[entropyfails]

$this->bbcode_second_pass_quote('Ludi', '
')I strongly disagree. Assigning meaningless numbers does nothing to increase our understanding. The number of neurotransmitters and activated neurons may have little to do with anything meaningful about emotions, such as the degree of discomfort I experience while reading and posting on PO.com.

[Ludi]

Dear me, this is difficult and takes us a bit OT.

The numbers do nothing to explain the quality of experience of the mental state.

Hmm. I wish I were a better communicator.

Suffice to say, you and I have a very different understanding of the universe. Not saying your way is wrong, just very different.

If I'm understanding you correctly, you say we can plug meaningless numbers into Bayes Theorem and it will tell us they are meaningless?

I'm sorry I'm such a dolt, but, there it is, we all know it.

[entropyfails]

$this->bbcode_second_pass_quote('Ludi', 'D')ear me, this is difficult and takes us a bit OT.

The numbers do nothing to explain the quality of experience of the mental state.

[Ludi]

Ugh.

If we already know the numbers are meaningless, why do we need Bayes Theorem to tell us they are?

(my training is in art, not science - all the science junk I've had to pick up on my own)

[entropyfails]

$this->bbcode_second_pass_quote('Ludi', 'U')gh.
If we already know the numbers are meaningless, why do we need Bayes Theorem to tell us they are?

[Ludi]

$this->bbcode_second_pass_quote('entropyfails', ' ')You may have the general feeling that constantly eating your home leads to ruin, but having numbers that prove it so makes it much easier to get people to change their minds.

[entropyfails]

$this->bbcode_second_pass_quote('Ludi', '')$this->bbcode_second_pass_quote('entropyfails', ' ')You may have the general feeling that constantly eating your home leads to ruin, but having numbers that prove it so makes it much easier to get people to change their minds.

[Ludi]

I don't know how to turn the data into probabilities...

[Ludi]

Sorry, I don't know how to do that....

Maybe you can do it. Here's some numbers:


•Our world population has grown more since 1950 than it has in the pre v i o u s four million years. With these additional people come additional demands on our earth: eighty perc e n t of the original rain forests have been c l e a red or degraded; one-third to one- half of the Eart h ’s land surface has been transformed.
•We lose one or more entire species of animal or plant life every 20 min- u t e s—some 27,000 species a year. This rate and scale of extinction has not o c c u rred in 65 million years.
•C u rre n t l y, 505 million people live in countries with water- s t ress or water- s c a rce conditions. By 2025, almost 48% of the Eart h ’s population–between 2.4 and 3.4 billion people–will be living in a reas of water stress or scarc i t y. This f reshwater shortage could intensify diff i- culties in meeting human consumption levels, and wreak devastating effects on our delicately balanced ecosystems.
•Only 0.3% of the planet’s water is available for human use. Due to
mismanagement, over 40% of the g roundwater in the U.S. is contaminat- ed by industrial, agricultural, and household pollution,making it e x t remely difficult and costly to purify.
•It takes 23 times more water to pro d u c e 1 ton of beef than it does to produce 1 ton of grain. Only about 2.5 billion people could be fed on a diet compara- ble to a developed country diet, in which approximately 35% of calories a re derived from animal sourc e s .
•Americans are only 5% of the world’s population, yet we consume 25% of the world’s re s o u rces. Resulting social and environmental problems re v e r b e r- ate around the world.
•Six million acres of prime farm- l a n d—an area the size of Ve rm o n t —w e re lost in the United States alone between 1982 and 1992. Four of those six million acres were usurped by urban and suburban expansion. The other 2 million acres were lost t h rough erosion caused by defore s t a- tion, unsustainable farming practices, and animal over-grazing.
•While the number of people living in 58 US metropolitan areas rose 80%
between 1950 and 1990, the land cov- e red by those areas expanded 305%.
•One U.S. citizen consumes about 30 times as much as a citizen of India. If e v e ryone on earth lived like the average N o rth American, it would re q u i re four m o re earths to provide all the material and energ y.
•Although the U.S. accounts for less than 5% of the world’s overall popula- tion, we produce 25% of all gre e n- house-gas emissions. In 50 years the U.S. will add 114 million people and Africa an additional 1.2 billion. Yet, it’s expected that the carbon emission for the U.S.’s 114 million will be the same as for Africa’s 1.2 billion.


http://www.populationconnection.org/Com ... 202002.pdf

[bobcousins]

$this->bbcode_second_pass_quote('MonteQuest', 'A')nd just what is your contribution here, besides just another ad hominem attack that does nothing to address the merits of the debate?

[smiley]

I like the effort Entropy, but I fear that Bayer's theorem would not be practical in this case. In order to use these kinds of theories you need to define your triggers in extremely clear and verifiable terms.

The term large blackout is subject to interpretation. How many megawatts is a large blackout? Will you be inclined to rate a blackout in the west higher than one somewhere else due to the press coverage?

Then you have to have a good dataset. Such a dataset should include the entire world, since it is no given that the effects will start everywhere simultaneously.

