public:rem4:rem4-20:simulation
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Using Models to Validate and Measure - a.k.a. Simulation
| What simulation is | A simplified model of subject under study - that is, a simplification not of the key causal factors in the phenomenon, which must remain in our model for it to be useful, but rather a reduction (sometimes a radical one) of the “extra stuff that really doesn't matter”. |
| What it does | Simplifies! Makes it easier to (A) set up testing conditions, (B) control independent variables, (C) make changes to the independent variables,(D) measure the results. |
| When to use | When the complexity of that which is to be modeled/understood becomes so great that mathematical models are intractable and hypothesis falsification would take decades, centuries or millennia, or is simply out of the question (as in e.g. astrophysics). |
| Kinds of simulation methodologies | Continuous time and state: E.g. differential equations. Discrete time/state: E.g. automata. |
| Relation between scientific theories and simulations | To build a simulation we need a theory that tells us how things relate to each other. |
| Procedure | Pick methodology. Decide which kinds of questions to answer. Model major states/transitions or input/output/functional properties of system. Run simulations with variations in independent variables. Note outcome. Fix model. Repeat. |
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