Insight diagram
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
Insight diagram
This model simulates a waterborne illness spread from a central reservoir. It illustrates the combination of System Dynamics (modeling pathogen levels in the reservoir) and Agent Based Modeling.

Make sure to check out the Map display to see the geographic clustering of disease incidence around the reservoir.
Clone of Clone of Clone of Reservoir Disease Spread
Insight diagram
Three Agent Model of IM-14058 with Spatial awareness. Unconscious affective dynamics Josh Epstein's Agent Zero Book webpage  Part II p.89 with spatial ABM

Clone of Fear Conditioning 3 Agents with Spatial Patches
Insight diagram
Demo of population growth with distinct agents.
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).

Agent Based Disease Simulation
Insight diagram
This Agent-based Model was an idea of Christopher DICarlo "Disease Transmission with Agent Based Model' aims to present the COVID cases in Puerto Princesa City as of June 3, 2021

Insight author: Jolina Rosile Magbanua

Clone of ABM Model of COVID-19 in Puerto Princesa City
Insight diagram
This Agent-based Model was an idea of Christopher DICarlo "Disease Transmission with Agent Based Model' aims to present the COVID cases in Puerto Princesa City as of June 3, 2021

Insight author: Jolina Rosile Magbanua

Clone of Clone of Clone of ABM Model of COVID-19 in Puerto Princesa City
Insight diagram
Clusters of interacting methods for improving health services network design and delivery. Includes Forrester quotes on statistical vs SD methods and the Modeller's dilemma. Simplified version of IM-14982 combined with IM-17598 and IM-9773
Clone of Complex Decision Technologies
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 Parking Lot Problem (WIP)
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
Artificial Economics Model based on Multi-Avatar Agents following the papers: "An economic experiment to investigate Firms Fi nancial decisions" and "Towards a Multi-Avatar Macroeconomic System"



Clone of Artificial Economics based on Multi-Avatar Agents
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 Estacionamento
Insight diagram
This Agent-based Model was an idea of Christopher DICarlo "Disease Transmission with Agent Based Model' aims to present the COVID cases in Puerto Princesa City as of June 3, 2021

Insight author: Jolina Rosile Magbanua

Clone of ABM Model of COVID-19 in Puerto Princesa City
Insight diagram
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。
SEIR
Insight diagram
This Agent-based Model was an idea of Christopher DICarlo "Disease Transmission with Agent Based Model' aims to present the COVID cases in Puerto Princesa City as of June 3, 2021

Insight author: Jolina Rosile Magbanua

Clone of Clone of ABM Model of COVID-19 in Puerto Princesa City
Insight diagram
This model simulates a waterborne illness spread from a central reservoir. It illustrates the combination of System Dynamics (modeling pathogen levels in the reservoir) and Agent Based Modeling.

Make sure to check out the Map display to see the geographic clustering of disease incidence around the reservoir.
Clone of Clone of Reservoir Disease Spread
Insight diagram
This model simulates a waterborne illness spread from a central reservoir. It illustrates the combination of System Dynamics (modeling pathogen levels in the reservoir) and Agent Based Modeling.

Make sure to check out the Map display to see the geographic clustering of disease incidence around the reservoir.
Clone of Clone of Reservoir Disease Spread
Insight diagram
First attempt at transition between multiple states
OA knee multiple state ABM
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.
This insight is an element of the Agent Based Modeling learning module in Systems KeLE.
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 Clone of Clone of Complex Intervention Modeling Areas
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.

Clone of Pattern Oriented Modelling
Insight diagram
This is my first attempt at creating a simple Agent Based Simulation Model. Nothing fancy, just something that works.

This insight is an element of the Agent Based Modeling learning module in Systems KeLE.
Clone of Your First ABM
Insight diagram
WIP Combining SD and ABM Representations
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 Spatially Aware SIR Diseasse Model