The model is a common and very important scientific tool, perhaps the most important, in the natural sciences, life sciences and medicine as well as in the social sciences, where it is used to describe economic, managerial ("business model") or social processes. In the humanities, too, its place is not neglected and models of the ways of the development of ideas are quite common.
What do the model, the London subway map have in common with a common scientific-research tool, perhaps among the most common in science and the word more common than sex on Google? This is not the beginning of a joke, and the answer: they are all models. Is there a connection between all these models or is the connection purely coincidental like between a weapon and a kiss? The answer: They are all strictly kosher models.
Indeed, the term "model" is used, in science and in everyday life, in many and varied contexts that apparently have no connection whatsoever. Most of us, if not all of us, were introduced to the word model early in our lives. The model was called in the past, even before the Hebrew Academy renewed this word, "model", although it was not always clear whether the model was the model or rather the clothes she was showing, or both (the answer: both). Another model that we also become familiar with at an early stage in our lives is that of various buildings (Jerusalem in the days of the Second Temple, ship models, etc.). Also in medicine, different anatomical, morphological or functional patterns are recognized, whose function is to copy precisely (similar to models in dentistry) or to imitate and demonstrate in a tangible and easier to understand way different phenomena or different mechanisms (like through the action of the "double helix" in molecular biology). We will see later that the London subway map also meets the definition of the model, even though it is called a map and not a model. The model is a common and very important scientific tool, perhaps the most important, in the natural sciences, life sciences and medicine as well as in the social sciences, where it is used to describe economic, managerial ("business model") or social processes. In the humanities, too, its place is not neglected and models of the ways of the development of ideas are quite common.
What is the secret of the model's magic? What is the secret of its power in science and medicine that it is so common? Does this result from the fact that the human mind is a virtuoso model maker and that we understand the world, about its phenomena, through models or schemes? It is possible that this is the only way to explain that this is perhaps the most common word in the Google search engine, even more than sex. The word model (singular and plural) has 1,370,000,000 mentions, while the word sex has 746,000,000 "only". If the reader's feeling that the reason for this proliferation is attributed precisely to top-models of the type of Bar Refaeli, he may not be completely wrong, but still hundreds of millions of references refer to scientific models of various types (of which at least 66,600,000 references are to models in the field of medicine and health). 205,538 scientific articles on medical topics carried the word model in the name of the article itself (source: PubMed). The word "model" appeared in the names (title) of theses of 160 theses for higher degrees in medicine and related sciences, cataloged in the library of the Medical School at the University of Jerusalem.
Perhaps we should focus on the nature of the word and the definition of the term. An accepted, but perhaps dry, definition of "model" is: "a form, pattern, plan, representation (usually miniature) whose purpose is to describe an object, system or idea". But it seems more interesting and challenging to define the widespread and distributed use of the word "model" precisely in terms of negation: all models are characterized by the fact that they are never the "real thing" and that their purpose is to demonstrate how the "real thing" looks and works in all kinds of levels and contexts. It seems that not all uses of the word meet this definition. Is the model not "the real thing"? Actually no, since the purpose of the display is for the potential buyer to imagine how the garment would look on her; The model, like a hanger or mannequin, is just a tool for this. Let's look at another usage that apparently is also not in line with the definition: when you point to someone and say "she is a model for an ideal woman" is it not meant to be a woman of flesh and blood, concrete, real. Of course, but this is an example of the elusiveness of words, since in this case the use of the word "model" is borrowed: we actually mean to say "if we were to build a model whose features resembled an ideal woman, the set of features of this woman would serve as a solid basis for this model."
The models in general and in the natural sciences in particular include mainly mathematical models but also abstract models or graphical models. We will insist on two important features of models.
