Requirements for LR model - please explain.

In summary: I should get an honorary degree from the UN?In summary, the requirements for an LR model of entanglement are that it must provide a dataset with three simultaneous settings (a b c) that match the predictions of quantum mechanics in order to meet the realism requirement. This is necessary because EPR implies that there are elements of reality that do not depend on observation, and Bell's theorem shows that a local theory must have at least three simultaneous elements of reality. Many proposed LR models fail this test and are not considered seriously, while the De Raedt model, which provides a dataset with three settings, is being actively researched.
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ThomasT
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Requirements for LR model -- please explain.

In a recent, and ongoing, thread it was asked what are the requirements that an LR model of entanglement must meet. The following response was given by DrChinese. I still don't understand what he's talking about, so if DrC, or anyone else, would care to clarify his 'clarification' it would be appreciated.

I don't foresee replying to any responses to my query. Hopefully, some more knowledgeable people than I, including but hopefully not limited to DrC, will reply and I'll then use those replies as a point of departure for future considerations and research. And, of course, if I consider any reply to be obviously incorrect, then I'll post what I currently think about it. Thanks in advance.

DrChinese said:
I demand of any realist that a suitable dataset of values at three simultaneous settings (a b c) be presented for examination. That is in fact the realism requirement, and fully follows EPR's definition regarding elements of reality. Failure to do this with a dataset which matches QM expectation values constitutes the Bell program.

ii) Clearly, Bell (2) has only a and b, and lacks c. Therefore Bell (2) is insufficient to achieve the Bell result.

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So to expand on these:

i) There are a number of papers that "purport" to provide local realistic models. But they do not provide datasets (with 3 angle settings) which match the QM expectation values. That is to be expected, because Bell discovered that there are no such datasets. Why is a dataset important? Because it was known already that datasets with 2 angle settings were possible. In fact, that was more or less one of the EPR conclusions although they did not really specify that single point. What they did specify was that the existing (at that time) QM program could be made "more complete" with additional parameters, yet to be discovered.

So local realistic theories with 2 simultaneous settings are missing the boat, precisely because they describe something which is not prohibited by Bell. And if they did offer the ability to provide a 3 setting dataset, they would simply provide it and Bell would be overturned. So I don't really need to read and de-bunk each purported solution until and unless a dataset can be provided.

In the case of the De Raedt local realistic computer simulation, on the other hand, such a dataset is provided. So naturally I DO take it seriously and am actively involved in working with a respected member of their team to understand their model and its characteristics. Keep in mind that it is a simulation, not a true physical model. However, the success of their model would open the door to a physical model - if it can survive questions that are inevitable.

ii) One of the recent questions on this board concerns whether Bell (2) is a sufficient assumption to achive the main result. You can see for yourself - as can anyone who will simply look - that it does not involve 3 settings, but instead only 2. The Bell program requires the assumption of at least existence of 3 simultaneous "elements of reality". In the EPR program, there was only 1 (let's call it a), because that was all that could be predicted with certainty. But they said in their closing papragraphs that it was reasonable to consider that any element of reality individually should reasonably be considered to exist independent of actual observation. So this is the counterfactual case: b, c... etc. They needed this because it was essential to their claim that QM was incomplete. Bell accepted the "challenge" and considered a, b and c, achieving his now famous result. But you cannot get it - as far as I have seen - with just (2). You need after (14) too.
 
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Sure, I'll try and I hope others will throw in too. I may not hit it all in one pass.

First, we would start with EPR before proceeding to Bell. EPR implies that a) there are elements of reality that do not depend on the act of observation; b) you might expect perfect correlations of entangled particles would be observed for some of those elements of reality; and c) generally a local theory might exist in which these elements are consistent with quantum mechanics.

Second, we add Bell and the explicit realism requirement: there must be simultaneous values for each element of reality, and at least 3 must be demonstrated (since Bell shows this is not possible within the framework of a theory which matches the predictions of QM).

So the basic idea is: i) demonstrate you have a local realistic theory; ii) demonstrate whether that matches the predictions of QM (or not).

With the De Raedt model, for example, I can actually simulate - on an event by event basis - data values for 3 different settings of my choosing. So I can actually look at the resultant values - I collect 5000 in my spreadsheet model. Then I can analyze and see how close it comes to QM predictions. Of course it can't match them, it only approaches the Bell inequality limit. But it absolutely passes the test of realism.

Now, if you put forth a model that does not accomplish this, then you are essentially conceding the main point: you don't have a realistic model. Most LR models being published fail this essential test - which is why no one bothers to refute them. I mean, why bother to refute a model that flunks it main raison d'etre?

If the author argues they have a realistic model and then don't show how to derive 3 simultaneous values at settings I choose, then they are ignoring Bell entirely. That is equivalent to a magician who claims his magic is real but won't allow you to look up his sleeves. Rabbits don't actually appear ex nihilo.

(Hey, I used phrases from 2 languages other than English in one post!)

So the 3 setting requirement is a simple way to find out if Bell was considered or not. It is not enough to say you considered Bell without actually passing the test. Since this test is quite severe, and quite easy to grade as pass/fail. Once you have a successful model, then other requirements can be applied. Such as agreement with experiment, which becomes a big problem.

Arrivederci. :smile:
 

Related to Requirements for LR model - please explain.

1. What is a LR model?

A LR (Linear Regression) model is a statistical method used to identify the relationship between a dependent variable and one or more independent variables. It is commonly used to predict or estimate a continuous outcome based on the values of the independent variables.

2. What are the requirements for building a LR model?

The main requirements for building a LR model include a dataset with a continuous dependent variable and one or more independent variables, the assumption of a linear relationship between the variables, and the assumption of normal distribution of the data.

3. How do you test the linearity assumption for a LR model?

The linearity assumption can be tested by creating a scatter plot of the dependent variable against each of the independent variables. If the data points form a roughly straight line, the linearity assumption is satisfied. Alternatively, statistical tests such as the Durbin-Watson test or the Breusch-Pagan test can be used to assess linearity.

4. What is the significance of the normality assumption in a LR model?

The normality assumption is important in a LR model as it allows for the use of certain statistical tests and helps in obtaining more accurate predictions. It also ensures that the residuals (the difference between the actual and predicted values) are normally distributed, which is a key assumption in LR model analysis.

5. Are there any other requirements for a LR model?

In addition to the main requirements mentioned above, other important considerations for a LR model include the absence of multicollinearity (high correlation between independent variables), the use of appropriate transformations for non-linear data, and the inclusion of relevant and significant independent variables in the model.

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