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Working model of Yellowstone dynamics created by students in Ecology 2015 at McDaniel College. This class collaboration is the first attempt by these students to develop a functioning model that includes competition, disease, predation, invasives and impacts of environmental variables on the major species over the last 30 years.
Although we are attempting to create a realistic model, we are not researchers and depend on varied data sources for coefficients.

Eco15 Yellowstone Model

Esther

14

IM-1175 with computable arguments, based on ideas from Micropublications paper about Claims, Evidence, Representations and Context Networks

Toulmin's Argument Model and Micropublications

Geoff McDonnell

Assignment 3: Bourke Crime and Community Development

This complex systems model depicts the impact of factors such as violence and community programs on the youth of Bourke. The time scale is in months and shows the next 6 years. The model aims to show how by altering expenditure in different areas, the town of Bourke can decrease crime and increase their population involvement in community programs. This model is intended to be dynamic to allow the user to change certain variables to see changes in impact

The town of Bourke has a population of 3634 people, 903 of which are classified as youth (being 0-24 inclusive) (ABS, 2016 census).

This population starts with all youths in three differing stocks:

- 703 in Youth

- 100 in Juvenile Detention

- 100 in Rehabilitation

Assumptions:

This model makes many assumptions that would not necessarily uphold in reality.

- Only the youth of the town are committing crimes.

- All convicted youths spend 6 months in juvenile detention.

- All convicted youths must go to rehabilitation after juvenile detention and spend 2 months there.

- The risk rate impacts upon every youth committing a crime and is a broad term covering effects such as abuse.

- No gaol effect, youths do not return to town with a tendency to re- commit a crime.

- No further external factors than those given.

- There cannot be zero expenditure in any of the fields.

The stocks:

Each stock depicts a different action or place that an individual youth may find themselves in.

These stocks include:

- Youth (the youths living in Bourke, where youths are if they are not committing crimes or in community programs)

- Petty Crime (crimes committed by the youths of Bourke such as stealing)

- Juvenile Detention (where convicted youths go)

- Rehabilitation

- Community Programs

The variables:

- Community Expenditure (parameter 0.1-0.4)

- Law Enforcement Expenditure (parameter 0.1-0.6)

- Rehabilitation Expenditure (parameter 0.1-0.4)

- Risk Rate (not adjustable but alters with Law Enforcement Expenditure)

Sliders on each of the expenditure variables have been provided. These variables indicate the percentage of the criminal minimising budget for Bourke.

Note that to be realistic, one should make the three differing sliders be equal to 1, in order to show 100% of expenditure

Base Parameter Settings:

- Law Enforcement Expenditure = 0.5

- Community Expenditure = 0.25

- Rehabilitation Expenditure = 0.25

Interesting Parameter Settings:

- When Law Enforcement is at 0.45 and Community and Rehabilitation at 0.3 and 0.25 (in either order) then convicted and not-convicted values are the same. If Law Enforcement expenditure goes any lower then the number of convicted youths is less than those not-convicted and vice versa if the expenditure is increased.

- When Law Enforcement is at 0.2 and Community and Rehabilitation at 0.4 each then the increase in community programs and decrease in crime and thus detention occurs in a shorter and more rapid time frame. This shows that crime can be minimised in this model almost entirely through community initiatives.

- Alternatively, when Law Enforcement is at 0.6 and Community and Rehabilitation at 0.2 each then the increase in community programs and decrease in crime occurs over a longer time period with more incremental change.

Population Source:

http://www.censusdata.abs.gov.au/census_services/getproduct/census/2016/quickstat/LGA11150?opendocument

Bourke Community (C.Woods, 44593961)

Caitlin Woods

Simple box model for atmospheric and ocean carbon cycle, with surface and deep water, including DIC system, carbonate alkalinity, weathering, O2, and PO4 feedbacks.

LAB #9: Modern Marine 2-box Carbon Cycle

Ashley Morris

3 6 months ago

Modified from Sterman (2006) and Gene Bellinger's Assumptions IM-351 by Dr Rosemarie Sadsad UNSW See also Complex Decision Technologies IM and IM-63975

Virtual Experiments

Geoff McDonnell

3

This simulation allows you to compare different approaches to influence flow, the Flow Times and the throughput of a work process. The simulation is described in the blog post "Starting late - The Superior Scheduling Approach - How, despite being identical, one company delivers almost 10 times the value of its competitor using flow-oriented project initiation."

By adjusting the slider below you can observe the work process

without any work in process limitations (WIP Limits),
with process step specific WIP Limits* (work state WIP limits),
with Kanban Token and Replenishment Token based on the Tameflow approach (a form of drum-buffer-rope)
with Drum Buffer Rope** scheduling method.