I experienced several blackouts affecting a few million people in Cuba. None of them were recorded by the international press. So your dataset is already missing one of the countries which is most prone to blackouts.

You have to be extremely careful not to let the human judgment into the equation. I think the subject is too ambiguous to avoid that. Then you shouldn't embark on these kind of studies. You'll end up like the scientist who tried to verify the existence of God using these methods, and came to probabilities ranging from 1% to 99% depending on their personal conviction.

[rockdoc123]

Wow....probably one of the best threads to be started here in my opinion. I am a huge fan of Baysian theory.....pretty much every projection I make for my field of endeavour is based on Monte Carlo or similar approach. I think this is a huge challenge however....as stated earlier pretty much everything can be expressed by a mathmatical equation or numbers can be applied to everything....that being said it requires a huge database of numbers to add any statistical validation to the results...as well....we have the old "unknown variable" issue. There are so many variables already in this equation that I think it is probably better handled with chaos theory....but not being a maths person I won't go there.
This is a worthwhile approach..but make sure you identify independent versus dependent variables....it makes a huge difference in the result. Just my tuppence worth.

[smiley]

I agree with rockdock that the amount of variables are going to pose a problem.

perhaps we should try something simpler.

There are a great number of predictions for the oil production: WOCAP Campbell, EIA, DOE, World Bank etc. Perhaps this methodology can be used to rate these models.

I mean you're just talking about one parameter oil production vs time, which is something which is verifyable and undisputable.

just an idea.

[entropyfails]

$this->bbcode_second_pass_quote('smiley', 'I') like the effort Entropy, but I fear that Bayer's theorem would not be practical in this case. In order to use these kinds of theories you need to define your triggers in extremely clear and verifiable terms.

The term large blackout is subject to interpretation. How many megawatts is a large blackout? Will you be inclined to rate a blackout in the west higher than one somewhere else due to the press coverage?

[Ludi]

How do we apply that data to Bayes Theorem? I mean the numbers I posted above?

[entropyfails]

$this->bbcode_second_pass_quote('rockdoc123', 'W')ow....probably one of the best threads to be started here in my opinion. I am a huge fan of Baysian theory.....pretty much every projection I make for my field of endeavour is based on Monte Carlo or similar approach. I think this is a huge challenge however....

[entropyfails]

$this->bbcode_second_pass_quote('Ludi', 'H')ow do we apply that data to Bayes Theorem? I mean the numbers I posted above?

[entropyfails]

$this->bbcode_second_pass_quote('smiley', 'I') agree with rockdock that the amount of variables are going to pose a problem.

perhaps we should try something simpler.

There are a great number of predictions for the oil production: WOCAP Campbell, EIA, DOE, World Bank etc. Perhaps this methodology can be used to rate these models.

I mean you're just talking about one parameter oil production vs time, which is something which is verifyable and undisputable.

just an idea.

[Andrew_S]

Bayes’ Theorem is used to update a conditional probability distribution in the light of new data. So it deals with random variables, for example A and B and their interdependence. You seem to be taking two mutually incompatible hypotheses (Olduvai and Civilization) and treating them as if they are random variables (which they are not: they are hypotheses).

To put it another way, on the real number line let Olduvai be called 1.0 and let Civilization be called 0.0. You seem to be trying to estimate the relative merits of the two mutually incompatible hypotheses as if there exists a continuous parameter from 0.0 to 1.0 which can be estimated. However, as stated 0.0 and 1.0 represent two incompatible hypotheses, i.e. nothing in between.

Another way of putting it, you have defined what's characteristic of Civilization and what of Olduvai. You could just as easily say, oh things are 0.5% Olduvai-ish this week, I'm not really a fan of that hypothesis. Then if a year later things are 95% Olduvai-ish you could say yeah there's a lot to be said for Olduvai. No need for Bayes' theorem there. Bayes' works in retrospect, it doesn't predict.

A parameterization of the problem is what you really want. Some knowledge of how the parameter(s) determine the outcome (Olduvai or Civilization) and a means to estimate the parameter(s) and their time course might provide a means to test the relative merits of the hypotheses.

I like the idea of some sort of rigorous testing, though.

[seldom_seen]

I like the idea of what you're doing, but it seems that applying a mathematical formula for calculating probabilities to whether industrial civilization will go on as is (civilization theory) or collapse (olduvai theory) is the equivalent to say filling up your gas tank, driving around all day long and using Bayes theorem to determine whether you will run out of gas or not.

The answer is already deducible through scientific method. There's is a %100 chance for the 'run out of gas theory' based on the continual drawdown of a finite amount of gas in your tank.

As MQ pointed out there is a 0.00% future probability for civilization theory. The planet is finite and industrial civilization requires the constant intake of raw materials for endless growth. Therefore, industrial civilization will at some point be stopped in its tracks. This isn't debateable in a scientific context. It's only debateable, or questionable if you haven't grasped the concept that we live in a finite world.

The only undetermined factor is time.