us in reaching these achievements, and gave us power over our environment. Instead, you have steadfastly and mysteriously attempted to downgrade the extreme importance of understanding and using models. That is where you have run into considerable difficulty here.

Fine, you are welcome to "smear" the meaning of "model" over space and time if you like-- but that still in no way refutes what models are and how completely we rely on them. Models are simplified descriptions that we create in our minds to unify what we view as the relevant or important elements of disparate complex phenomena. They can be vague if they are only informing vague predictions, or they can be mathematically precise and quantitative if they are informing quantitative predictions. That's just what they are, "smear them over space and time" all you like. In science, we tend to prefer the latter type, so we focus on situations where we can accomplish that.

And the way we do that is almost entirely by making models. You are even doing it yourself right now, when you say we "learn from context"-- you have a model in mind for what that means, otherwise, the words say nothing other than "we live". We already knew that part.

Well, you are certainly using them. So it only means, "pay attention to what you are doing when you think".

That's a preposterous claim. I have in countless instances defined what models are, and indeed did so again just above. Equally obvious is that what they enable us to do is part of what they are-- they allow us to unify experiences and make predictions at a useful level of reliability.

This is also just a game of words. Obviously every model I can cite was made by people, because I know nothing of how other animals, make models. Furthermore, we use our minds to make models, and we are therefore doing something. Thus your position here amounts to the tautology that the action of making a model is a human behavior. That tells me exactly nothing about models, but is pretty obvious, yes.

__________________
Logic is the grammar of truth.

Meaning and absolute certainty are incompatible, and profound meaning and absolute certainty are profoundly incompatible.

The only thing intelligence is capable of is recognizing itself.

That isn't what modeling is. A complex process like driving a car or flying a plane, or doing physics for that matter, would involve the application of many different models. A better description of "modeling" in regard to driving a car is what your conscious intellect does to try and understand how you drive a car.

Modeling is a self-aware process, whereas driving a car is effectively autonomic (no pun intended) most of the time. When you need models is when you need to make a driving decision, and then you need to model the outcomes of those choices based on a unified understanding of how the current situation projects onto whatever your model recognizes as relevant or important. Do you have time to brake, or must you swerve? If the conditions are slippery, does that change the relevant model? Should you include in your model the results of head-on versus glancing collisions? This is the part that conveys an intellectual understanding of the situation, and that's why I said that a baby begins making models at the point when its intelligence starts trying to understand its surroundings-- it knows how to cry long before that. Instinctive reflexes do not require any models. If you reflexively pull your hand off a hot stove, is that because you "understand" heat?
You are not describing the absence of modeling in driving, you are describing your own model of the act of driving.

The second sentence is not informed by the first sentence, as the first sentence is obvious to the point of meaninglessness. The second sentence is actually saying something about models-- they do indeed attempt to unify similarities, as has been said. Nor does that fact in any support your claims that we are not in fact applying models when we participate in these behaviors.

It is easy because you are not saying anything at all. I'm afraid nothing you've said sounds in any way like anything more than "everything we do involves doing something". This is our starting point before we begin to understand, it is not a moment of insight.

I believe I answered that, but I suppose it is an important enough point to bear repeating. We never understand toys, we only understand our models of toys. The very word "toy" involves a replacement of something real with something conceptual. Even if you claim that a toy is defined by how we use one, you are also doing a similar replacement, because if we trip over one, are we "using" it? You are modeling the meaning of toy even as you observe the behavior you are using to define the toy. A toy is a bunch of atoms, it has no idea it is a toy-- you are modeling it as a toy, and are simply choosing the parameters of your model (behavioral, in your case, but this is merely one choice of a way to model something).

The key point is, the human mind cannot understand reality any more than the word "toy" can be a toy. (The word "word" can be a word expressly because it is already a concept, i.e., already a model.) All we ever understand are words and concepts, i.e., models. Models are that which we can understand, and that's why we create them. That's simply because a mind can only understand what a mind can create. Water is water, rock is rock, and a rock cannot be made of water. So it is with the creations of our minds-- a mind only understands its own products. But this is easily mistaken for understanding the reality when the model yields a particularly good simulation of the aspects of reality it is intended to unify and predict. Indeed, our language is lazy and we say we understand the reality-- but no, we do not, we understand our model of reality, and as we have no other option, we call that understanding reality.

