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Challenges for IOCS:

Challenges for IOCS:. Multiscale processes Limitations of models Validation. Alex Ryan DSTO. Levels, Scale, Scope, Resolution. Level of a hierarchy Level of description Scale of effect Time scale Scope of observation Resolution of observation. Hierarchy.

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Challenges for IOCS:

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  1. Challenges for IOCS: • Multiscale processes • Limitations of models • Validation Alex Ryan DSTO

  2. Levels, Scale, Scope, Resolution Level of a hierarchy Level of description Scale of effect Time scale Scope of observation Resolution of observation

  3. Hierarchy Partially ordered set (by control) Levels screened off from each other by rate differences Time 0.1s 1s 10s 100s

  4. Cross-scale effect Multi-Level and Multi-Scale Analysis scales levels Cross-level effect

  5. Separation of Time Scales • Average over fast processes • Fix constant slow processes • Analyse the dynamics at focal level Dynamics

  6. - - - 100x 10x 10x Pattern Vs Scope & Resolution

  7. The Limitations of Models “In chemistry and physics and other natural sciences the object of experiment is to fill in the actual values of the various quantities and factors appearing in an equation or a formula; and the work when done is once and for all. In economics that is not the case, and to convert a model into a quantitative formula is to destroy its usefulness as an instrument of thought. . .” John Maynard Keynes

  8. The Limitations of Models • Cross scale effects imply that a model at any one scale is incomplete • Models never capture true novelty except in retrospect • Quantifying subjective, soft and data-poor processes can be dangerous

  9. The Necessity of Models “Having experienced the confusion that tends to arise whenever we try to relate cerebral mechanisms to observed behavior, I made it my aim to accept nothing that could not be stated in mathematical form, for only this language can one be sure, during one's progress, that one is not unconsciously changing the meaning of terms, or adding assumptions, or otherwise drifting towards confusion” W. Ross Ashby

  10. The Necessity of Models • To not model explicitly is to rely on a trivial model • An explicit model is often usefully wrong • Complex systems is founded on the model-based approach, but focussed on their assumptions and limitations

  11. Validation • The “dark secret” in the {ABM, SD, ANN, GA, …} closet • We need to think differently about validation • Validate individual behaviour not aggregates • Can’t validate in isolation to real world experimentation in context

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