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

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
162
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.

Agent Based Foraging Model
Scott Fortmann-Roe
208
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

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
65
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
Complexity Health Care Platform Methods Hybrid ABM Decision Making EHealth Technology Systems Engineering Forrester Data Science
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
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
Systems Health Care Methods Behavior Hybrid ABM Decision Making Causation Complexity Engineering Multiscale
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
Demo of population growth with distinct agents. @ LinkedIn ,  Twitter ,  YouTube
Roam ABM
Demo of population growth with distinct agents.
Agent Population
Gene Bellinger
50
Three Agent Model of  IM-13669 . Unconscious affective dynamics Josh Epstein's Agent Zero Book  webpage   See spatial patches version  IM-15119  
ABM Psychology Causation Agent Zero
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
WIP Combining SD and ABM Representations
ABM Hybrid Feedback
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
ABM Roam
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.
Random Walk ABM
Gene Bellinger
32
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)
Modelling human behaviour (MoHuB)
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
ABM Roam
This is my first attempt at creating a simple Agent Based Simulation Model. Nothing fancy, just something that works.
Your First ABM
Gene Bellinger
17
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
ABM Disease SIR Roam

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
31
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
ABM Causation Psychology Hybrid Agent Zero
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
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
Roam Life 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.
The Game of Life
Gene Bellinger
12 last month
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.
Electric Car Parking
Ray Madachy
12
A simple Susceptible - Infected - Recovered disease as a stock and flow model. @ LinkedIn ,  Twitter ,  YouTube
Roam Systems KeLE ABM Disease
A simple Susceptible - Infected - Recovered disease as a stock and flow model.
SIR Disease Model
Gene Bellinger
7
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
Causation ABM Hybrid Psychology Agent Zero
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
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 Softw
ABM Hybrid Process Concept Understanding Complexity Methods Computation Multiscale

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
Model combining system dynamics and agent based modeling. Based on Prochaska's Transtheoretical Model of Behaviour Change. See also preceding SD Version  IM-574
Tobacco Hybrid ABM Behavior Health Care Prevention Smoking
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
11
A simple agent based foraging model. Consumer agents will move between fertile patches consuming them. This insight is an element of the  Agent Based Modeling  learning module in  Systems KeLE .
Systems KeLE ABM Foraging Model

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

This insight is an element of the Agent Based Modeling learning module in Systems KeLE.

Clone of Foraging Model
Nathaniel Vala
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."  
First ABM Attempt: Modeling Student Mastery
Kate Glassman
7
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. Se
Methods Multiscale Design ABM Agent Hybrid AnyLogic Map Concept
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
The system in which it was possible to vary the number and size of street trees in a city, and to see the concomitant increases and decreases in the amount of pollution in that city.
ABM
The system in which it was possible to vary the number and size of street trees in a city, and to see the concomitant increases and decreases in the amount of pollution in that city.
Street Trees Model
Jian Song
4
Physician agents interacting with delegate agents for emergency department assessment diagnosis and treatment. From BMC paper  May 2013, combining figs 1 and 2
Health Care ABM Regional Services Clinical Care Emergency Patient Flow Work
Physician agents interacting with delegate agents for emergency department assessment diagnosis and treatment. From BMC paper May 2013, combining figs 1 and 2
ED Physician Delegation Hybrid Model
Geoff McDonnell