Of course it is the essence of science to verify hypotheses by experiments. Yet sometimes we have neither suitable experimental data nor a solid theory, for example in the case of very large agent-based systems (for instance for the self-organization and self-management of large internet applications on planetary scale, or the modeling of historical processes with millions of actors). It is hardly possible to examine these systems without simplified models, and in this case the questions I mentioned seem to be justified.
In traditional "factor-based" or "equation-based modeling" we use differential equations and everything is based on a soild theory: mathematics. This traditional modeling has a century-long history and we know the suitable parameters, equations and models. Agent-based modeling has a short history, we don't know exactly the suitable parameters, agents and models, and worst of all it is not based on a solid theoretical theorem-lemma-proof science or calculus like mathematics. What is missing is a solid science of ABM or a new science of complexity - something in the direction of Wolfram's NKS idea (exploring computational universes in a systematic way). Just as formal, symmetrical and regular systems can be described by mathematics and 'equation-based modeling', complex systems can in principle be described by a 'NKS' and agent-based modeling - which seems to be more an art than a science. -J. ============================================================ 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