The purpose of the model and the field of its definition: an important feature of models is the field of definition, which is often limited, of their purpose. The model usually does not describe the phenomenon in its entirety but refers to one aspect or aspects of it. For example, the purpose of the subway model (map) intended for passengers is to define the location of the stations and the transit options (connections between them and nothing else. This model does not have a complete description, or even aspires to be one, for the simple reason that this is not its purpose. For example, the fact that the red line is drawn above (North) its corresponding blue, means nothing. It is possible that the physical train line is located to the north of it or to the south or below it. Another model (map, drawing) used by the subway maintenance and rescue personnel will answer this. If, in another example, we want To demonstrate the effect of a residential tower that will be built on its surroundings, it is not necessary to include in this model of the tower the internal division of each floor into apartments and rooms. The same is true of the research questions that the model is supposed to address and answer: they should be well defined and also often limited.
Assumptions of the model: in each model there are overt or hidden assumptions based on proven facts or a reasonable approximation of them (for example the assumption that the left ventricle of the heart is shaped like a ball or an ellipsoid, although we know that this is not exactly the case but approximately). The purpose of the assumptions is to allow the model to be represented by solvable mathematical parameters and to become an effective tool.
The models in science are many and varied, with each scientific field having models with their own characteristics and classifications.
To shorten the story we will focus below on models in physiology and medicine. To demonstrate the essence of these models, it is perhaps appropriate to classify them in the following evolutionary or hierarchical way:
1) Models of perception: models of perception, i.e. the "illustration" "insight" or "perception" of the phenomenon, in familiar patterns of thinking, are of great importance in science, even if they are sometimes surprising in their simplicity (and sometimes cause the response: "How come we didn't think of that" previous"). A good example of this is the basic concept of the nature of the operation of the muscle, whether it is a skeletal muscle that works in the limbs and allows us to move and exert power or whether it is the heart muscle. We know that the muscle during its contraction is capable of exerting force and/or shortening, but how do we concretely describe its action in general and the duality of creating force and shortening in particular? Let's look, for example, at the well-known model of muscle contraction devised in 1938 by the great English scientist and Nobel laureate AV Hill. In this model, the muscle is described by two components: one is a contractile component, from a generator that upon receiving the electrical stimulation "creates force and shortening" and the other, connected to it in a column, is an elastic, spring-like component. The purpose of this model is to describe the force generation process in contraction. Despite its simplicity and the narrow scope of its purpose, this model at the time advanced the research to understand muscle action to a significant extent. The greatness of the model is that, for the first time, it offered a concept of how the muscle works. Despite its extreme simplicity, and perhaps because of it, this model is still used today as a basis for understanding muscle action and as a cornerstone for more advanced models. This model expresses and demonstrates the idea that we understand the world, about its phenomena, through models (of perception) or schemes. According to this view, the human mind is, as mentioned, a virtuoso model maker. However, sometimes there is probably a need for pre-processing (creating a familiar model like Hill's with tangible elements like a generator and a spring) so that the "entry" into the brain is smoother and simpler.
2) Models for understanding the "activation mechanism" of a phenomenon. After we have a perception of a phenomenon, there is a need for more detailed models that will offer concrete mechanisms for explaining a possible course of action of unknown phenomena, found in what are called "black boxes". The "black box" is characterized by the fact that we can feed it with "inputs" (inputs) and receive its responses (outputs) to these "inputs", but we do not have access to what is happening in the box itself, inside. It should be emphasized that one phenomenon can be described by different models. The model is based on the existing experimental knowledge and complements it with necessary assumptions. In the example of the heart muscle, for example, a model is required that describes the force generation mechanism ("the response") that changes depending on the heart rate or as a result of irregularities in the heart's action ("the inputs").
