One question I meet again and again if I try to make meaningful agent-based simulations is: - How do we simulate the core of a problem without merely constructing an illustration of our own beliefs and assumptions ? In other words: How detailed should an agent-based simulation be ? If the goal is "to capture the principal laws behind the exciting variety of new phenomena that become apparent when the many units of a complex system interact", as Tamas Vicsek says in http://angel.elte.hu/~vicsek/images/complex.pdf then how do we design models that are complex enough but not too complex ?
-If the simulation is too simple and matches your own theoretical ideas, then no matter how good these ideas are it is always easy to criticize that the simulation is either not realistic enough or only constructed to illustrate your own ideas and assumptions. -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 The "art of agent-based modeling" looks really like an art to me, something only mastered by a few scientists (for instance Axelrod). Grimm et al. propose 'pattern-oriented modeling', Macy and Willer say "Keep it simple" and "Test validity". What do you think is the best solution for this problem ? Macy and Willer "From Factors to Actors: Computational Sociology and Agent-Based Modeling" http://www.casos.cs.cmu.edu/education/phd/classpapers/Macy_Factors_2001.pdf Grimm et al. "Pattern-oriented modeling of agent-based complex systems" Science Vol. 310. no. 5750 (2005) 987-991 http://www.ufz.de/index.php?de=4976 -J. -----Original Message----- From: Michael Agar Sent: Saturday, August 12, 2006 5:05 PM To: The Friday Morning Applied Complexity Coffee Group Subject: [FRIAM] complexity and society [...] If you are considering a model, I like Axelrod's way of thinking about them. He sees them as "thought experiment labs" for a conclusion based on social research. So first of all the social research has to be solid to really do it properly. More often than not it isn't. The lab let's you test arguments of the form, if people do things in particular ways properties will emerge at the level of society. By "test" I mean it lets you see if the conclusion can be "generated," to use Epstein and Axtell's concept, in just the way your social research suggests that it can. It's a way of making the argument that underlies the conclusion explicit so it can be better evaluated, and it allows for exploration of the space of results that the same argument produces and alternative spaces given control parameter changes. It's a test of plausibility and an exercise in clarity, nothing more, nothing less. [...] ============================================================ 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