We don't "understand" the plastic model either, it is simply another real thing. What we understand is certain unified conceptual aspects of both the real plastic model and the real solar system, and we draw the connection, the unification. The plastic model (insofar as it replaces the solar system, not insofar as it is its own reality) gives us a clearer path to that understanding, but even the plastic model has many elements that are not part of what we mean by our solar system model (it is made of plastic molecules, etc.). It is our mind that makes the simplification, not the plastic spheres (which are of course no closer to spheres than actual planets are).

How does anything else? One can push around a toy without having intelligence about toys-- the latter comes from unification. Do we kick a ball because it is fun to feel the sensation, or does the fun come from noticing where the ball goes, and trying to attain mastery over certain repetitive tasks? Do we not generate a model of "cause and effect", and thereby feel that we are causing the ball to move, which connects to the concept of power over the ball, which is a model that is fun to apply and test? Via the repetition, we seek the unification that gives us "intelligence about toys", and the latter is the source of the fun.

I have to break here for space limitations...

__________________
Logic is the grammar of truth.

Meaning and absolute certainty are incompatible, and profound meaning and absolute certainty are profoundly incompatible.

The only thing intelligence is capable of is recognizing itself.

Continued...

I suppose it depends on the meaning of "understand", but when used to imply an intellectual understanding, as I mean the term, I'd say that word is more or less synonymous with modeling.
That is an interesting model of "toy" that you are describing. Yet, it is simply another model to help understand that activity. That is the core inconsistency in your position-- you are saying "here's my model-- see, it doesn't involve modeling". But it does.

But what is "it" that works here? Your model. We can agree that the purpose of models is to convey some intellectual advantage, unification, or simplification-- that does not make them not models. Everything you are talking about here is just how you model things. Those models are not unique, nor do they invalidate other models, nor do they invalidate the concept of a model, even as it applies to what you are doing.
To which I say:

Yes, your model involves some smearing, it tries to encompass more things, which has both advantages and disadvantages depending on the desired result, but it is silly to think that makes it not a model. In many cases, the things you encompass merely serve to weaken the power of the model, as would be true in almost all cases as applied to physics but certainly less so when applied to the models of psychology or sociology.
Yes-- that's the whole point of a model.

You misunderstand my point, I said the noticing the desire to unify is part of understanding the modeling process (modeling the modeling, the issue you brought up), I did not say, nor mean, that that is itself part of the modeling process. The self-referential nature of modeling modeling is not a contradiction, it is a key element of intelligence that remains an enigma.
But you are missing how Bobby knows those are circles even though they've never specifically been identified as such. It's simply because he has created a model of circles, and uses the model to recognize those similarities in reality.

No, he says, "yes, that is obviously true." The circle in the mind has always been there, it is the way he developed the ability to identify circles. Otherwise, all he could do is recall items that have been associated with that label.

This can be shown by an example. If I tell you that 2, 3, 5, 7, 11, 13, and 17 are the first seven "prime numbers", you could then identify those numbers as prime if you saw them somewhere else. But what would you need to do to identify 19 as prime? You would need to notice some characteristic of these numbers, in this case a mathematical property. Once you notice that property, you have created a model for what a "prime number" is (in mathematics, we can skip this and refer directly to the definition, but with reality, we never see the "rule book"). To the extent that this model works when applied to other numbers, you will say you "understand" prime numbers. But without that model of what primeness means, you would never have any hope of finding other prime numbers. It is just the same with circles, their mathematical property is just a little simpler to notice than the property that makes a number prime. No model, no capacity to generalize, no understanding.

