Michael Mozina wrote: Nobody likes to admit being wrong
I think there is the problem.
If you are NOT using empirical physics, you will ALWAYS be wrong after some time.
The current mainstream trend has been to focus on the models instead of the observations.
If it were the other way around, we could be discussing what models would fit the observations best.
But instead (in the mainstream) we are discussing observations that do not fit the mainstream model,
and putting them aside.
This problem is not just with astronomy, it is in many branches of science, like biology.
There are so many surprises in biology that were unbelievable 50 years ago.
But to change a model, the system of money and careers that is supporting that model has to be
revised too. There are now many people following careers into "big bang", "gravitational waves",
"Magnetic Reconnection" etc.
All of these will be similar to numerology/ astrology.
What would you do, if you had a PhD in numerology?
Keep the numerology, but make some changes to it.
IMHO, the cure is, not to say that their models are wrong, but to say that
science only works when we start with observations instead of the models.
That is what we should discuss.
The models are approximating reality, not reality itself.
Because our technology advances, we can get better observations of the same phenomena,
and these improved observations might possibly not fit our models.
And this is often the case, especially in astronomy after 100 of years.
The "dark matter" is a great example of that.
Something that scientists do not understand is that, Dark matter is NOT an observation,
it is an model-interpretation of an observation. And these observations are currently being
refined, and show that certain variations of the dark-matter model do not work.
The big bang is something similar. It is only a model based on the red-shift observation of stars.
A totally different problem is that many people simply do not understand the maths behind it.
And they use a twisted version of the maths to "proof" the stuff that they invented.
The Pi=4 discussion (which I am against, see mad theories) is in that sense not much
different from the general relativity discussion.
If people do not understand the maths completely, the discussion becomes an interpretation
of the maths. The famous idea that 1+2+3+4+5+6 = -1/12 is good example of bad maths.
Of course it isn't, but you can define a special + function that acts that way.
If we look at general relativity, Steve Crothers shows how the complex Tensor maths
has been used in a wrong way.
I notice that people find it harder to admit their maths problems than the problems with observations.
This problem might be solved with computer simulations.
A computer simulation will require to put some of the theory into practical maths.
There is a cure
Yes. In the programming industry we have no problem with people using bad models or bad maths.
These people produce bad programs, and make less money.
Luckily many of the programmers listen to other programmers to how they can
improve their models and maths. Especially in open-source software.
So there is a push to educate yourself and others.
There are many many different programs that are still functional.
Also many programs look alike, because they work with similar technologies and use similar models.
They also respect older technologies and older programs.
They often have solutions that still can be useful today.
And if someone would make a huge improvement today, all other programmers would
like to understand that improvement and add their own version.
If someone has made a huge mistake, everyone wants to know about it too,
and change their programs if necessary.
So the problem is really in the world of science, not in the world of programming.
So what are the requirements for science to do the same:
1. Open source = Open science.
Open and free available documentation, including the process and models behind it.
2. Bug report = Open reports and side research.
We need to know when something works and when not.
3. Running Program = Observation.
Does it work in practice?
Is it useful?
4. Continuous improvement = Adaption.
Can we make it better?
5. Open Source Forking = Allow alternatives.
Other models are fine too, as long as they work.
We do not need to criticize other models.
Your model needs to be better, for the task that you use it for.
6. New technologies = New science models.
You can try anything and see if it works.
7. Allow complaints about problems.
We learn from mistakes and problems, even if we can not solve them now.
In science we only have a few theories with a fixed set of models,
for which you have to pay to get more information about the actual research.
But you are (almost) never allowed to see the actual details and data.
You also have to follow a full education to understand them.
You are not allowed to improve them or make your own,
unless you spend many years doing work for someone else,
who is also against any change in any model.
In the programming world we have many programs that you
can download for free. And there are free tools with which you can make
improve them or make your own, after downloading the free open source.
You are even encouraged to do that.