ABM | Insight Maker

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

Scott Fortmann-Roe

166 5 months ago

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

Agent Based Foraging Model

Scott Fortmann-Roe

211

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.

The Game of Life

Scott Fortmann-Roe

67

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

Complex Decision Technologies

Geoff McDonnell

28 4 months ago

Demo of population growth with distinct agents.

@LinkedIn, Twitter, YouTube

Agent Population

Gene Bellinger

50

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

Complex Intervention Modeling Areas

Geoff McDonnell

9

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

@LinkedIn, Twitter, YouTube

Your First ABM

Gene Bellinger

18

Three Agent Model of IM-13669. Unconscious affective dynamics Josh Epstein's Agent Zero Book webpage

See spatial patches version IM-15119

Fear Conditioning 3 Agents

Geoff McDonnell

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)

Modelling human behaviour (MoHuB)

Geoff McDonnell

9

WIP Combining SD and ABM Representations

Combined SD and ABM SIR Disease Dynamics

Geoff McDonnell

16

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.

@LinkedIn, Twitter, YouTube

Random Walk ABM

Gene Bellinger

34

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

Spatially Aware SIR Disease Model

Gene Bellinger

34

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. See next version at IM-15690

Fear Conditioning 3 Agents with Spatial Patches

Geoff McDonnell

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.

Electric Car Parking

Ray Madachy

12

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.

@LinkedIn, Twitter, YouTube

The Game of Life

Gene Bellinger

12 7 months ago

A simple Susceptible - Infected - Recovered disease as a stock and flow model.

@LinkedIn, Twitter, YouTube

SIR Disease Model

Gene Bellinger

7

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 Smoking Cessation

Geoff McDonnell

12

From IM-3533 Grimm's ODD and Nate Osgood's ABM Modeling Process and Courses based on Volker Grimm and Steven F. Railsback's 2012 paper and Muller et al 2013 paper Describing Human Decisions in Agent-based Models – ODD + D, An Extension of the ODD Protocol', Environmental Modelling and Software, 48: 37-48.

Pattern Oriented Modelling

Geoff McDonnell

3

Completion of IM-15119 (which added patches to IM-14058). Unconscious affective dynamics Josh Epstein's Agent Zero Book webpage Part II p.89 with 2 agent types, spatial patches and location aware, mobile occupying (blue) agents

Fear Conditioning using 2 Agent types

Geoff McDonnell

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

Wu Ming

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 Clone of Parking Lot Problem (WIP)

Lynard Misa

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."

First ABM Attempt: Modeling Student Mastery

Kate Glassman

7

Wikipedia: 100 Prisoners Problem

YouTube Video: The Riddle That Seems Impossible Even If You Know The Answer

100 Prisoners Problem - PUBLIC

Chris Fortuin

3 months ago

Hybrid conceptual mapping of relationships involving system causal loop diagram linked with ABM. Output of the problem conceptualization phase of the modelling process prior to developing a computational hybrid model in AnyLogic. Includes Nate Osgood's O PARTIES extension of Ross Hammond's PARTE. See also earlier Canadian version Insight

Conceptual map of hybrid CLD and ABM

Geoff McDonnell

3

ABM Models