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.]

Completion of  IM-15119  (which added patches to  IM-14058 ). Unconscious affective dynamics Josh Epstein's Agent Zero Book  webpage   Part II p.89 with 2 agent types, spatial patches and location aware, mobile occupying (blue) agents
Completion of IM-15119 (which added patches to IM-14058). Unconscious affective dynamics Josh Epstein's Agent Zero Book webpage  Part II p.89 with 2 agent types, spatial patches and location aware, mobile occupying (blue) agents

 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.
 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.
 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.
 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 .

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.

Physician agents interacting with delegate agents for emergency department assessment diagnosis and treatment. From BMC  paper  May 2013, combining figs 1 and 2
Physician agents interacting with delegate agents for emergency department assessment diagnosis and treatment. From BMC paper May 2013, combining figs 1 and 2
The story board runs through the premise of the project with the approach I took
The story board runs through the premise of the project with the approach I took
WIP ideas about auto-configuring cross-cutting concerns and resolving where to place role relationship properties and methods
WIP ideas about auto-configuring cross-cutting concerns and resolving where to place role relationship properties and methods
8 months ago
Attempt to learn ABM models Modeling Congestion on a narrow bridge  Multiple forms of traffic, Trucks, cars, possibly others     Random spacing between start times  Cannot travel faster than the vehicle in front  Speed slows as on-coming traffic approaches
Attempt to learn ABM models
Modeling Congestion on a narrow bridge
Multiple forms of traffic, Trucks, cars, possibly others

Random spacing between start times
Cannot travel faster than the vehicle in front
Speed slows as on-coming traffic approaches
This model is cloned thru an Agent-Based Modeling Simulation of "Covid-19 (ABM)_VHK" Model by Venkata Habiram Koppaka last April 2020 for presenting the Pandemic COVID-19 Disease. This ABM Simulation aims to represent the trend of COVID-19 infection and death rate (dynamics) at Puerto Princesa City,
This model is cloned thru an Agent-Based Modeling Simulation of "Covid-19 (ABM)_VHK" Model by Venkata Habiram Koppaka last April 2020 for presenting the Pandemic COVID-19 Disease. This ABM Simulation aims to represent the trend of COVID-19 infection and death rate (dynamics) at Puerto Princesa City, PALAWAN using the June 3, 2021 data of the CESU-PPC.
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 no
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 

A simple Markov chain modeling the transfer of power between two parties in the US Senate. Developed using data from FiveThirtyEight.com for the years 1978-2018.    Transition matrix:            R   D  R    .7   .3  D    .4   .6
A simple Markov chain modeling the transfer of power between two parties in the US Senate. Developed using data from FiveThirtyEight.com for the years 1978-2018.

Transition matrix:

       R   D
R    .7   .3
D    .4   .6


From Schluter et al 2017  article  A framework for mapping and comparing behavioural theories in models of social-ecological systems COMSeS2017  video .   See also Balke and Gilbert 2014 JASSS  article  How do agents make decisions? (recommended by Kurt Kreuger U of S)
From Schluter et al 2017 article A framework for mapping and comparing behavioural theories in models of social-ecological systems COMSeS2017 video. See also Balke and Gilbert 2014 JASSS article How do agents make decisions? (recommended by Kurt Kreuger U of S)
 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 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
 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
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