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).

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).

146 8 months ago
 A simple agent based foraging model. Consumer agents will move between fertile patches consuming them.

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

200 8 months ago
 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.

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.
63 8 months ago
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
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
26 7 months ago
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
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
Demo of population growth with distinct agents.  @ LinkedIn ,  Twitter ,  YouTube
Demo of population growth with distinct agents.
Three Agent Model of  IM-13669 . Unconscious affective dynamics Josh Epstein's Agent Zero Book  webpage   See spatial patches version  IM-15119    
Three Agent Model of IM-13669. Unconscious affective dynamics Josh Epstein's Agent Zero Book webpage 
See spatial patches version IM-15119
 
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
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.
 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
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.
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)
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)
 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

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).

@LinkedInTwitterYouTube

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
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

This is my first attempt at creating a simple Agent Based Simulation Model. Nothing fancy, just something that works. @ LinkedIn ,  Twitter ,  YouTube
This is my first attempt at creating a simple Agent Based Simulation Model. Nothing fancy, just something that works.
A simple Susceptible - Infected - Recovered disease as a stock and flow model. @ LinkedIn ,  Twitter ,  YouTube
A simple Susceptible - Infected - Recovered disease as a stock and flow model.
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
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

 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

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.

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.
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.
Model combining system dynamics and agent based modeling. Based on Prochaska's Transtheoretical Model of Behaviour Change. See also preceding SD Version  IM-574
Model combining system dynamics and agent based modeling. Based on Prochaska's Transtheoretical Model of Behaviour Change. See also preceding SD Version IM-574
This model simulates a waterborne illness spread from a central reservoir. It illustrates the combination of System Dynamics (modeling pathogen levels in the reservoir) and Agent Based Modeling.    Make sure to check out the Map display to see the geographic clustering of disease incidence around th
This model simulates a waterborne illness spread from a central reservoir. It illustrates the combination of System Dynamics (modeling pathogen levels in the reservoir) and Agent Based Modeling.

Make sure to check out the Map display to see the geographic clustering of disease incidence around the reservoir.
Physician agents interacting with delegate agents for emergency department assessment diagnosis and treatment. From BMC  paper  May 2013, combining figs 1 and 2
Physician agents interacting with delegate agents for emergency department assessment diagnosis and treatment. From BMC paper May 2013, combining figs 1 and 2
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
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
3 7 months ago
WIP ideas about auto-configuring cross-cutting concerns and resolving where to place role relationship properties and methods
WIP ideas about auto-configuring cross-cutting concerns and resolving where to place role relationship properties and methods
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
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."