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I was caused to consider the problem of generalisable human behaviour by a presentation on evacuation modelling. In my eyes the ability to model or predict anything, and make the implementation transferable across different contexts is contingent on the assumption that you know how people will act. The fallacy here is that you know how people will act, but you don’t necessarily know how individuals will act. It is easy (relatively) to aggregate across social characteristics and say: young people move at x speed, but old people move at y speed, and use it as a way of building socially stratified and perhaps logically more realistic models (this is often known as disaggregation). This approach seems to work well for regional systems, and large groups of people, however as the system of interest becomes more and more localised, individuals whose observable characteristics diverge enough from a generalisable norm can actually have an important role in the outcome of the model. I tend to think of this group as the 10% who change everything, although the actually percentage is likely to vary contextually.

In my work I have been looking at uptake and registration with GPs (doctors’ surgeries) with a view to isolating the demographic qualifiers of choice that create these different spatial patternings of uptake. Essentially attempting to find interesting social disaggregations within the data. In this work, which is at the level of the surgery, but for a PCT system (i.e. administrative health area), it is clear that some people, a small minority, act unexpectedly and differently to others with the exact same socio-economic characteristics, and that the problem is exacerbated when you look at small and smaller problems. For example, the largest surgeries in Southwark have between 10,000 and 20,000 registered patients, for these it is far easier to model patient registration as a function of distance than it is for a surgery of only 2,000 people. This is because larger surgeries can aggregate out the small number of ‘deviants’ better than a small surgery can. This is a defacto small numbers problem – the effect of a small number of outlying cases has a larger effect on smaller units of aggregation (surgeries) because they make up a larger proportion of the total population.

What does this mean for small-scale agent based simulations then? Well, as far as I can see it is very difficult to predict who out of a population is likely to diverge from their socially-stratified peers and be the outlying individual, and since the scale of simulation is so localised this uncertainty is liable to dramatically change the predicted outcomes. Thus in any case estimating the likely outcome within a margin for error is plausible – x people of y population were subject to some disadvantageous outcome (i.e. death/injury), but assessing where deaths/injuries occured, or the characteristics of who died/was injured may be a bit ambitious, or open to a quantitatively unacceptable uncertainty.

Naturally, understanding local level social systems should be a priority, and deriving generalisable rule-bases to give the best possible outcome for the incidence of a given phenomena is important. However, I think we always have to accept that in these circumstances a small number of people can have a large effect on the outcome in a way which is largely incalculable.