Thank you, GŁnther. Consider joining ESSA ;-)
Even better, put this on your calendar:
2nd World Congress on Social Simulation
George Mason University
Bring your friends too and meet Team MASON
Professor of Computational Social Sciences
Director, Center for Social Complexity
Krasnow Institute for Advanced Study, George Mason University
Research-1 Bldg MS 6B2, 4400 University Drive, Fairfax, VA 22030 U.S.A.
tel (703) 993-1402, fax (703) 993-1399, [log in to unmask]
Center & Grad Program http://socialcomplexity.gmu.edu
MASON Project http://cs.gmu.edu/~eclab/projects/mason/
on 9/28/06 6:20 AM, GŁnther Greindl at [log in to unmask] wrote:
> Hello Sean, Tony, Claudio, Mike,
> thank you all for your answers, community = top notch :-))
> * Claudio wrote:
>> you should consider MASON if you need:
>> -replicability guarantee
>> -computational and visualization separation for
>> -lots of runs (>103) without viz every run ;-)
>> -checkpointing for stats analysis
>> -evolutionary computational methods, as common for assessing learning.
> That is indeed exactly what I need. Especially visualization is very
> important, as I want to experiment with a few different approaches here.
>> We will make sure to send you the revised
>> paper as we develop it.
> Please do, I would be very glad, I've already printed the paper and am
> currently reading it :-)
>> Both Epstein and Axtell
>> (now at our Center, no longer Brookings) provided input.
> Wow. Cool! :-) I thoroughly enjoyed their Sugarscape book and it was
> a source of great inspiration.
> * Tony wrote
>> I'm just about to release my source code (refreshing it for MASON 11
>> and doing general cleanup/commenting) and you'll be welcome to look at
>> or use it. I implemented 70-75% of the rules and outcomes in Growing
>> Artificial Societies.
> I'm looking forward to your code.
>> Any kind of complex cognitive architecture will fly against the
>> philosophy of Sugarscape, but is interesting that the agents have no
>> overt memory to use in reasoning/(ir)rational decision making.
> That is the big question - of course, the traditional CAS approach is
> to keep the individual components simple. But in the social simulation
> community voices have been raised (and I tend to concur - with reservations
> ;-) -
> that to make sensible descriptions of human societies one cannot completely
> neglect the cognitive aspect.
> I think one should iterete into more complex agents slowly, and see what
> Generative science in the best sense ;-)
>> As agents/models become more complex they will
>> become more difficult to understand and in turn establishing causality
>> between code and simulation outcomes. Given the very little
>> replication that has occurred in this field, in general, there are many
>> open questions.
> That is quite true. I am just starting out with my Ph.D on this topic
> (cultural diffusion modeling), and I'm not yet sure where it will take me -
> more into the philosophy of science section, as in "how applicable are the
> results", "is generative science real science" etc; or into the design of
> cognitive agents useful for simulation; or a return to the simple agent and
> adjusting the interplay of agents. All exciting questions :-)
> One more thing: what I certainly intend to do is distribute the simulation
> in a second step (long term goal); if I use cognitive agents, there will be no
> other way to do simulation sensibly - does MASON lend itself easily to this?
> I know that you can add distributed support to Repast Models quite easily.
> Kind Regards and thank you for your responses,
>> Alles gut
> Danke :-)