3) Models that include simulations: these are often more detailed and comprehensive mathematical models based, often but not necessarily, on the models described above. Weather forecasting models, the results of which we see on the television screens, are an example of mathematical models with multiple beacons, parameters and variables with complex simulation software that requires a lot of computing power. If we return to the muscle example, here too models with simulations can be expressed in diverse ways. For example, in models of walking, the simulation will provide the description of the mobility, efforts, pressures and forces in the foot or other areas of the leg, all according to the purpose of the model. In the area of the heart muscle, it is possible to simulate the operation of the left ventricle, for example, based on the forces exerted by the heart muscle on the volume of blood in the ventricular space, the description of the pressure developing in the chamber and the development of pressure and flow in the arteries after the opening of the aortic valve, depending on the ability of the heart muscle to contract, the heart rate, the pressure in the aorta before opening the valve etc. This kind of simulation can help, among other things, in testing the effect of drugs or in the construction of artificial valves by examining the flow profiles they create. Sometimes in models of this type, the importance of the simulation is in testing the behavior of such systems in a wide area and especially in extreme situations: for example, the output of the heart at very low or high rates (in an example from another area: finding the endurance limit of a bridge for abnormal loads). The possibility of testing the behavior of the model in dependence of many variables in a wide area may, with proper planning and no common sense, save precious resources or also reduce human tests or animal experiments.
In conclusion: The accepted way of creating a model is to characterize it by defining its purpose and make assumptions (usually simplifying) and then, depending on the type of model, also build mathematical equations and computer simulation software, the result of which depends on both the values of the parameters entered and the range of values of the variables. Computer software of this type allows the researcher and his colleagues to observe the behavior of the system and especially to examine its response as a result of changing the values of the parameters and in this way stand, in a relatively cheap and easy way, on the essence of the phenomenon or process being examined and its sensitivity to changes in various parameters. The credibility and effectiveness of the model are often tested both in its ability to explain phenomena observed in the past and in its ability to predict future phenomena. The use of models has advanced science and medicine to an unimaginable extent, although no patient has yet been cured as a result of ingesting "models" for breakfast.
Dr. David Adler served until recently as the director of the Department of Instrumentation, Medical Engineering and Communication at the Hadassah Medical Center at the University of Jerusalem and his field of research is "the role of models" in general and "models in the cardiovascular system" in particular.
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Another reason for using a concrete model - beyond the ones I mentioned in my previous response - is the lack of the ability to draw certain conclusions computationally - either due to difficulties in the calculation algorithm or due to a lack of data necessary to perform it.
Be that as it may - the concrete models are also found naturally and unconsciously in the behavior of the individual and without scientific intention - each child formulates the conceptual model of "dog" while basing himself on the concrete models he has met.
As I have already mentioned in other discussions - the entire scientific method (and the models as one of the tools used in this method) are a transfer of the process of drawing rational conclusions from the private and partially unconscious space to the public and conscious space.
David Adler:
The concrete models are also meant to represent a conceptual model and in fact they express a compromise we make when the detailed description of the conceptual model is beyond our ability - whether due to the lack of suitable terms in the language, whether due to complexity or as a result of laziness.
In fact - anyone who has taught mathematics - and especially geometry - must have often encountered mistakes that people make when they draw conclusions from the concrete model that are not correct in general and are simply a feature of the concrete model they used.
To all respondents
Sorry for the late response to your scholarly comments. I will address first first.
L.H.: Of course "there is a lack of reference to animal models...". There is a lack of reference to almost all the models that exist in the various disciplines, except for the few that I referenced to demonstrate one or another principle. All references in the article stemmed from a personal point of view and from my experiences and thoughts on the matter. If I write a book about "models", such a claim may be valid, in part.
To Amichai: As for your claim that models may lead their creators to "fixation", in a certain direction it is interesting and I do not reject it outright. If you mean the "human" tendency to receive reinforcements from results that confirm the theory or "direction" that the scientist seeks to prove and to examine with seven eyes a result in the opposite direction, then I agree that it is the mother of every sin in science and "true" scientists should check themselves, again and again, that they are not sinning in it. I admit that what has always bothered me in the approach of certain colleagues is the lack of symmetry: on the one hand, the tendency to unquestioningly accept a result that confirms the theory that the scientist believes in and, on the other hand, to trace the "abnormal" source (so-called or not so-so) of an opposite result and try to test it perhaps "Artifact" for example. But it exists more precisely on the experimental side. That is why I am inclined to Michael Rothschild's answer that "it is true whether he understood it through a model or whether he understood it in another way".
