public:e-217-prog-2010-1:thorisson-simulation-1
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Table of Contents
E-217-PROG-2010-1: Simulation 1 Thorisson Lecture Notes
Concepts
Simulation | A model of a process that can transform an initial state to a future state |
Scientific Method | In Western science, the usage of a set of principles that facilitate the production of reliable knowledge |
Theory (isl. kenning) | “A set of statements or principles devised to explain a group of facts or phenomena, especially one that has been repeatedly tested or is widely accepted and can be used to make predictions about natural phenomena.” REF A theory is a relatively big explanation, covering several phenomena, often through a single principle, or a set of simple principles. |
Hypothesis (isl. tilgáta) | Is a prediction about the relationship between a limited set of phenomena, as explained by a particular theory |
Experimental design | “A planned interference in the natural order of events“ |
Sample: Subject selection from a “population” A representative subset, drawn from a population, of the phenomenon we are studying. |
Examples:
a. Siggi, Maggi and Biggi representing human males.
b. 10 lakes representing all freshwater on the Earth's surface.
c. rust on bottom of doors representing the overall state of an automobile.
A sample should be randomly chosen to (1) minimize spurious correlations and thus (2) maximize the generalizability of the results of measuring only a small subset of the phenomenon. |
Sample Distribution | |
Data | Typically “raw numbers” – only contain low-level semantics |
Information | Processed and prepared data |
Statistics | Mathematical methods for dealing with uncertainty |
The Scientific Method: Classical Description
Identification, description and formalization of phenomenon | 1. Observation and description of a phenomenon or group of phenomena. |
Hypothesis, null-hypothesis | 2. Formulation of an hypothesis to explain the phenomena. In physics, the hypothesis often takes the form of a causal mechanism or a mathematical relation. |
Creation of experimental setup to test hypothesis | 3. Use of the hypothesis to predict the existence of other phenomena, or to predict quantitatively the results of new observations. |
Performance of experiment, collection and analysis of results | 4. Performance of experimental tests of the predictions by several independent experimenters and properly performed experiments. Basic assumption: Repeatability — Can be repeated by anyone anywhere |
Repeatability requires formal framework | Detailed description, clear goals, clear (limited) scope, hence the formalities in their execution |
Key idea: Comparsion | Baseline collected in same experimental setup without any other intervention by experimenter |
Where Simulation Fits In
“Connecting Glue” | Bridges between real-world experimentation, theory and hypotheses |
More flexible | than real-world experiments |
Less reliable | than real-world experiments |
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