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ABM Models

These models and simulations have been tagged “ABM”.

Related tagsMetapopulationDiseaseHybridHealth CareFeedbackMethods

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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
Profile photo James McRae
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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
Profile photo Anca Mihaela Chifu
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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
Profile photo Sebastian Zander
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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).

@LinkedIn, Twitter, YouTube

Clone of Spatially Aware SIR Disease Model
Profile photo Cindy Diaz
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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
Profile photo kmolenda
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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
Profile photo Andriy Samilyak
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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
Profile photo Lucas Farinazzo Marques
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ABM
Profile photo Andrey
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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
Profile photo Yong Joo Park
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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
Profile photo Andrzej Popiołek
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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
Profile photo Max Tay
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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
Profile photo Arnaud Huar
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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
Profile photo Felix Huang
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This model is cloned thru an Agent-Based Modeling Simulation of "Covid-19 (ABM)_VHK" Model by Venkata Habiram Koppaka last April 2020 for presenting the Pandemic COVID-19 Disease. This ABM Simulation aims to represent the trend of COVID-19 infection and death rate (dynamics) at Puerto Princesa City, PALAWAN using the June 3, 2021 data of the CESU-PPC.
Clone of COVID-19 ABM (SIR) Model of Puerto Princesa City, PALAWAN
Profile photo Nilo Guimaraes
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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).

Spatially Aware SIR Diseasse Model
Profile photo Paulo Villela
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A simple Markov chain modeling the transfer of power between two parties in the US Senate. Developed using data from FiveThirtyEight.com for the years 1978-2018.

Transition matrix:

       R   D
R    .7   .3
D    .4   .6


Clone of Senate
Profile photo Ryan Lay
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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
Profile photo Alex Tupper
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WIP Combining SD and ABM Representations
Clone of Combined SD and ABM SIR Disease Dynamics
Profile photo John Bragin
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Clusters of interacting methods for improving health services network design and delivery. Includes Forrester quotes on statistical vs SD methods and the Modeller's dilemma. Simplified version of IM-14982 combined with IM-17598 and IM-9773
Clone of Complex Decision Technologies
Profile photo carlos salazar
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Mammal modelling

Mammal population modeling
Profile photo Arnaud Courtecuisse
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A spatially aware, agent based model of the spread of fear in a population through local contagion. Modified from a simple disease model. There are three states people can take based on: susceptible (Potentially Fearful), infected (Afraid), and recovered (Confident).

A very imperfect and laughably sketchy simple start to pursue an unreasonably pompous proposal... https://metonymize.substack.com/p/what-anthropology-might-offer-ai

Toy ABM for fear propogation as world-building
Profile photo Kripa Rajshekhar
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A random walk demonstration using an ABM. As individuals drink more they become more intoxicated and their walk becomes more random. And when they drink to much it finally kills them.
Clone of Random Walk ABM
Profile photo r mud
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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.
Accident warning through VANET
Profile photo Abhinav Kapoor
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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.
Estacionamento
Profile photo Simulação Computacional
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