To Amichai: a request to address religions. My reference to R.H. Above answers this to some extent. But more than that is enough for the models to deal with the explanation of phenomena in the physical world, and it is better to leave them alone from dealing with metaphysics.
To Michael Rothschild: Your wording "the use of models in science is nothing more than bringing into the conscious realm of a process that we do anyway and that we have no option not to do", is interesting and perhaps in line with the opinion presented in the article "". At the same time, it is clear that this is true only to a partial extent and only in the conceptual model type, if at all (see the Hill model example I gave).
To refresh: you write "besides that, every model is necessarily only an approximation of reality or the "truth", due to the principle of the butterfly effect (chaos) and sensitivity to initial conditions..." . I did emphasize the "approximation" issue in my article, but the reasoning that it is "because of" the chaos is far-fetched. The model is an approximation made consciously due to the complexity of "reality". In simple reality the approximations will be minimal. "Chaos" has nothing to do with it. The "chaos" can be caused in models with certain properties of non-linearity. It is true that in these models, the sensitivity to changes, even tiny ones, in the initial conditions is enormous (hence the name "butterfly effect"). But what is the connection to the issue of approximation. A very simple explanation and demonstration of this from a theoretical point of view can be found in the appendix to the articles by Adler et al: Am. J. Physiol. 253:H690-H698, 1987 If you want to prove its practical implication you can do so in Adler et al.: Circulation Research 69:26-38, 1991
fresh:
I think I have already argued with you before and discovered that there is no point.
Therefore, I will content myself with saying that as a mathematician familiar with Gadel's theorem, I can tell you that although you have the right to think so - you are completely wrong.
Lenaam
You have the right to think so but you are wrong.
fresh,
Every scientific theory is:
1) Approximation of reality
2) Confirmed, until disproved
The above two assertions are true even if it were not for Gadel's incompleteness theorems and chaos theory.
Godel's theorems do not at all prove what you said (in fact they say almost the opposite: in a finite axiom system of a certain type, it is ** never ** possible to prove or disprove certain theorems), it's a shame that you are confusing yourself and others.
Amichai is 100% right
Indeed, the models, like all science in and of itself, are true and valid only for the present when in the future there is a possibility that the model will turn out to be wrong, and the law of incompleteness proves this, and the law of incompleteness is so strong that it is also valid for itself.
Apart from that, every model is necessarily only an approximation of reality or the "truth", because of the principle of the butterfly effect (chaos) and sensitivity to initial conditions, and therefore we will probably never discover the "theory of everything" i.e. the model of everything.
Defining the shape of the left ventricle of the heart as an ellipsoid is fine as long as you understand that it is not really an ellipsoid and therefore the predictions of the model are only approximations, and if the approximations are good enough then that is what matters.
Amichai:
Unfortunately I have no idea what you are talking about.
The things you said in the previous comments were clearly wrong, but here I no longer know what you are trying to say.
I do know that if this is meant to justify the previous comments - it is necessarily wrong.
Machel
As we know, there is no mathematical formalism for describing and calculating models in a general way.
One of the main reasons stems from the circularity of the matter.
After all the mathematical formalism itself is based on a model.
The comparison with the incompleteness theorem is only analogical.
I referred in this analogy to the fact that when you come to check the central structure of any model,
So there is usually a match for the prophecies.
But the further you get to the consequences and the consequences of the consequences etc.
Results are obtained that there is no way to decide them.
You will find this feature in any model. which at a certain point cannot be denied just as the results cannot be verified at this point.
In addition, it can be seen intuitively that there is no such thing as a completely closed model in such a way that it has no open edges.
Amichai:
Are you serious?
How did they uncover the error in the Newtonian model of gravity?
