Agent Based Disease Simulation
Agent Based Foraging Model
Complex Decision Technologies
The Game of Life
Complex Intervention Modeling Areas
Fear Conditioning 3 Agents
Fear Conditioning 3 Agents with Spatial Patches
Fear Conditioning using 2 Agent types
Your First ABM
Pattern Oriented Modelling
From IM-3533 Grimm's ODD and Nate Osgood's ABM Modeling Process and Courses based on Volker Grimm and Steven F. Railsback's 2012 paper and Muller et al 2013 paper Describing Human Decisions in Agent-based Models – ODD + D, An Extension of the ODD Protocol', Environmental Modelling and Software, 48: 37-48.
Random Walk ABM
Modelling human behaviour (MoHuB)
Combined SD and ABM SIR Disease Dynamics
1.0 Fear Conditioning 3 Agents
PARKING through VANET
The description of states are :
1. Cruising : State of cars which are moving out of parking area, but are still inside the parking lot.
2.Parked : State of cars which are already parked.
3. Just entered : State of cars which have just entered the parking lot and are searching for parking position.
Street Trees Model
ED Physician Delegation Hybrid Model
The Game of Life
An implementation of the classic Game of Life using agent based modeling.Rules:
- A live cell with less than two alive neighbors dies.
- A live cell with more than three alive neighbors dies.
- A dead cell with three neighbors becomes alive.
Spatially Aware SIR Diseasse Model
A spatially aware, agent based model of disease spread. There are three classes of people: susceptible (healthy), infected (sick and infectious), and recovered (healthy and temporarily immune).
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The Tyranny of small steps archetype (agent based)
Haraldsson, H. V., Sverdrup, H. U., Belyazid, S., Holmqvist, J. and Gramstad, R. C. J. (2008), The Tyranny of Small Steps: a reoccurring behaviour in management. Syst. Res., 25: 25–43. doi: 10.1002/sres.859
SIR Disease Model
We have the entities:- A population of cars which start off in a "crusing" state;- At each cycle, according to a Poisson distribution defined by "Arrival Rate" (which can be a constant, a function of time, or a Converter to simulate peak hours), some cars transition to a "looking" for an empty space state.- If a empty space is available (Parking Capacity > Count(FindState([cars population],[parked]))) then the State transitions to "Parked."-The Cars stay "parked" according to a Normal distribution with Mean = Duration and SD = Duration / 4- If the Car is in the state "Looking" for a period longer than "Willingness to Wait" then the state timeouts and transitions to impatient and immediately transitions to "Crusing" again.
The model is set to run for 24 hours and all times are given in hours (or fraction thereof)
WIP:- Calculate the average waiting time;- Calculate the servicing level, i.e., 1- (# of cars impatient)/(#cars looking)
A big THANK YOU to Scott Fortmann-Roe for helping setup the model's framework.