Difference between "realistic" and "predetermined"

In summary, the conversation discusses the meaning of realism and its relation to determinism in quantum theory. It clarifies that realism does not necessarily imply determinism, as there exist realistic interpretations of quantum theory that are not deterministic. The concept of "predetermined measurement outcomes" is also discussed, with the conclusion that determinism is not necessary for a theory to be considered realistic. The conversation also touches on the EPR experiment and how it relates to local realism and determinism. Ultimately, the conversation highlights the complexities and subtleties involved in understanding and defining realism in quantum theory.
  • #1
greypilgrim
548
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Hi.
A while ago, I apparently had a wrong idea about the meaning of realism. I stood corrected:
greypilgrim said:
Realism means that all measurement outcomes are predetermined.
Ilja said:
No. This is only an (unfortunately very popular) misrepresentation. There exist realistic interpretations of quantum theory, and in these interpretations there is no such predetermination. Instead, what is misleadingly name "measurement result" is only a particular result of an interaction with something called "measurement device". The outcome of this particular experiment, in this situation, is predetermined, but depends not only on the state of the particle itself, but also on the state of the "measurement device". And, once for all the other imaginable "measurements", there is no "measurement device" and no corresponding state, these other "measurement results" remain undefined.

I'm still struggling with those subtleties. Would following formulation be more appropriate:
"Realism means, that every observable ##x## is attributed a probability distribution ##p_P(x)## that might depend on a set of parameters ##P## such as the settings of the measurement device (e.g. polarizer angle).
Local realism means, that only parameter set in the measurement's past light cone can affect this probability distribution."

Also, I don't quite see why "predetermined" is necessarily wrong. Doesn't the Bayesian interpretation of probability basically say that probability emerges as a lack of knowledge? What's the difference between a predetermined measurement outcome that's just unknown and an observable that exists as a probability distribution until a measurement projects it onto one specific outcome?

Or do we need "not predetermined" here to allow for the experimentator to have free will when setting the measurement parameters?
 
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  • #2
greypilgrim said:
What's the difference between a predetermined measurement outcome that's just unknown and an observable that exists as a probability distribution until a measurement projects it onto one specific outcome?

I think there is a difference in the predicted distribution of results. If one could toss a coin with absolutely identical initial conditions and stimulus time after time, one predicts the exact same result for every toss, or at least I predict that. Its an impossible experiment to carry out as far as I know, but maybe its possible to build a machine that tosses a coin, makes it flip 100 times and always land heads, even if the stimulus and starting conditions are not exactly identical toss to toss. One can imagine such a device even if we can't make one. That is a pre-determined measurement outcome that is just unknown (until the first toss has completed). If there is some inherent randomness in the coin dynamics that cannot be accounted for physically except to say 'there is randomness' then even under the hypothetical identical starting conditions and stiumulus (tossing, I mean) the one expects some spread in the results due to the randomness.
 
  • #3
It's a matter of opinion as to whether realistic in the sense of Bell (and Einstein) implies determinism. One could imagine a local stochastic process: Brownian motion, for instance, where at every moment, particles randomly choose among several options for their next state. I would consider such a model realistic, even though things are not predetermined.

But there is a double connection between realism and determinism, as applied to EPR:
  1. There is no way to reproduce the perfect correlations predicted by quantum mechanics for the EPR experiment using a locally realistic theory unless that theory is deterministic.
  2. Given any locally realistic theory (deterministic or not) there is an equivalent locally realistic theory that is deterministic that makes the same predictions. So if you rule out all possible deterministic locally realistic theories, then you also rule out all possible nondeterministic locally realistic theories. So it's good enough to consider deterministic theories in your analysis.
 
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FAQ: Difference between "realistic" and "predetermined"

What is the difference between "realistic" and "predetermined"?

Realistic and predetermined are two terms that are often used in the scientific community. While they may seem similar, they have distinct meanings and implications in research and experimentation.

How do you define "realistic" in a scientific context?

In science, "realistic" refers to something that is based on or closely resembles reality. This can include data, models, and experiments that accurately reflect the natural world.

What does "predetermined" mean in science?

In science, "predetermined" refers to something that has already been decided or planned before any actual data or evidence is gathered. This can include predetermined hypotheses, experimental designs, and outcomes.

Can something be both "realistic" and "predetermined" in science?

Yes, something can be both realistic and predetermined in science. For example, a researcher may have a predetermined hypothesis and experimental design that is based on realistic data and observations of the natural world.

How does understanding the difference between "realistic" and "predetermined" impact scientific research?

Understanding the difference between "realistic" and "predetermined" is crucial in conducting unbiased and accurate scientific research. It allows researchers to distinguish between actual evidence and preconceived notions or biases, leading to more reliable and valid conclusions.

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