1. Simulation A “model” that is a simulation of a past or potential event Typically the models are not considered general (simpler models may be) Relies on knowledge of the mechanisms behind the processes that created the event "3DiTeams percuss chest". Licensed under CC BY-SA 3.0 via Wikipedia - http://en.wikipedia.org/wiki/File:3DiTeams_percuss_che st.JPG#/media/File:3DiTeams_percuss_chest.JPG

2. Simulations are Used In: Volcanic eruption processes Flood dynamics Land slides Earthquakes Disease propagation Oil spills Species population dynamics Social dyanmis

3. Validation? Past Events: –Can ground-truth based but how generalizable are they? Future Events: –How to ground-truth? Best case: –Model based on past events, ground-truth, then extend into the future carefully

4. Civil Engineering Civil engineering is based on what has worked in the past New structures are built based on: –Understanding of materials –Books of “margins of error” based on what has worked and not worked in the past –Simulations of potential scenarios

5. Tacoma Narrows Bridge http://www.youtube.com/watch?v=j- zczJXSxnwhttp://www.youtube.com/watch?v=j- zczJXSxnw After the Tacoma narrows bridge collapsed, all suspension bridges had to be checked for harmonic oscillations against the typical winds in the area Today, this is just one of the simulations that are used to test structures in different situations.

6. Simulation Models NASA’s Perpetual Ocean –http://svs.gsfc.nasa.gov/vis/a000000/a0038 00/a003827/http://svs.gsfc.nasa.gov/vis/a000000/a0038 00/a003827/ NASA Simulation of aerosols:

7. Animations (Simulations?) Tsunamis: –http://www.youtube.com/watch?v=_bCTa5s u8IIhttp://www.youtube.com/watch?v=_bCTa5s u8II –http://www.youtube.com/watch?v=WgpXzw LuGDohttp://www.youtube.com/watch?v=WgpXzw LuGDo

8. When to simulate? Completely hypothetic scenarios Really minimal data Temporal process -> compelling animations The process is believed to be well understood (simulations are typically mechanistic) When the problem can be simplified enough to run on available hardware! Educational

9. Methods Agent-Based Cellular automaton

10. Agent: –Typically a point –Has “attributes”: health, size, age, sex, etc. –Behaves independently Moves, feeds, breeds, dies –Can “interact” with other agents –Can “interact” with its envrionment Agent Based Models www.anylogic.com

11. Environmental Science Spatially Explicit Individually Based Models (SEIBM) –Each “object” in the model represents one individual Spatially Explicit Population Based Models (SEPBM) –Each “object” represents N individuals

12. Simple Model All Agents –X –Y Predator –Hunger Prey –Health Prey 1 Pred 1

13. How it works Move agents Agent interactions –Prey Update attributes –Hunger –Birth –Death

14. Movement Each agent has an x, y coordinate Moves to a new position based on: –Random movement –Directed movement –Terrain –Forces: wind, water, slope Random Directed Lagrangian Movement

15. “Walking” Random Walk –Brownian Motion: pseudo-random movement of particles when interacting with other particles “Directed Walk” –Movement toward a resource Lévy flight foraging hypothesis –Line lengths drawn from a “heavy tailed” distribution

16. Interactions Agents interact with each other: –Breed –Feed –Interact with distance < some minimum Agents interact with the environment: –Feed on grass

17. Agents Update Attributes Hunger/Health go down without food Birth happens at some cycle if conditions are correct Death –If Hunger/Health are too high/low –Age > maximum –Conditions too harsh Also can: –Grow –Learn –Bloom, senesce

18. Life Cycle Birth Youth Adult Death

19. Individually Based Models Crowds –http://www.lsi.upc.edu/~npelechano/MACES /MACES.htmhttp://www.lsi.upc.edu/~npelechano/MACES /MACES.htm Princeton’s migration studies: –http://icouzin.princeton.edu/leadership- collective-behavior-and-the-evolution-of- migration/http://icouzin.princeton.edu/leadership- collective-behavior-and-the-evolution-of- migration/ Agent Based Traffic Model –http://www.cs.unc.edu/~wilkie/

20. Cellular Automata Monitor what is in each “cell” –Typically: Each raster has the number of individuals of one type (or amount of available veg) –Can also include: Land cover, barriers, water vs. land, etc. Difficulty to cross area Open vs. protected areas

21. Tools NetLogo HexSim MASON Multi-Agent Simulation Toolkit Repast Programming! –Python –Java Books: “Agent-Based Models of Geographical Systems”

22. Python SEIBM Very simple model Includes 2 classes: –Animal (prey and predators) –Veg (grass)

23. SEIBM – Main Script Imports: Tkinter, time, random, Veg, Animal Setup the GUI Initialize animal objects in an array Loop forever: –Update each object –Redraw the window –Let Python process events (mouse clicks) –Sleep for a bit