Good methodological questions, except I think it's a mistake, in this science, to exclude all experiments that don't have a falsifiable premise. That's a great methodological 'ratchet' for progress when it's definable, but a lot of what we're studying with complexity is a theoretical mess. I'd even sometimes drop the idea that your research should even be accumulative, if you're playing with something that fascinates you. Mainly though, very good science is done with simple documentation of observations when people don't quite know what theory to propose, whether documenting the behavior of artificial or natural complexity.
Phil Henshaw ¸¸¸¸.·´ ¯ `·.¸¸¸¸ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 680 Ft. Washington Ave NY NY 10040 tel: 212-795-4844 e-mail: [EMAIL PROTECTED] explorations: www.synapse9.com > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of Marcus G. Daniels > Sent: Monday, August 14, 2006 10:07 AM > To: The Friday Morning Applied Complexity Coffee Group > Subject: Re: [FRIAM] The art of agent-based modeling > > > Jochen, > > -If the simulation is too complex and matches > > official experimental data, everything takes a > > lot amount of time (creation, setup and execution of > > the experiment and finally the cumbersome analysis > > of the complex outcomes), and it becomes increasingly > > difficult to identify the principal laws, because it is > > easy to get lost in the data or bogged down in details > > > This may be a false choice. In the case of having some data of > moderate resolution, there's no point in making a hugely > elaborate model > and simulation, because you'll never be able to validate beyond your > data anyway. And if you don't validate, although the modeling still > may be useful as an thought experiment, it isn't science. > You have to > be able to say something that can be shown to be wrong. If > you do aim > to learn things about the world and then predict them it's > not desirable > to have giant black box with lots of moving parts. It's > better, if at > all possible, to have a simple story and make the simulation nothing > more than apparatus to help extend the data so that the > dynamics can be > studied by theoreticians. > > Another mode of use for ABMs is to lower expectations of theoretical > traction and opportunistically look for ways a model makes useful > predictions and then modify the model in that direction over time. > This is a risky and expensive craft, but one that might have > high enough > payoffs to consider (e.g. national security). > > It depends on the data and what is of interest. If the data > tells you > about a number of rare events, and it is these events is what > you really > care about, then it may make sense to loosely model everyday > behaviors > and focus on model microstructure that can create the rare events you > care about. > > Finally, sometimes microstructure is known with clearly > defined degrees > of freedom, and the dynamics are of interest. Consider modeling a > factory where different assembly regimes are to be > evaluated.. There's > no need to validate here because the whole exercise is to answer > what-ifs about realizable specific systems. > > Marcus > > > > ============================================================ > 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 > > ============================================================ 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