* Well know in (agile) Kanban

** Known in the physical world of factory production

The simulation and the comparison between the different scheduling approaches can be seen here -> https://youtu.be/xXvdVkxeMMQ

The "Tameflow approach" using Kanban Token and Replenishment Token as well as the Drum Buffer Rope method take the Constraint (the weakest link of the work process) into consideration when pulling in new work items into the delivery "system".

Feel free to play around and recognize the different effects of work scheduling methods.

If you have questions or feedback get in touch via twitter @swilluda

The work flow itself

Look at the simulation as if you would look on a kanban board.

The simulation mimics a "typical" feature delivery process on portfolio level.

From left to right you find the following ten process steps.

Ideas
Selected ideas (waiting)
Initiate and pitch
Waiting for preparation
Prepare
Waiting for delivery
Deliver
Waiting for closure
Close and communicate
Closed

[Published] Simulation Starting late - The Superior Scheduling Approach (simplified version)

Stefan Willuda

4

Improvement Science as one of the clusters of interacting methods for improving health services network design and delivery using complex decision technologies IM-17952

Improvement Science

Geoff McDonnell

This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

Experiment with adjusting the initial number of moose and wolves on the island.

Day 22: More Realistic Model of Isle Royale: Predator Prey Interactions

Jacob Englert

Spring, 2020: in the midst of on-line courses, due to the pandemic of Covid-19.

With the onset of the Covid-19 coronavirus crisis, we focus on SIRD models, which might realistically model the course of the disease.

We start with an SIR model, such as that featured in the MAA model featured in

https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

Without mortality, with time measured in days, with infection rate 1/2, recovery rate 1/3, and initial infectious population I_0=1.27x10-4, we reproduce their figure

With a death rate of .005 (one two-hundredth of the infected per day), an infectivity rate of 0.5, and a recovery rate of .145 or so (takes about a week to recover), we get some pretty significant losses -- about 3.2% of the total population.

Resources:

Coronavirus: A Simple SIR (Susceptible, Infected, Recovered) with death

Andrew E Long

40

Clone of IM-806 modified to integrate AnyLogic Realworld, Model World with Van de Ven Engaged Scholarship and LAnd Use Modelling approaches. See also Complex Decision Technologies IM

Real World and Model World

Geoff McDonnell

5 months ago

Documentation

The Insight shown demonstrates how demand and supply in a real estate market can affect pricing.

Demand, Supply and Price have been represented by stocks. Each has an inflow where it has an increase in stock, and a corresponding outflow where stock is decreased.

Linking each stock and flow is a variable that changes that which it is linked to. These have been labelled appropriately. Each variable takes a decimal value and multiplies it with that it is linked to, such as the rate of demand affecting the price set as 0.001*Demand. This is to generate the loops required to show the rise and fall in price, demand and supply.

Adjustments can be made to the price, supply and demand stocks to simulate different scenarios. Price can be between 400 (400,000) and 1000 (1,000,000) in accordance to average housing prices. Demand and supply can be between 0 (0%) and 100 (100%), although having these set as realistic figures will demonstrate the simulation best.

Each simulation can be focused on how either demand and price interact over time or supply and price. These are shown in different tabs.

When the simulation is carried out, the way in which demand and supply rates affect pricing can be seen. Demand and supply are shown with price following shortly after with a slight delay, since changes in market behavior does not immediately affect prices of housing.

It should also be noted that the lines that represent each stock do not directly reflect the prices of housing in reality. Prices do not fluctuate so rapidly from 400 to near 0 like they do on the graph, however these are just representations of the interactions between each stock in a marketplace.

Zachary Chapman - 43309399 - Assignment 3 Final

Zachary Chapman

3

Peircean process approach to Causation from Menno Hulswit's article. See also Peirce IM-1376

Peircean Causation and Causality

Geoff McDonnell

Diagrams modified from article

Urban renewal and health inequality

Geoff McDonnell

WIP Summary of recent IEEE Computer graphics article (abstract) which could be applied to almost any chronic persistent health or social problem

Building useful theory on solid foundations

Geoff McDonnell

Realistic Teaching Tool

Joshua Brian Kirk

Summary of Ray Pawson's Book The Science of Evaluation: A Realist Manifesto See also lse review

Clone of The Science of Evaluation

Jim Rogers

WIP integration of dynamic and complexity insights using rubik's cube metaphor from IM-78725 folders, Dynamics in action IM-3239 System dynamics IM-1248 and others

Beyond connecting the dots

Geoff McDonnell