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

Bruce Edmonds,Centre for Policy Modelling

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Supporting Social Simulation

• Physics vs. Biology Paradigms

• CPM’s approach to Social Simulation

• How SDML supports this approach

• SDML’s role in Firma


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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


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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


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SDML’s support for social simulation

• Description of basic mechanism

• Declarative/time level approach

• Agent/object features

• Result Modelling

• Flexibility

• Exploration of Possibilities


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SDML’s basic mechanism (1)

• User-defined tokens and syntax for facts


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• Rules act upon databases of facts to produce a set of facts consistent with rules


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• Means that process of interpretation is as natural and straightforward as possible

• Means that it is easier to model qualitative processes


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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


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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.


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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


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• Queryable (including to simple graphs)– Means that understanding complex models is

facilitated


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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


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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)


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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


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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

Documents

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