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ABM Models

These models and simulations have been tagged “ABM”.

Related tagsMetapopulationDiseaseHybridHealth CareFeedbackDecision Making

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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
Profile photo Nathaniel Vala
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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)
Modelling human behaviour (MoHuB)
Profile photo Geoff McDonnell
11
Insight diagram
WIP Combining SD and ABM Representations
Clone of Combined SD and ABM SIR Disease Dynamics
Profile photo Olga Kolchyna
Insight diagram
Wikipedia: 100 Prisoners Problem
YouTube Video: The Riddle That Seems Impossible Even If You Know The Answer
The 100 Prisoners Riddle - PUBLIC V2
Profile photo Chris Fortuin
8 months ago
Insight diagram
WIP Combining SD and ABM Representations
Clone of Combined SD and ABM SIR Disease Dynamics
Profile photo MaxMuster
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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. See next version at IM-15690

Fear Conditioning 3 Agents with Spatial Patches
Profile photo Geoff McDonnell
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: Pia Mae M. Palay

ABM Model of COVID-19 in Puerto Princesa City
Profile photo Pia Mae Palay
11
Insight diagram
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)
Clone of Modelling human behaviour (MoHuB)
Profile photo Luis R Matos
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Three Agent Model of IM-13669. Unconscious affective dynamics Josh Epstein's Agent Zero Book webpage 

Clone of Fear Conditioning 3 Agents
Profile photo garywolf
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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

Clone of Fear Conditioning using 2 Agent types
Profile photo Steven van Hekelen
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
Profile photo Igor Benić
Insight diagram

Modélisation spatiale et multi-agents d'une épidémie. Avec trois classes d'individus: susceptibles (sains), infectés (malades et contagieux), et remis (sains et temporairement immunisés).

Traduit de 

https://insightmaker.com/insight/2846/Agent-Based-Disease-Simulation  


Clone of Épidémie Multi-Agents
Profile photo Igor Belot
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)
Profile photo David Joseph McLaren
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
Profile photo Luis R Matos
Insight diagram
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.
Clone of Accident warning through VANET
Profile photo Abhinav Kapoor
Insight diagram
3бөлім Агент
Profile photo Gauhar Mukhameddamin
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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.
If you find these contributions meaningful your sponsorship would be greatly appreciated.
Clone of The Game of Life
Profile photo Mitchel Fry
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 Reservoir Disease Spread
Profile photo Geoff McDonnell
7
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A simple Susceptible - Infected - Recovered disease as a stock and flow model.
@LinkedIn, Twitter, YouTube
SIR Disease Model
Profile photo Gene Bellinger
7
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Modelling the effect of street trees
Profile photo Jian Song
8
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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.
Clone of Accident warning through VANET
Profile photo Abhinav Kapoor
Insight diagram
3 өзіндік
Profile photo Aigen Bekbolatkyzy
3
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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
Clone of Conceptual map of hybrid CLD and ABM
Profile photo Luis R Matos
Insight diagram
WIP Combining SD and ABM Representations
Clone of Combined SD and ABM SIR Disease Dynamics
Profile photo Dusan Pavlovic
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