How is it that they exposed the mistake in the model that put the thoughts and feelings in the heart?
How does science even progress if not by exposing the errors in the models?
Your words are simply not true and if you say that they derive from Gadel's incompleteness theorem, you simply show that you do not understand this theorem.
Machel
All models have the feature that it is not possible to reveal the mistakes in them.
This is a conclusion that follows from the incompleteness theorem.
The limits of all models do not converge.
Amichai:
The truth is, I really don't understand what you are trying to achieve.
To predict the behavior of a system you need to describe it in a way that maximizes the features you value.
The description can be by a set of axioms or by an example.
Once you have such a description (=the model) you can investigate it and draw conclusions.
If the model does represent reality - you can make predictions.
If not - you can make predictions...wrong.
The incorrect predictions will eventually be discovered and reveal the inadequacy of the model.
As I said - the model is also exposed to further criticism - apart from the predictions it issues - and this is thanks to the ability to challenge its validity.
The validity of the models provided by the religions is easy to challenge - both due to their disproved predictions and due to their basic assumptions.
That's why sane people can't believe them.
If you find a model in which the mistakes cannot be revealed, then for every practical need you have found... the truth!
When I said that I don't understand what you are trying to achieve - I meant it in all seriousness.
Are you asking humans to stop thinking?
After all, all our thinking is based on models that we build in our minds to plan our steps in the future.
The use of models in science is nothing more than bringing it into the conscious realm of a process that we do anyway and that we have no option not to do.
You too - when you describe the "harmful effect" of models - use a "model of models" for this purpose, but the model you use does not take into account our ability to reject models that do not work and as a result - is itself an invalid model.
Dr. David Elder
In your introduction to the article (after further reading) the reference to religions is conspicuous in its absence.
Especially since models in general are an ancient invention of religions and not necessarily of modern science.
So how do you explain that modern science is better at using models than religions??????
Machel
The problem with models is much more complex and lies in human nature.
People tend to create an overlap between some truth and the model that represents that truth.
This thing is not limited to the sciences.
Beliefs in general and religious beliefs in particular grow from some model that is the fulcrum of these beliefs. and is a central marker of the nuclear content of truth as that model is identified with.
You cannot separate such human behavior in the realm of religious beliefs from those dealing with scientific theories.
Things like these cause people to remain captive in the same concepts without being able to free themselves from them.
It should be remembered that a model does not represent truth but merely a certain way to a certain solution.
And you have to remember that for every solution there are many competing solutions that may be much better.
Amichai:
The problem you describe is not related to the models. It belongs to any situation where the scientists think they have understood something.
Once someone thinks they have understood a phenomenon - indeed - it is more difficult to convince them that they did not understand, but this is true whether they understood it through a model or whether they understood it in another way.
However - understanding reality is the goal of all scientific activity, therefore the feeling of understanding should not be condemned due to this phenomenon. A scientist should try to keep his openness but still he should try to understand and the models help him in this.
The truth is even more interesting: the use of models can even help the scientist to change his mind because if the model is not correct it is possible to show the scientist what is wrong with the model and does not correspond to reality! In other words - contrary to the conclusion that the scientist draws "out of thin air", the model has many points where it can be challenged.
To illustrate - imagine that someone builds a mathematical model based on which he deduces what happens inside a black hole.
We currently have no way to directly test what happens inside a black hole, but usually we will have a way to show that the model is incorrect (of course - only if the model is incorrect)
Nice article:
I hope that those who did not understand some of my previous responses such as, for example, these (in which I spoke about a model of chemistry):
Response a
Response b
will understand them now.
Dr. David Elder
Models also have a bad side.
Because people tend to settle according to a certain and fixed way of thinking/perception/approach from which it is impossible to get out.
A conspiracy theory is also missing... 🙂
Interesting article. However, there is a lack of reference to the advantages and disadvantages of animal models from bacteria through yeast, worms, flies, mice and many other animals without which biology and medicine would not have reached the current achievements.