Can system dynamics models "learn"
Posted: Fri Apr 25, 2003 2:19 pm
Marsha,
Thank you for the energy and thoughtfulness you put into the message. It
has given me a lot to consider and some to comment on.
First I would like to ask you not to be too discourage about your finding of
the ridged view “we do x when y occurs”. I have run into this for decades.
It is not your fault. Stake holders in the current system keep their names
sake - stake holders - they have a stake in the system. It may be self
esteem as they were an originator or a subsequent supporter, or their
livelihood my depend upon the current system. If you present opportunities
that imply a change, the change may mean they loose their income. It is
very difficult to have a brainstorming session with subject matter experts
when they hold a stake in the game - they may not be really looking for
brainstorming as much as justifying their existence. A key working with
this is to understand it going in. Their frustration may be a knee-jerk
response that only needs some smoothing over or it may be the underlying
ploy to maintain status quo. I like to go into a situation and point out
these types of pit falls and ask the participants to suggest how to assure
full, good faith participation. Not that I expect an answer, I just want
everyone aware of the situation so they may be self policing. The worst
case position is where a client asks an service provider to brainstorm with
you but the service provider has a vested interest in the status quo. You
need to assure that the client brings additional representatives in to
equalize or level the playing field.
Second is the genetic algorithm (GA) approach. Please let me digress and
inform you of my prejudices with GA. I have seen GA mostly applied
inappropriately due to a lack of knowledge of the underlying mathematical
structure of the system and of optimization process in general. Different
types of systems can be addressed most appropriately by different types of
optimization methods, but rather than researching the correct combination,
GA is often pulled in as it is popular and stable if not robust.
But there is no magic to GA. One must still either understand the possible
node/link structures or resign to a fixed structure to optimize against.
Both classical and GA methods can address either. As the dimensions of the
problem grow, random perturbations against multiple combinations of
dimensions makes many problems unsolvable in the needed time frame. Where
one can more directly finesse an approach with classical techniques.
And lastly(?)! Running multiple experiments in “Hill-climbing” example
should not be considered reinforcement. In a problem with one independent
variable and one dependant (two dimensions), where the dependant has a
parabolic relationship with the independent, two measurement points are
needed to identify the optimum point. This is just the nice part about
math, one only needs two points to define a parabola. Reinforcement helps
us, easily distracted individuals with remembering a relationship. By
definition, an algorithm doesnt need such help. Now if the measurements of
the points are not accurate (noisy measurements), we are addressing a
stochastic system and may need multiple readings to assure some degree of
confidence for the parameters of the parabola and thus the optimum.
As for nimble learning, I wish to consider more - I am still wrestling with
the ghost in the machine. I really liked your example of being confronted
with thirst and a faucet but only knowing how to open a bottle.
Raymond T. Joseph, PE
281 343-1607
RTJoseph@ev1.net
Thank you for the energy and thoughtfulness you put into the message. It
has given me a lot to consider and some to comment on.
First I would like to ask you not to be too discourage about your finding of
the ridged view “we do x when y occurs”. I have run into this for decades.
It is not your fault. Stake holders in the current system keep their names
sake - stake holders - they have a stake in the system. It may be self
esteem as they were an originator or a subsequent supporter, or their
livelihood my depend upon the current system. If you present opportunities
that imply a change, the change may mean they loose their income. It is
very difficult to have a brainstorming session with subject matter experts
when they hold a stake in the game - they may not be really looking for
brainstorming as much as justifying their existence. A key working with
this is to understand it going in. Their frustration may be a knee-jerk
response that only needs some smoothing over or it may be the underlying
ploy to maintain status quo. I like to go into a situation and point out
these types of pit falls and ask the participants to suggest how to assure
full, good faith participation. Not that I expect an answer, I just want
everyone aware of the situation so they may be self policing. The worst
case position is where a client asks an service provider to brainstorm with
you but the service provider has a vested interest in the status quo. You
need to assure that the client brings additional representatives in to
equalize or level the playing field.
Second is the genetic algorithm (GA) approach. Please let me digress and
inform you of my prejudices with GA. I have seen GA mostly applied
inappropriately due to a lack of knowledge of the underlying mathematical
structure of the system and of optimization process in general. Different
types of systems can be addressed most appropriately by different types of
optimization methods, but rather than researching the correct combination,
GA is often pulled in as it is popular and stable if not robust.
But there is no magic to GA. One must still either understand the possible
node/link structures or resign to a fixed structure to optimize against.
Both classical and GA methods can address either. As the dimensions of the
problem grow, random perturbations against multiple combinations of
dimensions makes many problems unsolvable in the needed time frame. Where
one can more directly finesse an approach with classical techniques.
And lastly(?)! Running multiple experiments in “Hill-climbing” example
should not be considered reinforcement. In a problem with one independent
variable and one dependant (two dimensions), where the dependant has a
parabolic relationship with the independent, two measurement points are
needed to identify the optimum point. This is just the nice part about
math, one only needs two points to define a parabola. Reinforcement helps
us, easily distracted individuals with remembering a relationship. By
definition, an algorithm doesnt need such help. Now if the measurements of
the points are not accurate (noisy measurements), we are addressing a
stochastic system and may need multiple readings to assure some degree of
confidence for the parameters of the parabola and thus the optimum.
As for nimble learning, I wish to consider more - I am still wrestling with
the ghost in the machine. I really liked your example of being confronted
with thirst and a faucet but only knowing how to open a bottle.
Raymond T. Joseph, PE
281 343-1607
RTJoseph@ev1.net