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.
 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).

Demo of population growth with distinct agents.
Demo of population growth with distinct agents.
 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 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.
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.
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
 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).

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.
 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.

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
 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 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.

If an accident occurs at a place, the master car informs the OBUs of neighbouring cars in group about the accident and they change direction . Some of the cars depending upon their position become master car in other groups and the process of warning is propagated to car population in radius of the
If an accident occurs at a place, the master car informs the OBUs of neighbouring cars in group about the accident and they change direction . Some of the cars depending upon their position become master car in other groups and the process of warning is propagated to car population in radius of the accident.
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.
 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.
 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 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 simu
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.