That would be the incredibly clunky way our minds would have to work if we could not make models for these things. Fortunately, we can. That is basically the difference between "google science" and inteligence-- google can only categorize and differentiate, never model, never understand.
No, "apples" are in the mind too, insofar as a-p-p-l-e is part of a language that exists in our minds. The word represents something we find in a tree (or something that is represented by the word "tree)", but when something stops being an apple, or a tree, is a completely arbitrary element of semantics, invented by us. Reality has no idea what an "apple" is, and can make a continuous distribution of a collection of atoms from what we call DNA, to a seed, to a green apple, ato red apple, to a decayed worm-eaten apple, to some worm excrement. The rest is all us, all our models of what we decide is the important attribute worth calling an apple.
Correct-- there is, and that is the important one for the intellectual activity of understanding. We use it all the time-- little progress has ever been made without it, despite your complaints.
To my ear, that just says a circle is a type of model. You are simply modeling what a circle model can entail, but your model for conceptualizing a circle is far from unique, and as I said, has advantages and disadvantages. It might be useful for understanding the sociology of the role of circles in human culture, but it would clearly be terribly overblown and useless for developing mathematical theorems about circles, and that's why it is not used for same.
The way you are choosing to model it, perhaps. The "reality" is none of those things-- it could also be viewed as just a bunch of atoms obeying laws of physics-- except that too is just another model for what is happening. It's models all the way down-- get used to it!
You can sure split a hair, but I see no important difference in those statements. Do you?

So the theorems I can prove about circles are unique to my own physiology? Thank goodness models don't work that way! This is supposed to convince me you have a "better way" to think about circles?
I'll bet that is not at all how you learned to draw circles. I'll bet you did it the same way everyone else did-- my forming a model of what a circle should look like, then trying to create one yourself. You didn't need to show the result to anyone, you could just look at it yourself, and compare it to your concept of circle (perhaps informed by a reference circle that you could see did conform to your mental model). When it didn't look like the referent, you modified and tried again, training your hand using the referent which in turn was informed by the model. After awhile, you could do the comparison with your own idea of a circle, your own model.

This is quite easy to prove. Have you ever drawn a circle, using that so-called muscle memory you are describing, and then looked at it and erased part and redrawn it better? Did you do that because of some muscle memory in the process of drawing it? No. Did you do that because you referred to something you were told was a circle and noticed a problem? No. I know perfectly well what you did-- you compared your result to exactly that inner concept of a circle that you claim you don't have. How else would you be led to redraw it?

You see, our inner model of a circle is the whole point of a circle, that's the whole reason we are talking about circles here and not the coastline of France. You probably have a vague model of the coastline of France, and no muscle memory in drawing it, yet you could do your best at drawing what you remember. You will be referring to your inner model of the coastline of France. But we are talking about circles because they are so much easier to model, so much easier to unify, than the coastline of France. Your teacher insisted on you being able to model circles, but not the coastline of France, expressly because the former is easy to model and the latter quite difficult. It is a perfect example of the power of models, and when they are most useful.
No, that would be the case if you were a trained pigeon with no intelligence. In fact, what you did was to unify that lack of correction into a concept of "size doesn't matter". That then becomes part of the model. As you become more sophisticated mathematically, you can even solidify this into a mathematical concept called "scale invariance". Your insistence that such modeling skills are irrelevant is quite pointless, as it denies essentially all of mathematics and how we use it, in favor of dog training.
Certainly not, that would be the last thing I would suggest! Instead, they need to understand what mathematics is, and how to apply it for their benefit, in terms of the application of intelligence not dog training. Are people in charge of curricula thinking like you do? No offense, but that would explain a lot.

__________________
Logic is the grammar of truth.

Meaning and absolute certainty are incompatible, and profound meaning and absolute certainty are profoundly incompatible.

The only thing intelligence is capable of is recognizing itself.

The scientific method is far from obsolete. Datamining and other methods of study can tell us what occurs, but it cannot answer the question of why. The scientific method provides us with such scientific laws as F=ma; PV=qRt. These require the scientific method to take the data that has been collected and provide a reasoned understand of the underlying mechanism at work.