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

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

ABM Model Test

ABM

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

I used the "disease dynamics" tutorial to help me construct this ABM, in which the individual agents are students and the states in which they can find themselves (with regard to learning a new skill/concept) include "confusion," "familiarity," and "mastery." I modeled the transitions from one state to the next under the assumption that a student cannot transition from "mastery" of a particular concept back to "confusion." This model also operates under the assumption that the more students who become familiar with a skill, the more likely it is that other students will, too (presumably, students help each other). 
The skill I imagined being taught to these students is something like Argumentative Writing, as most students can become "familiar" with this skill (or perform "satisfactorily" in it), while only some students are likely to "master" this skill in a given school year. 
I labeled the transitions "exposure" and "practice" to signify that exposing students to a new skill/concept tends to lead to their becoming familiar with it, while students taking on the task of practicing is the only way for them to transition to mastery. 
I complicated this model by adding a teacher to the mix. I also changed the number of states that students can exhibit in order to make it such that there is a 50/50 chance that once a student has learned a skill, he/she will enter a state of confusion as opposed to familiarity with the new skill/concept. The states that teachers can enter include "helpful" and "overwhelmed." The "overwhelmed" state depends on the number of students who are in a state of confusion (asking too many questions). As students transition to the states of familiarity or mastery, the teacher becomes less overwhelmed and moves back into the state of simply being "helpful."  

ABM

The story board runs through the premise of the project with the approach I took

ABM Assignment MGMT220

ABM

An implementation of the Butterfly model from Agent-Based and Individual Based Modeling by Steven Railsback and Volker Grimm. 
Model ODD: http://www.railsback-grimm-abm-book.com/Chapter04/ButterflyModelODD.txt

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

An implementation of the classic Game of Life (original de Scott Fortmann-Roe 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 IBM

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 Disease

Tutorial model of disease dynamics using ABM

Disease ABM

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.

ABM

Model combining system dynamics and agent based modeling. Based on Prochaska's Transtheoretical Model of Behaviour Change. See also preceding SD Version IM-574

Health Care Prevention Smoking Tobacco Hybrid Behavior ABM

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).

ABM Health

ABM

A recent article by a former Australian cricketer got me thinking about how Agent Based Models could help understand how bowlers and batsmen behave in cricket.... 
http://www.theguardian.com/sport/blog/2013/aug/20/ashes-glenn-mcgrath-england-australia

To try and keep things simple I've assumed there are 4 bowlers and 4 batsmen.
There are 2 innings for each side in each game and 5 games in the series, therefore 10 innings for each bowler-batsman combination in the series.
In each innings I model, for each batsman, the following parameters:
- A baseline estimate for how likely each bowler is to get the batsman out in each innings (the baseline varies between batsmen, but is kept constant for each combination for each innings)- Whether or not the bowler got the batsman out in this innings- The number of times the bowler has got the batsman out in previous innings

Cricket ABM

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.

ABM

From Schluter et al 2017 article A framework for mapping and comparing behavioural theories in models of social-ecological systems 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

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.

Erlang ABM

WIP based on previous work on obesity

Health Care Obesity ABM Map

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

Agent Zero Psychology Causation ABM Hybrid

Modelo Baseado em Agente para a dispersão espacial de doenças, considerando o modelo SIR com perda da imunidade ao vírus, conforme [Bellinger G.]

ABM DOENÇAS SIR

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