I'd really be interested in what you think about this clip:
Unfortunately, I don't have 20 minutes for a video at the moment, whatever is the video about. Here, I however see two reasons that do not motivate me to watch.
The title image of the video does not make much sense to me. We have a bunch of observations and the standard model of cosmology offers a very simple way to fit them. There is no reason to say that this model is wrong or correct or even has to be debunked. It is just a model that provides a good fit, and that can be used to make testable predictions. That's all.
It could then serve to understand better how the universe works and what is the true theory of nature. This could be guided by the successes of the model, as well as by the observed anomalies (that could in particular lead to throw the model to the bin, which is not the case yet by the way).
Finally, other options different from the standard model of cosmology exist, but they do not provide a fit to data that is as good as in the case of the standard model of cosmology (at least to my knowledge, which is what it is).
Now I move on with the second reason I won't watch the video. The Thunderbolt project (as advertised on the first image of the video) pushes for the electric universe idea. This framework does not allow for quantitative predictions, as we already discussed in another of my blog. As long as those are not there, there is just nothing to compare.
When the electric universe idea could be used to make quantitative predictions, then we could compare them with data, which will yield conclusions about the viability of the idea and compare the goodness of the associated fit to that obtained from the standard model of cosmology or any other cosmological model. We are however far from being able to do so.
These two things together does not motivate me to watch the video. Sorry to disappoint you.
Let me finish with a side note. If someone comes with a new idea (this is fine), this person must show that the idea actually works. This includes predictions, comparisons with data, etc. As far as I know, this is not there for the electric universe. This is, I think, the main reason why most scientific ignores it.
Cheers!
Thanks for the response; I think that I understand your reasons (from your point of view), but I feel it is quite a pity that the sciences have become so separated from each other (if you allow me to make this broad generalization). There are so many good scientists like yourself that seem to me at least to have a fairly narrow field of view when it comes to knowledge about competing models and theories within one's field of qualification, but also in other disciplines. Of course there are good reasons for it (with lack of time probably being the most important one). But like you stated before you have never really looked into the EU and also the plasma universe (there are differences) which then seems to me a strange starting point from where one could judge these paradigms.
Finally, other options different from the standard model of cosmology exist, but they do not provide a fit to data that is as good as in the case of the standard model of cosmology (at least to my knowledge, which is what it is).
This then seems to me like a closed knowledge loop which is destined to stay within its boundaries.
Anyway, I wish you much success with the models and theories you work on!
There are so many good scientists like yourself that seem to me at least to have a fairly narrow field of view when it comes to knowledge about competing models and theories within one's field of qualification, but also in other disciplines.
I don't think it is related to being narrow (at least I like to think that way). I am currently working on half a dozen subjects (some of them being even competing with each others, somehow), and I have worked on numerous different topics in the past. The real problem is that my time is limited and I cannot work on everything. I therefore decide carefully on what I focus.
But like you stated before you have never really looked into the EU and also the plasma universe (there are differences) which then seems to me a strange starting point from where one could judge these paradigms.
I indeed didn't. I quickly noticed that such a framework does not allow for quantitative predictions, which is a good reason to me to pass. I don't see why I should work on something that cannot be compared with data. Note that for almost the same reason, I don't work on string theory (although in this case predictions can be made; they are however far from being testable with current data). This is my choice.
Finally, other options different from the standard model of cosmology exist, but they do not provide a fit to data that is as good as in the case of the standard model of cosmology (at least to my knowledge, which is what it is).
This then seems to me like a closed knowledge loop which is destined to stay within its boundaries.
I mostly added the second part of the sentence to protect myself from making a wrong statement. From all alternatives I investigated, none of them seem to make a fit to data that is as good. Modified gravity theories are one example (which you may dislike ;) ). This is the reason why I decided to focus on standard cosmology as part of my research. In fact, dark matter barely consists of about 1/5 of everything I work on, most subjects having nothing to do with dark matter.
I hope this clarifies a bit my previous statement.
Cheers!
and to add, I think the general "problem" is just how complicated the world is! It is super tough for the human mind/brain to wrap its head around this world we find ourselves in. I sometimes feel we only really understand 1% (at best) of nature and much of the rest could be too difficult for us to really comprehend. And then there are the thousands of papers in one's field that one has to "read" in order to stay on top of things which is basically also an impossible task (again the time argument). Perhaps artificial intelligence could be one of the next steps to really gain greater insight?
Machine learning and artificial intelligence techniques are already widely used, but more to solve specific problems and not to get a general knowledge of a field (from what I know as this is a bit far from my zone of comfort).