Phil, > 1) for change situations referred to as, [ before | gap?| > after ] Causal mechanisms that take time can generally be > found in the 'gap' but predictive models are often missing > because they can jump over gaps.
I'm wondering where there may be some shared vocabulary between complexity-speak and your notion of growth curves. Is it possible that everything you mean by growth curves is captured by curves of phase-transitions? When you mention "gap" above, might that be the critical point in a phase transition where a system breaks symmetry and chooses between different basins of attraction. The occurence of the transition is predictable but which attractor will emerge, is not. Most models in complexity are concerned with such phenomena. Take an ising model as a classic example <http://www.ibiblio.org/e-notes/Perc/ising.htm>. As a regular array of mutually influencing agents, each decides to spin up or down depending on the states of its neighbors. Magnetization in this model is measured as the proportion of spin up or spin down in the populuation and is called an "order parameter" which is a low-dimensional variable that can describe the collective state of the system. Experimenters tend to not have direct control over order parameters. The parameters they can manipulate are called "control" parameters and in the ising model example, are things like initial configuration and temperature (degree that a spin ignores its neighbors influence and just randomly flips). Does this vocabulary map to yours? If you observe the graph of the magnetization order parameter in the ising model is it a "growth curve"? -Steve > -----Original Message----- > From: Phil Henshaw [mailto:[EMAIL PROTECTED] > Sent: Sunday, April 08, 2007 5:18 AM > To: 'The Friday Morning Applied Complexity Coffee Group' > Subject: Re: [FRIAM] predictive models v. causal mechanisms > > well, so can anyone add to the list of things that make them > different? > > 1) for change situations referred to as, [ before | gap?| > after ] Causal mechanisms that take time can generally be > found in the 'gap' but predictive models are often missing > because they can jump over gaps. > > > > Phil > > Re: > Sent: Friday, April 06, 2007 7:35 AM > To: 'FRIAM' > Subject: [FRIAM] predictive models v. causal mechanisms > > > > Big subject, but, first, are there useful ways tell the > difference? I think the main difference is between images > and things, a big clear difference, and very useful to be > able to distinguish. > > > Phil Henshaw ¸¸¸¸.·´ ¯ `·.¸¸¸¸ > ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ > 680 Ft. Washington Ave > NY NY 10040 > tel: 212-795-4844 > e-mail: [EMAIL PROTECTED] > explorations: www.synapse9.com <http://www.synapse9.com/> > > > ============================================================ FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College lectures, archives, unsubscribe, maps at http://www.friam.org
