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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
Insight diagram

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
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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 Agent-Based and Continuous Model
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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
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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 Clone of Smoking Cessation
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Three Agent Model of IM-13669. Unconscious affective dynamics Josh Epstein's Agent Zero Book webpage 
See spatial patches version IM-15119
 
Clone of Fear Conditioning 3 Agents
Insight diagram

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
Insight diagram

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.
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Clone of The Game of Life
Insight diagram

Modélisation spatiale et multi-agents d'une épidémie. Avec trois classes d'individus: susceptibles (sains), infectés (malades et contagieux), et remis (sains et temporairement immunisés).

Traduit de 

https://insightmaker.com/insight/2846/Agent-Based-Disease-Simulation  


Clone of Épidémie Multi-Agents
Insight diagram

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
Insight diagram
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)
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Modelo Baseado em Agente para a dispersão espacial de doenças, considerando o modelo SIR com perda da imunidade ao vírus, conforme [Bellinger G.]

Clone of Modelo de dispersão espacial de uma doença baseado em SIR-ABM
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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)
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The story board runs through the premise of the project with the approach I took
42845270: Nathaniel Vala_ Assignment 3
Insight diagram

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
Insight diagram
WIP Combining SD and ABM Representations
Clone of Combined SD and ABM SIR Disease Dynamics
Insight diagram

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
3 6 months ago
Insight diagram

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.
Follow us on YouTube, Twitter, LinkedIn and please support Systems Thinking World.
Clone of The Game of Life
2 months ago
Insight diagram

Modelo Baseado em Agente para a dispersão espacial de doenças, considerando o modelo SIR com perda da imunidade ao vírus, conforme [Bellinger G.]

Clone of Modelo de dispersão espacial de uma doença baseado em SIR-ABM
Insight diagram
The story board runs through the premise of the project with the approach I took
Clone of 42845270: Nathaniel Vala_ Assignment 3
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WIP Combining SD and ABM Representations
Clone of Combined SD and ABM SIR Disease Dynamics
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Will the ducks make it to the pond?  or will the hawks swoop on them?
Duck v Hawk v3
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First attempt at transition between multiple states
OA knee multiple state ABM
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From Schluter et al 2017 article A framework for mapping and comparing behavioural theories in models of social-ecological systems See also Balke and Gilbert 2014 JASSS article How do agents make decisions? (recommended by Kurt Kreuger U of S)
Clone of Clone of Modelling human behaviour (MoHuB)