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.

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.     Understanding Relationship and Their Implications: The Essence of AND?
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.

当处在春节时期,疫情来临时,外来人口较多的S市的疫情传染仿真模型。 人群的状态可分为S/E/I/R/D的五个状态,S为易感染者(即S市所在人群),E为潜伏期患者(人群不会对他远离,但是会传染他人),I为感染者(为医院确诊人群,他人会远离该患者),R为康复人群,D为死亡人群。
当处在春节时期,疫情来临时,外来人口较多的S市的疫情传染仿真模型。
人群的状态可分为S/E/I/R/D的五个状态,S为易感染者(即S市所在人群),E为潜伏期患者(人群不会对他远离,但是会传染他人),I为感染者(为医院确诊人群,他人会远离该患者),R为康复人群,D为死亡人群。
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.    Follow us on  YouTube ,  Twitter ,  LinkedIn  and please support  Systems Thinking World .
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.

Follow us on YouTube, Twitter, LinkedIn and please support Systems Thinking World.
Demo of population growth with distinct agents.    This insight is an element of the  Agent Based Modeling  learning module in  Systems KeLE .
Demo of population growth with distinct agents.

This insight is an element of the Agent Based Modeling learning module in Systems KeLE.
 Modelo Baseado em Agente para a dispersão espacial de doenças, considerando o modelo SIR com perda da imunidade ao vírus, conforme [Bellinger G.]

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

 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.

Tutorial model of disease dynamics using ABM
Tutorial model of disease dynamics using ABM
 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.

 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).

 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).

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."  
Demo of population growth with distinct agents.    Follow us on  YouTube ,  Twitter ,  LinkedIn  and please support  Systems Thinking World .
Demo of population growth with distinct agents.

Follow us on YouTube, Twitter, LinkedIn and please support Systems Thinking World.
 Uma implementação do clássico Game of Life usando modelagem baseada em agentes. Regras:   Uma célula viva com menos de dois vizinhos vivos morre.  Uma célula viva com mais de três vizinhos vivos morre.  Uma célula morta com três vizinhos se torna viva.

Uma implementação do clássico Game of Life usando modelagem baseada em agentes.

Regras:
  • Uma célula viva com menos de dois vizinhos vivos morre.
  • Uma célula viva com mais de três vizinhos vivos morre.
  • Uma célula morta com três vizinhos se torna viva.
Demo of population growth with distinct agents.
Demo of population growth with distinct agents.
 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).

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 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.
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.
 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.

 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).