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
ABM
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
Suprgya Bhushan
From Schluter et al 2017  article  A framework for mapping and comparing behavioural theories in models of social-ecological systems COMSeS2017  video .   See also Balke and Gilbert 2014 JASSS  article  How do agents make decisions? (recommended by Kurt Kreuger U of S)
Agent Environment Economics Framework Behavior PCT Decision-Making Learning ABM
From Schluter et al 2017 article A framework for mapping and comparing behavioural theories in models of social-ecological systems COMSeS2017 video. See also Balke and Gilbert 2014 JASSS article How do agents make decisions? (recommended by Kurt Kreuger U of S)
Clone of Modelling human behaviour (MoHuB)
Nilo Guimaraes
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).
ABM KeLE ABM

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
Bechara Assouad
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.
ABM
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
Ray Madachy
5
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.
ABM KeLE ABM

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
Alex Tupper
this is my first attempt
ABM
this is my first attempt
First ABM
SungwooLEE
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.
ABM
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
Natty Gur
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 simu
ABM
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.
Undergraduate curriculum model 1
Joe LeDoux
This model is a classic instance of an Erlang Queuing Process. We have the entities: - A population of cars which start off in a "cruising" 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 sim
ABM
This model is a classic instance of an Erlang Queuing Process.

We have the entities:
- A population of cars which start off in a "cruising" 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 Electric Car Parking
Ray Madachy
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。
ABM Disease
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
Allen Zhang
Based on Nate Osgood's April 2014 Singapore Presentation  Youtube video  and Lyle Wallis material Gojii at  DecisioTech  See also  Complex Decison Technologies IM  as a more polished version
Behavior Hybrid ABM Engineering Causation Decision Making Complexity Multiscale Systems Methods Health Care
Based on Nate Osgood's April 2014 Singapore Presentation Youtube video and Lyle Wallis material Gojii at DecisioTech See also Complex Decison Technologies IM as a more polished version
Clone of Clone of Complex Intervention Modeling Areas
Ana Cristina Beja
This is my first attempt at creating a simple Agent Based Simulation Model. Nothing fancy, just something that works.
ABM
This is my first attempt at creating a simple Agent Based Simulation Model. Nothing fancy, just something that works.
Clone of Your First ABM
Somdeep Nandy
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).
ABM KeLE ABM

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 Clone of Agent Based Disease Simulation
Stanislava Mildeova
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.
ABM KeLE ABM

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
Bart Bias
Tutorial model of disease dynamics using ABM
ABM Disease
Tutorial model of disease dynamics using ABM
Clone of Clone of Agent-Based Disease Dynamics
charbel saad
This is my first attempt at creating a simple Agent Based Simulation Model. Nothing fancy, just something that works.
ABM
This is my first attempt at creating a simple Agent Based Simulation Model. Nothing fancy, just something that works.
Clone of Your First ABM
Remigiusz Kinas
This model is a classic instance of an Erlang Queuing Process. We have the entities: - A population of cars which start off in a "cruising" 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 sim
ABM
This model is a classic instance of an Erlang Queuing Process.

We have the entities:
- A population of cars which start off in a "cruising" 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 Electric Car Parking
Ismael Costa
A simple agent based foraging model. Consumer agents will move between fertile patches consuming them.
ABM

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

Clone of Agent Based Foraging Model
LUCAS TEIXEIRA OLIVEIRA
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.
ABM KeLE ABM

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
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).
ABM KeLE ABM

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
Stanislav Kubis
Tutorial model of disease dynamics using ABM
ABM Disease
Tutorial model of disease dynamics using ABM
Clone of Clone of Agent-Based Disease Dynamics
charbel saad
This is my first attempt at creating a simple Agent Based Simulation Model. Nothing fancy, just something that works.
ABM
This is my first attempt at creating a simple Agent Based Simulation Model. Nothing fancy, just something that works.
Clone of Your First ABM
Rob Bemthuis
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).
ABM KeLE ABM

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
David Spencer
Insight diagram
ABM
ABM
Alexandr