ABM | Insight Maker

WIP Combining SD and ABM Representations

Clone of Combined SD and ABM SIR Disease Dynamics

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)

Luis R Matos

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

Guilherme Werpel Fernandes

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

Clone of Agent Based Foraging Model

James Bevins

Clone of Modeling User Adoption

Suprgya Bhushan

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

Clone of Agent Based Foraging Model

lee gwo guang

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).

Clone of Clone of Agent Based Disease Simulation

Andrew Bryant

Physician agents interacting with delegate agents for emergency department assessment diagnosis and treatment. From BMC paper May 2013, combining figs 1 and 2

Clone of ED Physician Delegation Hybrid Model

David Gilding

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

Ben

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

Joe

Demo of population growth with distinct agents.

Clone of Agent Population

Chia-Hsiu Chang

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

ABM Random Walk

Gary Schasteen

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 Spatially Aware Disease Model

Ray Madachy

6

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

harari

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

Guilherme Werpel Fernandes

Model combining system dynamics and agent based modeling. Based on Prochaska's Transtheoretical Model of Behaviour Change. See also preceding SD Version IM-574

Clone of Clone of Smoking Cessation

Evgeny Volkov

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 Parking Lot Problem (WIP)

David Joseph McLaren

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

FJR

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

Clone of Agent Based Foraging Model

marcus james thomson

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

Clone of Agent Based Foraging Model

Dr Steve Moran

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)

Maurice W.

WIP Combining SD and ABM Representations

Clone of Combined SD and ABM SIR Disease Dynamics

Dorothea

ABM Models