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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).

Agent Based Disease Simulation
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This model is a classic instance of an Erlang Queuing Process.

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.
Clone of Estacionamento
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WIP Combining SD and ABM Representations
Clone of Combined SD and ABM SIR Disease Dynamics
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WIP Combining SD and ABM Representations
Clone of Combined SD and ABM SIR Disease Dynamics
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Modelo Baseado em Agente para a dispersão espacial de doenças, considerando o modelo SIR com perda da imunidade ao vírus, conforme [Bellinger G.]

Clone of Modelo de dispersão espacial de uma doença baseado em SIR-ABM
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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.
This insight is an element of the Agent Based Modeling learning module in Systems KeLE.
Clone of The Game of Life
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If an accident occurs at a place, the master car informs the OBUs of neighbouring cars in group about the accident and they change direction . Some of the cars depending upon their position become master car in other groups and the process of warning is propagated to car population in radius of the accident.
Clone of Accident warning through VANET
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A recent article by a former Australian cricketer got me thinking about how Agent Based Models could help understand how bowlers and batsmen behave in cricket.... 

http://www.theguardian.com/sport/blog/2013/aug/20/ashes-glenn-mcgrath-england-australia

To try and keep things simple I've assumed there are 4 bowlers and 4 batsmen.

There are 2 innings for each side in each game and 5 games in the series, therefore 10 innings for each bowler-batsman combination in the series.

In each innings I model, for each batsman, the following parameters:

- A baseline estimate for how likely each bowler is to get the batsman out in each innings (the baseline varies between batsmen, but is kept constant for each combination for each innings)
- Whether or not the bowler got the batsman out in this innings
- The number of times the bowler has got the batsman out in previous innings
Simulating the Ashes Test Series
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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.
Clone of The Game of Life
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Clone of Clone of First ABM Attempt: Modeling Student Mastery
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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.
Clone of The Game of Life
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Modelo Baseado em Agente para a dispersão espacial de doenças, considerando o modelo SIR com perda da imunidade ao vírus, conforme [Bellinger G.]

Clone of Modelo de dispersão espacial de uma doença baseado em SIR-ABM
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This Agent-based Model was an idea of Christopher DICarlo "Disease Transmission with Agent Based Model' aims to present the COVID cases in Puerto Princesa City as of June 3, 2021

Insight author: Jolina Rosile Magbanua

Clone of ABM Model of COVID-19 in Puerto Princesa City
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WIP Ideas for a hybrid budding SD plus ABM depression dynamics model
Clone of Hybrid Depression Dynamics Model
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当处在春节时期,疫情来临时,外来人口较多的S市的疫情传染仿真模型。
人群的状态可分为S/E/I/R/D的五个状态,S为易感染者(即S市所在人群),E为潜伏期患者(人群不会对他远离,但是会传染他人),I为感染者(为医院确诊人群,他人会远离该患者),R为康复人群,D为死亡人群。
SEIR
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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.
Clone of The Game of Life
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A simple agent based foraging model. Consumer agents will move between fertile patches consuming them.

Clone of Agent Based Foraging Model
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This is my first attempt at creating a simple Agent Based Simulation Model. Nothing fancy, just something that works.

This insight is an element of the Agent Based Modeling learning module in Systems KeLE.
Clone of Your First ABM
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Modeling User Adoption
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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).

Clone of Agent Based Disease Simulation
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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).

Agent Based Disease Simulation
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agent population example
Agent Population
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Tutorial model of disease dynamics using ABM
Agent-Based Disease Dynamics
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A new archetype, The Tyranny of Small Steps (TYST) has been observed. Explained through a system dynamics perspective, the archetypical behaviour TYST is an unwanted change to a system through a series of small activities that may be independent from one another. These activities are small enough not to be detected by the ‘surveillance’ within the system, but significant enough to encroach upon the “tolerance” zone of the system and compromise the integrity of the system. TYST is an unintentional process that is experienced within the system and made possible by the lack of transparency between an overarching level and a local level where the encroachment is taking place.

Reference:

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 

Clone of The Tyranny of small steps archetype (agent based)