Insight diagram

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

Clone of Agent Based Foraging Model
Insight diagram
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?
Clone of Random Walk ABM
Insight diagram
Tyler Connors Random Walk
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

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

Clone of Agent Based Foraging Model
Insight diagram
Demo of population growth with distinct agents.

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Clone of Agent Population
Insight diagram

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).

Clone of CS558 Agent-Based Spatially Aware Disease Model
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
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
Clone of Complex Intervention Modeling Areas
Insight diagram

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).

Clone of Agent Based Disease Simulation
Insight diagram

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).

Clone of Agent Based Disease Simulation
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.
Clone of The Game of Life
Insight diagram

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).

Clone of Spatially Aware SIR Diseasse Model
Insight diagram

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).

Clone of Agent Based Disease Simulation
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
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.

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

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).

Clone of Agent Based Disease Simulation
Insight diagram
A new archetype, The Tyranny of Small Steps (TYST) has been observed. Explained through a system dynamics perspective, the archetypical behaviour TYST is an unwanted change to a system through a series of small activities that may be independent from one another. These activities are small enough not to be detected by the ‘surveillance’ within the system, but significant enough to encroach upon the “tolerance” zone of the system and compromise the integrity of the system. TYST is an unintentional process that is experienced within the system and made possible by the lack of transparency between an overarching level and a local level where the encroachment is taking place.

Reference:

Haraldsson, H. V., Sverdrup, H. U., Belyazid, S., Holmqvist, J. and Gramstad, R. C. J. (2008), The Tyranny of Small Steps: a reoccurring behaviour in management. Syst. Res., 25: 25–43. doi: 10.1002/sres.859 

Clone of The Tyranny of small steps archetype (agent based)
Insight diagram
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?
Clone of Random Walk ABM
Insight diagram
Demo of population growth with distinct agents.

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