Recent Speculation

I Timothy 6

20Timothy, guard what has been entrusted to your

care. Turn away from godless chatter and the

opposing ideas of what is falsely called

knowledge, 21which some have professed and in so

doing have wandered from the faith.

Grace be with you.


The word translated 'knowledge' here is also

translated 'science'. Here's the KJV:


20 O Timothy, keep that which is committed to thy

trust, avoiding profane and vain babblings, and

oppositions of science falsely so called:

21Which some professing have erred concerning

the faith. Grace be with thee. Amen.


There is a difference between science and

speculation. Problem is, folks tend to be so

scientifically illiterate that they can't tell

the difference. Another aspect of that, is the

difference between revelation and speculation,

and in technical terms exegesis and eisegesis.

http://dictionary.reference.com/browse/exegesis

http://dictionary.reference.com/browse/eisegesis

The greek for 'science' is gnosis or gnosi.

There are opposing ideas that are falsely called

'science'. That would be the false idea that

evolution is science, among other modern myths.


What I'd like to look at, is that there is a

difference between science and speculation.

Every discussion related to origins, is mostly

based on speculation, when it is put in the

context of science. In the context of scripture,

the discussion of origins is based on Divine

revelation. What we need to look at are the

facts, just the facts. It's impossible to prove

either doctrine correct. But it is possible to

identify where errors lead to speculation, and

that speculation is not science.


Since the origins conflict is an aspect of

pre-historic man, there is no scientific

observation involved. There are no data sets

available for study of pre-history. We do have

the historic account of scripture. But it's been

rejected, and classified as 'non-scientific'.


http://en.wikipedia.org/wiki/Most_recent_common_ancestor


In scientific and engineering work, models are

made to represent the data that has been

collected over a period of time. Models are used

all the time, and they work really well within

the limits of their application. Most of the

time, models are used for a very specific

application. One reason for that is because a

model tends to get more complicated as it

attempts to include wider realms of data, and

more complex systems.


Here's an example of everyday modeling:

http://en.wikipedia.org/wiki/Data_modeling

Whenever a computer programmer begins to build a

database, they have to look at the kind of data

that needs to go into a database and figure out a

way to sort and store that data, so that it can

be retrieved. Some of the time, it's a really

easy project. But if you ever have a chance to

talk to an old-timer, who was around in the first

years of data processing, they will tell you that

it was a very bumpy road, before all of the rules

of thumb were worked out.


Eventually, a model was developed in order to

standardize and simplify the work of database

design and administration. Until recently, the

most common model is the relational database

model. That's what you will find in the most

commonly used databases of today. It's just a

set of rules that were developed over time in

order to make the job of designing and using

databases consistent no matter what data was

being stored. For most data, a RDB ( relational

database ) works out OK.


There is nothing that says a database has to be a

RDB. You can develop your own database system,

using any rules and techniques that you wish.

But in most cases, people will just buy an RDB

system, like Oracle, because it works well enough

to do the job for their application. It's too

much work to build a system from scratch, and the

RDB has become a de facto industry standard.

Over time, what happens is that people begin to

associate 'database' with 'RDB', and think that

it includes the entire universe of databases.

Oracle would like you to believe that.

Commercial enterprises like a captive audience.

And in the world of engineering, just like the

world of science, the captive audience principle

works just as well as it does on Madison Avenue.


There are models that are 'conventional', meaning

that they are the most widely used models, like a

RDB, for instance, and then there is everything

else -- all the other models -- that border on

heresy. So, unless you like to wear the

'heretic' label, you follow the convention. All

the same, conventional practice doesn't mean that

there isn't another way to do things, and in some

cases, it may be the least effective way to do

things. But try and tell that to your

pointy-haired boss, you infidel.


So in the 'science' of origins, we are stuck with

the evolution model, just like the database world

is stuck with the RDB model. It is the de facto

standard, and to suggest another model borders on

heresy. It won't further a scientific or

engineering career to be labeled a heretic.


There is a huge difference between database

models and origins models. The former attempts

to accomodate a fairly small set of data, and

still becomes overly complex. The latter is

overwhelmed with data and complexity. It makes

no difference. Don't question dogma.


