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).

ABM KeLE ABM

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

ABM KeLE ABM

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

Health Care Methods Hybrid ABM Decision Making EHealth Technology Systems Engineering Complexity Forrester

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.

ABM KeLE ABM

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

Health Care Methods Behavior Hybrid ABM Decision Making Causation Complexity Systems Engineering Multiscale

Three Agent Model of IM-13669. Unconscious affective dynamics Josh Epstein's Agent Zero Book webpage See spatial patches version IM-15119 

Agent Zero Psychology Causation ABM

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

Agent Zero Psychology Causation ABM Hybrid

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

Agent Zero Psychology Causation ABM Hybrid

This is my first attempt at creating a simple Agent Based Simulation Model. Nothing fancy, just something that works.
Understanding Relationship and Their Implications: The Essence of AND?

ABM KeLE ABM

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.

Methods Process Multiscale ABM Hybrid Computation Concept Understanding Complexity

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)

Environment Economics Framework Behavior Decision-Making Agent ABM Learning PCT

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.
Understanding Relationship and Their Implications: The Essence of AND?

ABM KeLE ABM

WIP Combining SD and ABM Representations

Hybrid ABM Feedback

Demo of population growth with distinct agents.
Understanding Relationship and Their Implications: The Essence of AND?

ABM KeLE ABM

Agent Zero Psychology Causation ABM

The VANET handles situation of parking in crowded areas. It takes into account the parking capacity, arrival rate of cars, already parked cars , while making decisions.
 The description of states are :


1. Cruising : State of cars which are moving out of parking area, but are still inside the parking lot.

2.Parked : State of cars which are already parked.

3. Just entered : State of cars which have just entered the parking lot and are searching for parking position.

Erlang ABM

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.

ABM

Physician agents interacting with delegate agents for emergency department assessment diagnosis and treatment. From BMC paper May 2013, combining figs 1 and 2

Health Care Emergency Patient Flow Work ABM Regional Services Clinical Care

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).

Understanding Relationship and Their Implications: The Essence of AND?

Systems KeLE ABM Disease

WIP ideas about auto-configuring cross-cutting concerns and resolving where to place role relationship properties and methods

Methods Evolution Architecture ABM Hybrid Holon Multiscale

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.

Understanding Relationship and Their Implications: The Essence of AND?

Sysetms KeLE ABM Life Game

A simple Susceptible - Infected - Recovered disease as a stock and flow model.
Understanding Relationship and Their Implications: The Essence of AND?

Systems KeLE ABM Disease

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 

Agent Based Modelling ABM System Dynamics Archetype

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.

Erlang ABM