Re: Intelligent ways to search parameter space?

[Rick Riolo]

Well, i don't know about "intelligent searches",
but there have been works on "adaptive searches",
e.g.,

Exploratory Modeling.  Search Through Spaces of Computational Experiments.
  Bankes, Steve.
  Proc. Third. Conf on Evolutionary Programming, pp353-360. 1994.

Active Nonlinear Tests (ANTs) of Complex Simulation Models.
  Miller, John.
  Sante Fe Institute Working Paper 96-03-011.

Of course your question is also related to
"sensitivity analysis", and most texts on
simulation discuss "classical" methods for that.

More generally, it seems to me your question
is asking "Is there a theory of complex systems"
and I'd have to say "No."  There are some rough rules of thumb
(for instance see axelrod and cohen's recent book
Harnessing Complexity), but at this point the rules
are generally at the level of

  "If you turn the mutation rate too low you might get..."
  "If you turn the mutation rate too high you might get..."

There are similar, sometimes more specific rules for
particular models (eg kaufman's nk models), but
since there isn't really a good taxonomy of complex systems,
based on some key features/dimensions/mechanisms of the systems,
i don't think we can talk very effectively about the
relationship between "general qualities of complex systems"
and expected behaviors given certain specific parameters
in a specific model.

(but maybe i'm too pessimistic on this dark, very cold
and snowy morning in michigan!)

- r


Rick Riolo                           address@hidden
Center for Study of Complex Systems (CSCS)
4477 Randall Lab
University of Michigan         Ann Arbor MI 48109-1120
Phone: 734 763 3323                  Fax: 734 763 9267
http://www.pscs.umich.edu/PEOPLE/rlr-home.html

On Thu, 7 Dec 2000, Ken Gosier wrote:

> Date: Thu, 07 Dec 2000 07:52:29 -0500
> From: Ken Gosier <address@hidden>
> Reply-To: address@hidden
> To: address@hidden
> Subject: Intelligent ways to search parameter space?
>
> So, I've been working a while now on making an extension to the ASM
> model for the stock market. I've extended it to many assets, and 2 types
> of agents.
>
> I've got it up and running, which is very cool, and I have the
> LispArchiver hooked in, so it's nice and easy to make different runs for
> different parameters. I don't yet have a batch mode, but I can figure
> that out as well.
>
> At this point my main task is to start searching parameter space, to
> find interesting behavior. But: there are ~50 parameters to this model,
> so exhaustively searching through parameter space in a brute force way
> is not feasible. Granted, there are only about 5-10 params that I
> initially care about messing around with, but still it's a daunting
> task.
>
> My question is: are there reference materials out there, which provide
> some guide to intelligently searching parameter space? I was wondering
> if there's been any work saying something like: These are general
> qualities of different types of complex systems. To produce these
> qualitatively different types of behavior, then you should follow this
> strategy in setting up the model and/or parameters.
>
> I've already managed to change the behavior of the model from completely
> unstable, to mostly stable, just by using financial knowledge to set the
> parameters realistically.
>
> Apologies if this question is overly vague. I know there's lots of good
> materials out there. (I know the Michigan Complex Systems page has a
> good readings page about complex systems.) But, if someone knows some
> materials in this specific area, it would be quite helpful. Many
> thanks--
>
> Ken Gosier
> address@hidden
> address@hidden
>
>
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