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110 Socially Intelligent Agents

studying applied problems do not regard data collection about social behaviour
as an important part of the design process. Those interested in co-operating
robots on a production line assess simulations in instrumental terms. Do they
solve the problem in a timely robust manner?
The instrumental approach cannot be criticised provided it only does what
it claims to do: solve applied problems. Nonetheless, there is a question about
how many meaningful problems are “really” applied in this sense. In practice,
many simulations cannot solve a problem “by any means”, but have additional
constraints placed on them by the fact that the real system interacts with, or
includes, humans. In this case, we cannot avoid considering how humans do
the task.
Even in social science, some researchers, notably Doran [8] argue that the
role of simulation is not to describe the social world but to explore the logic
of theories, excluding ill-formed possibilities from discussion. For example,
we might construct a simulation to compare two theories of social change in
industrial societies. Marxists assert that developing industrialism inevitably
worsens the conditions of the proletariat, so they are obliged to form a revo-
lutionary movement and overthrow the system. This theory can be compared
with a liberal one in which democratic pressure by worker parties obliges the

powerful to make concessions. Ignoring the practical difficulty of constructing
such a simulation, its purpose in Doran’s view is not to describe how indus-
trial societies actually change. Instead, it is to see whether such theories are
capable of being formalised into a simulation generating the right outcome:
“simulated” revolution or accommodation. This is also instrumental simula-
tion, with the pre-existing specification of the social theory, rather than actual
social behaviour, as its “data”.
Although such simulations are unassailable on their own terms, their rela-
tionship with data also suggests criticisms in a wider context. Firstly, is the
rejection of ill-formed theories likely to narrow the field of possibilities very
much? Secondly, are existing theories sufficiently well focused and empirically
grounded to provide useful “raw material” for this exercise? Should we just
throw away all the theories and start again?
The second exception is that many of the most interesting social simulations
based on MAS do make extensive use of data [1, 16]. Nonetheless, I think it is
fair to say that these are “inspired by” data rather than based on it. From my
own experience, the way a set of data gets turned into a simulation is something
of a “dark art” [5]. Unfortunately, even simulation inspired by data is untypical.
In practice, many simulations are based on agents with BDI architectures (for
example) not because empirical evidence suggests that people think like this
but because the properties of the system are known and the programming is
manageable. This approach has unfortunate consequences since the designer
has to measure the parameters of the architecture. The BDI architecture might