Anthropic Principle vs Copernican Principle

[Nicholas Kang]

They are simulations based on available data.

 

I have mentioned simulations are not as reliable as observed data.

 

The point is that, at certain scales, it is that simple. It seems that where ever we might be in the universe we would see galaxies that have a limited range of sizes and structures, composed of stars and gas and - apparently - dark matter, and assembled in groups, some small some large. It's the same everywhere.

 

I mean the company I work for. It is a large multi-national service and supply company within the oil and gas drilling industry. I do not work as a geologist: that was simply my original training and my continuing interest.

 

Wow, what are the same? It is that simple at one point, what/which point, how to measure that point? galaxies that have a limited range of sizes and structures? how wide the range is? 50 types, 100 types... some small some large but still same? The inner stars, dusts,gases, planets... aren`t same.

 

Large MNS. I bet it is Shell. Can you tell me which company?

[Ophiolite]

 

Wow, what are the same? It is that simple at one point, what/which point, how to measure that point? galaxies that have a limited range of sizes and structures? how wide the range is? 50 types, 100 types... some small some large but still same? The inner stars, dusts,gases, planets... aren`t same.

 

Large MNS. I bet it is Shell. Can you tell me which company?

There are fewer than a dozen types of galaxy. The same types are seen where ever we look. There are a dozen types of stars. The same stars are seen wherever we look. It appears that there are a limited number of kinds of planet. It is early days, but it seems they are going to be the same kinds wherever we look.

 

If Darwin thought every finch on the Galapogos islands was different he would never have been able to develop the theory of evolution. He generalized and saw that the differences on any one island were small compared with the differences between islands. You are looking at the galaxies and seeing what is different, not what is the same. That is the wrong approach in science.

 

(I separate my work life from my forum contributions. I do not share who I work for, though I am happy - where appropriate - to discuss what I do. I do not work for Shell. Shell is an operator. Operators own the oil fields, or have been granted rights to develop them. The bulk of the workers in the oil industry work for service or supply companies. These companies own the drilling rigs, provide the technical services and the varied equipment needed to drill and complete wells. That is the segment I am in. Enough off-topic.)

[Essay]

I have mentioned simulations are not as reliable as observed data.

 

Wow, what are the same? It is that simple at one point, what/which point, how to measure that point? galaxies that have a limited range of sizes and structures? how wide the range is? 50 types, 100 types... some small some large but still same? The inner stars, dusts,gases, planets... aren`t same.

 

Nicholas,

 

I think this is mostly a problem with your definition of homogeneous. While homogeneous means 'all the same' (I think), it needs to be thought of relative to (or compared to) the 'heterogeneous' alternative.

 

In other words, what are the criteria (or rules or qualities or characteristics) that you would use to define something as heterogeneous? It is by those standards or "rules" that you should consider whether or not something else is homogeneous, rather than defining homogeneous as meaning "exactly the same" everywhere.

===

 

It might be better to think of 'homogeneous' as meaning an "evenly mixed" or "evenly distributed" mixture of stuff, whereas 'heterogeneous' would be an "unevenly mixed," or an "unevenly distributed" mixture of stuff.

 

You're right that nothing can ever be exactly the same everywhere, if you examine each and every detail. But if you are only interested in certain details, such as the physical laws that govern motion or energy transfer, then you can overlook the little differences that aren't relevant.

 

Homogenized milk is the "same everywhere" except at the molecular level, where it is still a mixture of water and proteins and some fat molecules. But non-homogenized milk has differences in the distribution of that same stuff; so it is only "relative to" non-homogenized milk, that homogenized milk can be thought of as "all the same" stuff.

 

~

[MonDie]

Do you realise the unequally sized-black space/dots? They seem to vary in size, only the purple chains look the same.

Nicholas, you're looking at Stringjunky's image at the wrong scale, looking at the individual parts rather than the broad pattern. However, there's even a problem with how you look at the individual parts. You're pointing at outliers.

In statistics, an outlier is an element of the sample that's far outside the standard deviation from the average. Clinical naricissists are the outliers—they're rare—, so I can deduce that I'm probably not a narcissist. In general, odds are that I'm normal rather than an exception. If I'm normal, the norm is me. Hence, odds are that most people are like me.

