ABM | Insight Maker

Demo of population growth with distinct agents.

Follow us on YouTube, Twitter, LinkedIn and please support Systems Thinking World.

Clone of Agent Population

Khorben Boyer

WIP Combining SD and ABM Representations

Clone of Combined SD and ABM SIR Disease Dynamics

Amelia Sosnowski

WIP Combining SD and ABM Representations

Clone of Combined SD and ABM SIR Disease Dynamics

Olga Kolchyna

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).

Spatially Aware SIR Diseasse Model

Robert J. Scott

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 Clone of Spatially Aware SIR Diseasse Model

kl josc

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

Luis R Matos

ABM Model Test

@LinkedIn, Twitter, YouTube

ABM Test

Gene Bellinger

3

WIP Combining SD and ABM Representations

Clone of Combined SD and ABM SIR Disease Dynamics

Olga Kolchyna

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

Conceptual map of hybrid CLD and ABM

Geoff McDonnell

3 2 months ago

Random Walk

Chunze Liu

A spatially aware, agent based model of disease spread. There are three classes of people: susceptible (healthy), infected (sick and infectious), and recovered (health and temporarily immune).

Agent Based Disease Simulation

Dusan Pavlovic

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

This insight is an element of the Agent Based Modeling learning module in Systems KeLE.

Clone of Foraging Model

Nathaniel Vala

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

Sergei Titov

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.

Street Trees Model

Jian Song

5

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)

Lynard Misa

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.

Clone of Electric Car Parking

Wu Ming

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

Geoff McDonnell

3

First attempt at transition between multiple states

Clone of OA knee multiple state ABM

Ruri

Demo of population growth with distinct agents.

This insight is an element of the Agent Based Modeling learning module in Systems KeLE.

Clone of Agent Population

Paul Dawidowicz

3 months ago

agent population example

Agent Population

Kimberly Jones

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 Estacionamento