As I see it, the overall complexity of the

evolution model is really its greatest advantage,

because it makes it impervious to the average

Joe. It's too easy to get lost in all the

details, and completely overlook the simple and

what should be obvious flaws. De facto standards

tend to die a slow and painful death. Especially

if they are the basis of a profitable business.


One of the more obvious assumptions of the

evolution model, and the assumption that stands

clearly in contradiction to the scriptural model

of origins, is the time scale. It's been

repeated so often, in every imaginable context,

that scientists 'know' the age of the universe,

that people can't seem to recognize it for the

'de facto' standard that it is. To question it

is heretical within the ranks of popular science.

But how is it modeled? It's a question that

occupies the astrophysicist. And it's a world

that's beyond the reach of almost everyone. How

to model time in the universe. Is it billions of

years, and does it even matter? An

astrophysicist probably recognizes that there is

a high degree of speculation involved when trying

to model time scales across the universe. But to

admit to it, would be to admit a 'scientific'

heresy. Besides, for the average Joe, the math

is just too daunting. Even in my best days, I

could barely grasp the shape of things on the

cosmological scale.


But the math of a simple growth model is not too

tough. It's a simple model to begin with, and

provides a starting point. It's not too hard to

narrow it down to the pre-history and history of

man. Just begin with where we are today and work

back in time.


http://en.wikipedia.org/wiki/Exponential_growth

http://en.wikipedia.org/wiki/Fibinocci


These links provide the simple math of

exponential growth. That's the rate of growth of

human population. In general terms, it means

that human population doubles every 35 years.

Just take the population of the world as it is

today, and cut it in half every thirty five

years, until you get to a small number, and you

arrive at an estimate of a timeframe of the

origin of man. It comes as no surprise that the

MRCA genetic work shows that common ancestry only

goes back a few thousand years. That's the

scriptural model. Forget about the millions of

years. ( See the MRCA link above )


Here's a simple table to look at:

DATE POPULATION

-1- 2
-35- 4
-70- 8
...

-350- 2,048
-700- 2,097,152
-1050- 2,147,483,648

This table begins with time at year number 1, and

then increases exponentially through the first

1050 years. You can see that after only 1050

years, the population reaches in excess of two

billion people. That's roughly the population of

China. So, even if you factor in a huge amount

of climatic, social and health related

catastrophe, empirical population growth rate is

so high that there just isn't time enough for a

pre-historic presence of man that goes back even

a hundred thousand years.


Remember, this is not a 'de facto' standard

model, this is the model of growth that we see

happening right here, right now. It's an

empirical (scientific) model. A geometric growth

rate model represents the *facts* that we

observe. It's the simplicity of the model that

leads to the conclusion that the pre-history of

man is no where near as long as the evolutionista

would have us believe. And IF the evolution

model were correct, then we should have ample

evidence in the form of the remains of large

populations of pre-historic man scattered across

the face of the earth. It's not there. The

empirical evidence suggests that man has not been

present for more than a few thousand years.


When we look at a simple, easy to understand

empirical (scientific) model, it begs the

question about the inordinate *speculation* that

the evolutionary time scale model demands. It

demands no growth for hundreds of thousands of

years. It just doesn't add up, based on

empirical evidence. An exponential growth rate

proves otherwise and the lack of evidence of

prior civilizations proves otherwise.

Speculation -- saying it isn't so -- is not

scientific.


If you can imagine this concept and then apply it

to the entire world of biological life, then you

begin to get a hint about the vast amount and

complexity of the data that are being thrown at

the evolution model. It's a model that attempts

to explain every bit of data with the assumption

( non-scientific speculation ) that it all

happened by chance. Tell any database admin that

if they just stand aside and let a computer run a

while that the data will sort itself out. They

won't buy it. But that's what the evolution

model implies. And increasing the time frame

only makes matters worse, ignoring the empirical

data and the high level of complexity.


Oh well. I guess that the best they can do is to

burn heretics at the stake. But the Apostle's

warning from 2000 years ago still stands.

'...avoiding ... oppositions of science falsely

so called...' applies as well today as it did in

his day.