Bruce Edmonds, Centre for Policy Modelling 1 of 57 Supporting Social Simulation Physics vs. Biology Paradigms CPM’s approach to Social Simulation How SDML supports this approach SDML’s role in Firma

Bruce Edmonds, Centre for Policy Modelling 2 of 57 Physics vs. Biology Paradigm Wish for ‘quick fix’ leads to use of (unvalidated) prior assumptions Radical uncertainty means that such as Law of Large Numbers will not apply Social simulation needs to copy biology –Lots of field-work and description –Post hoc generalisation and approximation –Almost no generally applicable theory

Bruce Edmonds, Centre for Policy Modelling 3 of 57 Bottom-up, descriptive approach Iteratively –Develop model to capture reports of social mechanisms as directly as possible –Validate model against expert opinion of stakeholders, academics and data Look for patterns, generalisations to summarise emergent processes Use these in coarser grained model and start this process again

Bruce Edmonds, Centre for Policy Modelling 4 of 57 SDML’s support for social simulation Description of basic mechanism Declarative/time level approach Agent/object features Result Modelling Flexibility Exploration of Possibilities

Bruce Edmonds, Centre for Policy Modelling 5 of 57 SDML’s basic mechanism (1) User-defined tokens and syntax for facts

Bruce Edmonds, Centre for Policy Modelling 6 of 57 SDML’s basic mechanism (2) Rules act upon databases of facts to produce a set of facts consistent with rules

Bruce Edmonds, Centre for Policy Modelling 7 of 57 SDML’s basic mechanism (3) Means that process of interpretation is as natural and straightforward as possible Means that it is easier to model qualitative processes

Bruce Edmonds, Centre for Policy Modelling 8 of 57 Declarative approach Control separated from data Flexibility of knowledge representation maximised Means that relations are specified, processes emerge to be examined Rather than processes specified and relations and state emerge

Bruce Edmonds, Centre for Policy Modelling 9 of 57 Agent & temporal features Agents and objects naturally occur in description of stakeholders (and others) Local time/variable temporal granularity to suit situations Non instantaneous communication forces appropriate model development Composite agents etc. for institutions, collections etc.

Bruce Edmonds, Centre for Policy Modelling 10 of 57 Result Modelling (1) Complete browsable simulation record –Means that you do not have to guess what data you will need to record before hand Pseudo-linguistic output –Means that non-experts can easily relate to the results

Bruce Edmonds, Centre for Policy Modelling 11 of 57 Result Modelling (2) Queryable (including to simple graphs) –Means that understanding complex models is facilitated

Bruce Edmonds, Centre for Policy Modelling 12 of 57 Result Modelling (3)

Bruce Edmonds, Centre for Policy Modelling 13 of 57 Flexibility (1) Large vocabulary of built-in predicates, new predicates easy to develop/incorporate Multiple inheritance type hierarchy and modules Declarative basis Means that it has a sharp learning curve but then rapid model development and adaptation Means that it can used responsively, supporting iterative and stakeholder led development

Bruce Edmonds, Centre for Policy Modelling 14 of 57 Flexibility (2) Not designed for huge number of runs/agents Hooks for integration to other systems/models (need for development) Runs on many platforms Means that developing and aligning models at different grains and aspects are facilitated (compositional methodology)

Bruce Edmonds, Centre for Policy Modelling 15 of 57 Exploration of Possibilities Controlled arbitrariness Constraint-based features for exploration of complete space Some built in statistics and graphical output (needs development) Means that known uncertainties can be explicitly represented and tagged Means that the uncertainty of outcomes in the model can be rigorously explored and determined

Bruce Edmonds, Centre for Policy Modelling 16 of 57 SDML’s role in FIRMA Fast and flexible exploration and development of modelling techniques Integrating models –At different granularities/levels –Of different types To facilitate dialogue between academics and stakeholders