On 06/12/2008, at 6:18 PM, A. Wolf wrote:
>> I guess what I am on about is a bit closer to the 80s idea of "chaos"
>> - something that is inherently unpredictable; at least if you adopt
>> the stance of always launching your prediction from a single
>> present -
>> the one you happen to find yourself in.
> I think you mean randomness, not chaos. Chaos theory deals with
> deterministic systems that vary widely in result based on small
> changes in
> initial starting conditions; these systems are 100% predictable.
Don't believe in randomness. "Random" means we don't understand what
determines it. Like "Junk DNA"; it's only junk up until we work out
what it's really for.
You can predict with 100% accuracy that systems with varying initial
conditions will bifurcate and become chaotic once driven beyond a
certain point. What you cannot say is what is determining the order in
the chaos once it arrives. That's closer to what I mean.
2 men start to dig a hole. They are instructed to make it reach a
depth of 5 feet. One of them murders the other with his shovel. Nobody
predicted that would happen. We can 'determine' the reasons for this
event only AFTER it happens, even though it was determined by
something that we might have noticed prior to the event if only we had
been able to. All action can be seen as logically determined in
hindsight. Before something happens is where we would like to be more
on top of things. Intelligence exists on terrorism but usually this
usually fails to determine our actions to prevent terrorist acts,
interestingly enough. If you look closely at what I am saying, it is
the rather messy human consciousness part of the equation that SPOILS
the mathematical modelling in most cases. I am saying that a full
perceptual scan of the situation often takes us way beyond what the
data suggests. Just like the Mumbai massacre - intelligence WAS
available that suggested it could happen. Yet the massacre was
"allowed" to happen, because nobody could see the looming pattern in
the data until after it happened by which time it was bleeding obvious
to one and all. It seems that in many situations the sheer volume of
information available is as much a part of the problem as the decision-
making process. How do we decide? Data alone are incapable of making
decisions. You still need a wet, messy human brain with a perceptually
skilled mind to do that
>> Isn't this kind of like an act of faith?
> No. Faith isn't based on evidence. When we use math to model
> things in
> reality, we do so empirically. If a distribution doesn't fit after
> it, we don't use it to model that set of data, for example. How we
> use math
> functionally is different from math itself.
Sure, but correlations occur between sets of data and there is a
tendency for them to look like causations. Most scientists and data
collectors are trained to say "correlations do not necessarily imply
causations" which is great advice up until the correlation turns out
to BE a causation
>> If we could perfectly model where things are heading then
>> please tell me why all the BTSOAPs of the dismal science of the
>> economics world could not arrange a more stable financial future for
>> us than the one we are currently moving into?
> The problem with (for example) economic forecasts is not that the
> mathematics is flawed; the math is fine. It's the data collection
I would say that it is the premises - the starting assumptions that
are probably flawed, not the collection method, unless by collection
method we include the starting assumptions which are unavoidably
guiding the triage of the data. The biggest flawed assumption of all
is the belief that data collection of itself will give rise to ideas
and concepts. Data do not do that. We choose our starting points and
assumptions in all cases. Often we aren't even aware of these because
- well, they are assumptions and nobody really questions assumptions
much. Our way of looking at the data is not itself present in any way
in the data. I'm saying that if reality conforms to our model of it,
then the danger is we are looking at only a part of the situation. The
issue in question is the WHOLE situation, not just that part we CHOOSE
to look at. How can we ever be ccertain that we are in fact looking at
the whole situation. We only see what our assumptions and premises
allow us to see - we are blind to many things up until we get
clobbered by them
> We have access to incomplete and imperfect information with which
> to make predictions, so we're occasionally wrong.
Hence my whole argument. I would say you are being somewhat kind!!
> That's exactly what
> happened with the banking system in the US, actually...they tried to
> mathematical models developed on responsible lendees to apply to other
> lendees who were much less likely to pay back the bank, and after a
> the amount of fail exceeded the ability of banks to handle it.
So why wasn't the "people situation" factored into the modelling? How
can we say we have a reputable model to go by when it explicitly does
not include the human problem? Answer: people only looked at the money
trail. The human foibles were not investigated until they suddenly
revealed themselves not as correlations but as causations. Even the
High Priest of Economics, the great Alan Greenspan had to admit
(rather humiliatingly) that he had been "too trusting of human nature"
No model of a situation is ever going to be complete if it doesn't
include the OBSERVER which is why I think my making an issue of this
is relevant to this list.
> I think you're forming a straw man of mathematics, because I don't
> that math does all the things you're suggesting.
Exactly, but people like to think it does. Why we need "Lateral
Thinking" or "Possibility Thinking" or "Creative Thinking" to project
alternative futures and steer the present into the future by DESIGNING
ways around problems.
Mostly people just stand back and say "Well - if only that pattern had
been present in the data we would have seen it and would have been in
a position to act". That's nonsense. Usually we are completely BLIND
to the gravitating patterns in the data because our starting
assumptions don't allow that kind of clarity of sight
> Mathematics is not the
> science of fitting math to the natural world, and flaws in the
> latter don't
> suggest a fundamental incompleteness (Godel aside) to the former.
No - agreed. I am not "anti-mathematical" by any means. I merely
suggest that there are limits - very strong limits - to what we can
expect mathematical reasoning to achieve in the light of the chaotic
nature of human decision-making in a rapidly evolving (and
accelerating) universe. We may be the dream of numbers, but we cannot
use the numbers to predict how we ourselves might respond in a given
situation. You call that random - well, I am saying it is determined
by things we can only be conscious of AFTER events have transpired.
No data set can ever be the optimum model of a given reality - I don't
care what we are modelling.
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