ABM | Insight Maker

Clone of Modeling User Adoption

Suprgya Bhushan

Demo of population growth with distinct agents.

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Clone of Agent Population

Alexandria LeClerc

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.

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Clone of Random Walk ABM

Cilem Bozdemir

A simple agent based foraging model. Consumer agents will move between fertile patches consuming them.

Clone of Agent Based Foraging Model

Carlos

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

Marvelúcia

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

Yong Joo Park

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

Andrey Veretennikov

ABM Random Walk

Gary Schasteen

Uma implementação do clássico Game of Life usando modelagem baseada em agentes.

Regras:

Uma célula viva com menos de dois vizinhos vivos morre.
Uma célula viva com mais de três vizinhos vivos morre.
Uma célula morta com três vizinhos se torna viva.

MBA 04 The Game of Life

Ismael Victor Costa

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

Rojean R. Rosales

4

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.

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Clone of Random Walk ABM

Dakota Koki

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

Rik Blok

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

Abhinav Kapoor

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

Simulação Computacional

19

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

@LinkedIn, Twitter, YouTube

2.2 part

Айдана Елібай

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

Alessandro Batista Martins

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 Clone of The Game of Life

Brian Siervi

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

Yan Barbosa

Agent Population

Mitchel Fry

Demo of population growth with distinct agents.

Clone of Agent Population

Jian Liu

I used the "disease dynamics" tutorial to help me construct this ABM, in which the individual agents are students and the states in which they can find themselves (with regard to learning a new skill/concept) include "confusion," "familiarity," and "mastery." I modeled the transitions from one state to the next under the assumption that a student cannot transition from "mastery" of a particular concept back to "confusion." This model also operates under the assumption that the more students who become familiar with a skill, the more likely it is that other students will, too (presumably, students help each other).

The skill I imagined being taught to these students is something like Argumentative Writing, as most students can become "familiar" with this skill (or perform "satisfactorily" in it), while only some students are likely to "master" this skill in a given school year.

I labeled the transitions "exposure" and "practice" to signify that exposing students to a new skill/concept tends to lead to their becoming familiar with it, while students taking on the task of practicing is the only way for them to transition to mastery.

I complicated this model by adding a teacher to the mix. I also changed the number of states that students can exhibit in order to make it such that there is a 50/50 chance that once a student has learned a skill, he/she will enter a state of confusion as opposed to familiarity with the new skill/concept. The states that teachers can enter include "helpful" and "overwhelmed." The "overwhelmed" state depends on the number of students who are in a state of confusion (asking too many questions). As students transition to the states of familiarity or mastery, the teacher becomes less overwhelmed and moves back into the state of simply being "helpful."

Clone of First ABM Attempt: Modeling Student Mastery

Jeanette Ling

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

jacson caiton miranda

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

ERICK MARIO DO NASCIMENTO OLIVEIRA

Three Agent Model of IM-14058 with Spatial awareness. Unconscious affective dynamics Josh Epstein's Agent Zero Book webpage Part II p.89 with spatial ABM

Clone of Fear Conditioning 3 Agents with Spatial Patches

Peter Addor

ABM Models