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--- public:e-217-prog-2010-1:thorisson-simulation-2 [2010/03/24 19:26] – thorisson
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 ===== E-217-PROG-2010-1: Simulation 2 Thorisson Lecture Notes=====
+[[http://cadia.ru.is/wiki/public:e-217-prog-2010-1:thorisson-simulation-1|Thórisson Lecture 1]]
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 === More Concepts ===
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 === Creating a Model===
 | Determine the scope  | What set of questions would we want to be able to answer with our simulation? What are the most extreme conditions that the system should be able to take? What is a typical set of questions we will study?  |
-| Simplification  | All simulations select certain parts to be included and some parts to be excluded; no complete simulation exists of any natural phenomenon --
+| Simplification  | All simulations select certain parts to be included and some parts to be excluded (no complete simulation exists of any natural phenomenon -- it couldn't, because everything in the world can (potentially) affect anything else). \\ "...simplifications, or abstractions, are a necessary part of the modeling of nature. Without them, the models would mimic reality instead of explaining it." \\ — Przemyslaw Prusinkiewicz  |
 | Define parameters  | Decide which variables should be independent and dependent; deciding on how to represent these in variables in the selected programming language.  |
 | Define processes  | What are the operations we want to manipulate the parameters? What are their inputs and outputs?  |
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 === Implementing the Model as a Simulation ===
-| Selecting a programming language   |   |
+| Selecting a programming language   | Main choice is between a compiled language vs. interpreted.  |
-| Programming  |   |
+| Complied (ísl. þýdd) language  | A compiler is used to compile into machine code first, once. The machine code then runs on the CPU. \\ Pros: Runs fast. Cons: Less flexible than interpreted code, harder to develop incrementally.  |
-| Incremental development  |   |
+| Interpreted (ísl. túlkað) language  | An interpreter converts each program instruction as the program is being run. \\ Pros: More flexible runtime execution. Easier to develop incrementally.  Cons: Runs 10x slower than compiled code.  |
+| Programming  | Now you know what this is!  |
+| Incremental development  | By incremental development a simulation is built piece by piece, module by module. There are many ways to do this -- an interpreted programming language allows developers to do this flexibly; some systems exist to help with the process.  |
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+=== Simulation Creation Process ===
+{{:public:e-217-prog-2010-1:simulationprocess.png?500|Simluation Process}} \\
+(source: Nigel Gilbert (2005). Simluation for the Social Scientist, p. 13. New York: Open University Press.)
 \\
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 === Types of Simulation ===
-{{:public:e-217-prog-2010-1:typesofsimulation.png|Types of Simluation}}
+{{:public:e-217-prog-2010-1:typesofsimulation.png?600|Types of Simluation}}\\
+(source: Nigel Gilbert (2005). Simluation for the Social Scientist, p. 13. New York: Open University Press.)
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-| Cellular Automata  | A.k.a. CA \\ Grid-based computing system where each cell can have two or more states and looks at its neighbors to determine its next state. [[public:e-217-prog-2010-1:thorisson-simulation-2:CA page?350|CA]]  |
+| Cellular Automata  | A.k.a. CA \\ Grid-based computing system where each cell can have two or more states and looks at its neighbors to determine its next state. [[public:e-217-prog-2010-1:thorisson-simulation-2:CA page|CA]]  |
 | Agent-oriented  | A.k.a. Multi-agent simluations. \\ Use the concept of semi-autonomous programs that interact with each other. Compared to e.g. the cells in CAs each agent is typically very complex (think of it as a full computer program executable).  |
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 \\
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+=== Features of Agent-Based Simulation ===
+| Main component | The "agent" - a semi-autonomous piece of code.  |
+| Autonomy  | Agents are considered stand-alone entities.  |
+| Interaction  | A.k.a. "social ability" - agents can interact.  |
+| Reactivity   | Agents react to incoming data.  |
+| Proactivity  | Agents "plot" on their own.  |
+| Abstraction  | An agent in an agent-based simulation can stand for any acting entity - it is assumed to map to some real (or imagined) phenomenon with one or more of the above features. \\ Example: An agent can stand for a whole consumer market or it can stand for a single human consumer.  |
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+=== Examples of NetLogo Simulation Code ===
+{{:public:e-217-prog-2010-1:netlogo-toact.png?500|NetLogo-ToAct}} \\
+(source: Nigel Gilbert (2005). Simluation for the Social Scientist, p. 189. New York: Open University Press.)
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+{{:public:e-217-prog-2010-1:netlogo-toshop.png?500|NetLogo-ToShop}} \\
+(source: Nigel Gilbert (2005). Simluation for the Social Scientist, p. 186. New York: Open University Press.)
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 EOF