Ann, Are you looking for information on desktop (generally, serial processing or dual multi-processing, threaded) applications - such as NetLogo or StarLogo? Or are you looking for information on ABM using MPP (massively parallel processing) using multiple GPU (graphics cards) for the mathematical vector and matrix simulations of "intelligent" agents (natural, human or weak artificial)? The sigevolution.org site shows how evolutionary genetic algorithms evolve into patterns (such as swarm behavior, or entangled behavior as ref: in the Ropella e-mail). Some of this work is from E-Plex and their research into Complex Pattern Producing Networks (CPPN's) http://eplex.cs.ucf.edu/. Do not be mislead by the triviality of their examples - this can be powerful stuff. Another place to start is the Swarm wiki http://www.swarm.org at the University of Michigan. The term Agent Based Modeling (ABM) seems to cast a wide net - from "simple" cellular automata to phenotypical behavior of genotypically evolved and generated, quasi-intelligent artificial organisms (often referred to as complex adaptive systems). I guess I would need to know at what level you wish to understand agent based modeling of complex systems in order to recommend an executive summary (which may be an oxymoron). By this I mean, Stephen Wolfram's A New Kind of Science may be considered by some to be an executive summary on ABM. Specifically see page 991, Implications for Everyday Systems (Notes for Chapter 8) - Issues of Modeling - which ties up my recommendations with a pretty bow. This is as close as I could come to an executive summary. Referring to your original e-mail, IMO, the Bayesian approach is meaningless without the application of the Inverse Theory to refine it. I recommend Scales (see original post), because it is simple, clear, and reachable. Having said this, I'm afraid I haven't been very helpful. ============================= Kenneth A. Lloyd CEO and Director of Systems Science Watt Systems Technologies Inc. Albuquerque, NM USA [EMAIL PROTECTED] [EMAIL PROTECTED] - MBSE Complex, Adaptive & Stochastic Systems [EMAIL PROTECTED] - Director of Education www.wattsys.com <http://www.wattsys.com/> <http://www.linkedin.com/pub/7/9a/824> http://www.linkedin.com/pub/7/9a/824
This e-mail is intended only for the addressee named above. It may contain privileged or confidential information. If you are not the addressee you must not copy, distribute, disclose or use any of the information in it. If you have received it in error please delete it and immediately notify the sender. _____ From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of [EMAIL PROTECTED] Sent: Tuesday, April 08, 2008 12:17 PM To: 'The Friday Morning Applied Complexity Coffee Group' Subject: Re: [FRIAM] ABM,Baysian and Monte Carlo Method's Role in Understanding Complexity Thank you Ken, I am wondering though if someone has an executive summary? Were any of your citations specific agent based models? _____ From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Ken Lloyd Sent: Tuesday, April 08, 2008 10:51 AM To: 'The Friday Morning Applied Complexity Coffee Group' Subject: Re: [FRIAM] ABM,Baysian and Monte Carlo Method's Role in Understanding Complexity Ann, Get you pencil and paper ready ... http://www.sigevolution.org/issues/pdf/SIGEVOlution200702.pdf Christopher Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995 Klaus Mosegaard - Monte Carlo Analysis of Geophysical Inverse Problems, http://wwwrses.anu.edu.au/~malcolm/papers/pdf/SamMos02.pdf Klaus Mosegaard, 1998: Resolution Analysis of General Inverse Problems through Inverse Monte Carlo Sampling: Inverse Problems 14, pp. 405-426. James Scales, M. Smith, and S. Treitel, Introductory Geophysical Inverse Theory, Samizdat Press, Golden, CO USA, 2001, <http://acoustics.mines.edu/jscales/gp605/snapshot.pdf> http://acoustics.mines.edu/jscales/gp605/snapshot.pdf See the many books and papers by Mosegaard and Tarantola The network aspects are generally covered in Newman, Barabasi and Watts, The Structure and Dynamics of Networks, Princeton Series on Complexity Ken ============================= Kenneth A. Lloyd CEO and Director of Systems Science Watt Systems Technologies Inc. Albuquerque, NM USA [EMAIL PROTECTED] [EMAIL PROTECTED] - MBSE Complex, Adaptive & Stochastic Systems [EMAIL PROTECTED] - Director of Education www.wattsys.com <http://www.wattsys.com/> <http://www.linkedin.com/pub/7/9a/824> http://www.linkedin.com/pub/7/9a/824 This e-mail is intended only for the addressee named above. It may contain privileged or confidential information. If you are not the addressee you must not copy, distribute, disclose or use any of the information in it. If you have received it in error please delete it and immediately notify the sender. _____ From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of [EMAIL PROTECTED] Sent: Tuesday, April 08, 2008 10:16 AM To: 'The Friday Morning Applied Complexity Coffee Group' Subject: Re: [FRIAM] ABM,Baysian and Monte Carlo Method's Role in Understanding Complexity Thanks to all who responded. "After all, it's the _braided_ or woven nature of causal networks (in contrast to causal _chains_) that gave rise to ABM to begin with." glen e. p. ropella Could you or someone recommend a good ABM (as in the above quotation) that I might study? I thought glen's description of bayesian was very clear. Could glen or someone else give a similarly clear and intuitive description of Bayesian Monte Carlo or Markov Chain Monte Carlo method? Ann Racuya-Robbins World Knowledge BankR A Virtual Democratic Country www.wkbank.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
============================================================ 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
