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Data-Integration Models (DIMs)

Data-Integration Models (DIMs). Bruce Edmonds, Centre for Policy Modelling, Manchester Metropolitan University. Underlying principle : Evidence should not be ignored without a very good reason.

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Data-Integration Models (DIMs)

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  1. Data-Integration Models (DIMs) Bruce Edmonds, Centre for Policy Modelling, Manchester Metropolitan University Underlying principle : Evidence should not be ignored without a very good reason Modelling Goal : to be consistent with a heterogeneous set of evidence at a level of detail which is as directly comparable to that evidence as possible In other words, to describe what is observed usinga dynamic and detailed simulation model (rather than to predict or explain). Thus including kinds of data that are difficult to formalise, e.g. partial, qualitative, meso-level etc. This does not eliminate the need for independent validation, but Data-Integration Models can form a precise basis for subsequent abstraction stages or generalisation. • Advantages • To separate the processes of representation (evidence to DIM) and abstraction & generalisation (DIM to simpler model) • To have a consistent and precise integration of the evidence, maintaining the reference of details • To be a precise reference point for associated models and theories, experiments can be performed on DIM to check more abstract models (simpler simulation, statistical and analytic models) • Can be used to integrate very different kinds and levels of evidence/data e.g. narrative accounts, aggregate data, time-series data etc. • Disadvantages • Tend to be slow and complicated • Difficult to understand completely, need further analysis An example of a DIM in use, as part of a chain of models from evidence to abstract theories Progressive abstraction from the precise starting point of a DIM, to other kinds of models (themselves models of the DIM) Representing a variety of evidence in the DIM (micro in the agent behaviour, macro against the results) Data-Integration Models will be at the core of the SCID Project (Social Complexity of Immigration and Diversity) which will use these models as the start of an abstraction, analysis and evaluation process, forming a chain of related models from descriptive to abstract. See http://www.scid-project.org

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