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Dear List, Should a computational model's scale measured in number of agents indeed lead to a qualitative change of the model's outcome, a number of results elaborated by means of social simulation would have to be reconsidered. This would particularly apply to models where insight into real world social systems is inferred from models consisting of only a small agent population. However, the results obtained from my models were produced by a relatively smrall agent population and could nevertheless be cross-validated. So far I am not concerned. But I do think that the inherently empirical question "Does size matter?" cannot be solved without experiments and some of the contributors to this discussion have already made some relevant statements. But besides an intra-model approach to this issue a problem-oriented or, if you wish, evidence-driven approach could help to find some answers. The question "Does size matter?" should be therefore not only directed towards the computational application, but also to the social phenomenon that is being scrutinised. If there is evidence in a real social phenomenon that size, measured in number of actors as well as their inter-relations and -actions, is a pivotal feature of the system, then a construct valid model should represent this characteristic. Of course the same applies to real world cases where scalability plays no or no significant rôle. Armando Scott Moss wrote: > Though I have certainly not simulated on the scale of millions of > agents, I have found that beyond some fairly low scale it is not the > number of agents but the population density (in the sense of how many > other agents any one agent could in principle observe of communicate > with) that matters. For example, in the markets model described in > the Moss-Edmonds paper, I specified the radius within which agents > could observe and communicate with one another and the agents were > allocated randomly over a grid. By reducing the size of the grid but > keeping everything else the same, the sort of turbulence described by > Norman Johnson emerged. > > I conclude from these results that the importance of scale is not > independent of other factors such as (but probably not exhaustively) > the intensity of social interaction within the population of agents. > > > On 08/11/2008 10:07 AM, Peer-Olaf Siebers wrote: >> Here are my random thoughts... >> >> In order to study the real effects of policy changes one needs to get >> the >> response variability of the model system to the same level than the real >> system. Therefore, you need the right level of heterogeneity of the >> agents >> but also a scale model would help to account for unforeseen effects >> that you >> do not model explicitly, at least that is my experience. Often the >> size of >> the modelled population regulates the effects of policy changes. It >> is also >> important to remember that a group is not just a collection of >> individuals >> but develops its own dynamics. These might be different for different >> group >> sizes. Bigger groups might create additional dynamics and >> consequently the >> levels of variability and stochasticity within the simulation model will >> change even if the proportions of stereotypes within the groups are kept >> constant (is that what people often call emergence?). Therefore, I >> think, >> size matters! >> >> Another aspect to consider is validation. Previously I initiated a >> SIMSOC >> discussion about validation of ABMS models. From there I learned that >> AMBS >> models are often validated quantitatively on a micro level and >> qualitatively >> on a macro level (see for example Moss and Edmonds, 2005). I guess that >> having a scale model of the real system definitely supports the >> qualitative >> validation on a macro level and might even allow some (limited) form of >> quantitative validation on the macro level. >> >> Regards >> Peer-Olaf >> >> Reference: >> Moss, S. and Edmonds B. (2005). Sociology and simulation: statistical >> and >> qualitative cross-validation. American Journal of Sociology >> 110:1095-1131. >> >> > > -- :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: Dr Armando Geller Phone: +44 (0)161 247 6073 Centre for Policy Modelling Fax: +44 (0)161 247 6802 Manchester Metropolitan University Mobile: +44 (0)789 407 5810 Aytoun Building Email: [log in to unmask] Aytoun Street Web: www.cfpm.org/~armando Manchester, M1 3GH United Kingdom ...................................................................... Before acting on this email or opening any attachments you should read the Manchester Metropolitan University's email disclaimer: http://www.mmu.ac.uk/emaildisclaimer. ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::