There is a wish that if the right assumption can be made, the 'field work' to make a model can be missed out. This desire for a 'quick fix' leads to use of (unvalidated) prior assumptions. However, structured uncertainty that means that assumptions cannot just be applied, assuming that they will average out. This radical uncertainty, means that such things as the Law of Large Numbers will not apply. Instead, social simulation needs to copy aspects of biology:

• Lots of field-work and description
• Post hoc generalisation and approximation
• Almost no generally applicable theory

The aim then is to produce a descriptive model, which after it has been developed, can be used to make assumptions.

bottom-up, descriptive approach

This approach aims not to take some pre-theory of cognition, but to develop the model to capture reports of social mechanisms as directly as possible. This will then be validated against the people involved, the expert opinion of stakeholders, academics and the data, and then adapted again as necessary. The idea is to present these interested parties with a context for provoking a response. Then, patterns will be looked for, and generalisations made to summarise emergent processes. These will then be used in a coarser grained model, and then this process will be repeated

SDML's support for social simulation

This support is computational, not numeric. The syntax used for facts is very flexible, and includes names numbers, matrices and symbols, and is kept in databases throughout the model. The rules then act upon these databases of facts to produce a set of facts consistent with rules. The aim is the manipulation of tokens, from the point of view of the stakeholders; this is not just a social experiment. This means that the process of interpretation is as natural and straightforward as possible, and that is easier to model qualitative processes.

declarative approach

The approach taken means that control is separated from the data, and that the flexibility of knowledge representation is maximised. This means that changes required in response to comments made by the stakeholders can more easily be made. Furthermore, this means that relations are specified, and that processes emerge to be examined, rather than that processes are specified and relations and state emerge.

agent and temporal features

Agents and objects naturally occur in description of stakeholders (and others). This means that they can more easily be adapted into the model. There is included local time/variable temporal granularity to suit situations, and non instantaneous communication forces appropriate model development. There are composite agents for institutions, collections etc.

result modelling (1)

• The complexity of the model that may result from this is surely inevitable in light of the complex subject area. As it is therefore difficult to understand the results of the model, so it is necessary to model the results of the model. A complete browsable simulation record means you do not have to guess what data you will need to record before hand, and pseudo-linguistic output means that non-experts can easily relate to the results.

result modelling (2)

• The results are queryable (including to simple graphs), which facilitates the understanding of complex models.

result modelling (3): flexibility (1)

The large vocabulary of built-in predicates means that new predicates are easy to develop and incorporate. The multiple inheritance type hierarchy and modules, and declarative basis means that there has a sharp learning curve, but this is then followed by rapid model development and adaptation. This means that it can used responsively, supporting iterative and stakeholder led development, and responding to user demand.

result modelling (3): flexibility (2)

The fact that the model has within it hooks for integration to other systems/models and runs on many platforms means that developing and aligning models at different grains and aspects are facilitated (compositional methodology).

exploration of possibilities

One of the advantages of this system is that the model can be used to explore possibilities. There is controlled arbitrariness, and constraint-based features for exploration of complete space. This allows known uncertainties to be explicitly represented and tagged, and means that the uncertainty of outcomes in the model can be rigorously explored and determined

SDML's role in FIRMA

Other advantages of this system are the fast and flexible exploration and development of modelling techniques it permits. Models of different types can be integrated, and at different granularities/levels. SDML can be a way of facilitating dialogue between academics and stakeholders