 

Imagine this is an image of two possible universes. You live in a red region of a universe. Which universe do you probably live in: a predominantly red universe, or a predominantly white universe?

 

 

One problem, however, is determining how large the standard deviation is. I'm probably not clinically narcissistic, but I could easily be significantly more narcissistic than most people if narcissism is highly variable between individuals.

 

---

 

If I am wanting to do an cosmology on a large scale I must assume (note; assume not know) that things on a larger scale are broadly homogeneous. I need to be able to say that the reason superclusters x, y, and z behave in the way they do is part of a universal rule and not because they happen to be where they are.

 

(hyperlink for nicho)

Actually, the anthropic principle says that we could just as easily live in an outlier. The anthropic principle says there is selection bias.

Edited August 7, 2014 by MonDie

[Nicholas Kang]

Ok, Essay`s answer is clear, at least providing me an alternative way of looking at the whole story. Thanks, Essay your post deserves a +1 from me and for MonDie, I am also fair to him. A +1 is given to him too. Thanks for teaching me a new term in statistics and at least correcting me my mistake when I analyze/look at a graph. Thanks to both of you, but for MonDie, I have one question.

 

Shouldn`t we analyze the graph into every specific point and maybe spikes or other shape of the graph etc... when analyzing scientific data? How can we overlook them, maybe they contain important features that could lead to a new scientific discovery?

[MonDie]

I don't deserve the +1.

 

Is it a graph?

Regardless, they're not "outliers". I was applying the term much too loosely.

 

I hardly know any cosmology. If you understand the logic, that's as far as I can take you.

Edited August 13, 2014 by MonDie

[Nicholas Kang]

Your words are dissapointing me. I +1 to express my gratitution to you. Usually, I point out the small mistakes but leave out the obvious one.

It isn`t a graph, but I mean using your concept to analyze daily graph encountered in Science and Maths. I apologise to you if my message above isn`t clear.

[MonDie]

Thanks, Nicholas. I'm glad you noticed problems with my post.

 

Nicholas,

I think this is mostly a problem with your definition of homogeneous. While homogeneous means 'all the same' (I think), it needs to be thought of relative to (or compared to) the 'heterogeneous' alternative.

In other words, what are the criteria (or rules or qualities or characteristics) that you would use to define something as heterogeneous? It is by those standards or "rules" that you should consider whether or not something else is homogeneous, rather than defining homogeneous as meaning "exactly the same" everywhere.
===

It might be better to think of 'homogeneous' as meaning an "evenly mixed" or "evenly distributed" mixture of stuff, whereas 'heterogeneous' would be an "unevenly mixed," or an "unevenly distributed" mixture of stuff.

You're right that nothing can ever be exactly the same everywhere, if you examine each and every detail. But if you are only interested in certain details, such as the physical laws that govern motion or energy transfer, then you can overlook the little differences that aren't relevant.

Homogenized milk is the "same everywhere" except at the molecular level, where it is still a mixture of water and proteins and some fat molecules. But non-homogenized milk has differences in the distribution of that same stuff; so it is only "relative to" non-homogenized milk, that homogenized milk can be thought of as "all the same" stuff.

~

This is a good post. My previous post diverged from the topic. Whether our localation is average is irrelevant to overall homogeneity.

 

It has occurred to me that homogeneity is ambiguous. If I'm sampling a pond, the pond's boundaries may be an objective feature of the pond, but my sampling scale and my significance threshold are both arbitrary. Which puddle is more homogeneous, puddle one or two?

 

Small scale, high significance

Hypothetical Puddle One contains evenly distributed colonies of microbes. Because the colonies are evenly distributed, differences in microbe density fall below statistical significance at low magnification. However, at high enough magnification, one observes regular differences in microbe density with p-values of .999 or higher.

 

Large scale, low significance

Hypothetical Puddle Two has a higher concentration of hydronium ions on the south end, but the trend only has a p-value of .85 when the analysis includes every hydronium ion.

 

Statistical significance only becomes irrelevant when any variation is completely patterned and non-random, but even then homogeneity is a continuum, like tallness and shortness. I don't think we can argue that it's absolutely true that the universe is homogeneous.

Edited August 13, 2014 by MonDie

[Nicholas Kang]

Thanks, Nicholas. I'm glad you noticed problems with my post.

 

Ah?! Now I don`t understand. What problems?