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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. S → S' |
| 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. | Typically you can't study all the individuals of a particular subject pool, so in your experiment you use a sample and hope that the results generalize to the rest of the subjects. 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 | If you sample data many times it may not always give the same result. Example: If you measure the temperature repeatedly over a full day, the values you get will not be identical, they will be distributed. |
| Normal distribution | The Bell Curve. Also called Gaussian Distribution.  See picture |
| 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” | a. Bridges between real-world experimentation, theory and hypotheses b. Can be considered part of the Comparative Experiment paradigm |
| More flexible | than real-world experiments: Easy to pose WHAT-IF questions Example: CCP runs a virtual world with its own economy; they can run “mixed-world” experiments that benefit from the flexibility of software |
| Less reliable | than real-world experiments |
| Challenges | a. Grounding Simulations b. Detail c. Abstraction |
| Grounding | The connection between a simulation (model) and reality |
| Complexity | How complex should we make a model/simulation? |
| Abstraction | How abstract can we make things? What is the difference between simulating a market by modeling the behavior of every individual consumer, versus modeling the market as ”%of those who buy“? |
Simulations
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