Attempts to model in the social dynamics of  Pavilion host aquisition
Attempts to model in the social dynamics of  Pavilion host aquisition
WIP integration of dynamic and complexity insights using rubik's cube metaphor from Pop Health Book  insight  folders,  and others linked in notes
WIP integration of dynamic and complexity insights using rubik's cube metaphor from Pop Health Book insight folders,  and others linked in notes
Summary of  Ch 22 of Mitchell Wray and Watts Textbook see  IM-164967  for book overview
Summary of  Ch 22 of Mitchell Wray and Watts Textbook see IM-164967 for book overview
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.
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.
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.
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.
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.  We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale websi
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

A decent match to the data is made with
Wolf Death Rate = 0.15
Wolf Birth Rate Factor = 0.0203
Moose Death Rate Factor = 1.08
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is MBR*M*(1-M/K)
Moose death flow is MDRF*Sqrt(M*W)
Wolf birth flow is WBRF*Sqrt(M*W)
Wolf death flow is WDR*W

A combination of qualitative and quantitative methods for implementing a systems approach, including virtual intervention experiments using computer simulation models. See also  Complex Decision Technologies IM  Interventions and leverage points added in  IM-1400  (complex!) 
A combination of qualitative and quantitative methods for implementing a systems approach, including virtual intervention experiments using computer simulation models. See also Complex Decision Technologies IM
Interventions and leverage points added in IM-1400 (complex!) 
  About
the Model  

 This
model is designed to simulate the youth population in Bourke, specifically
focusing on the number of criminals and incarcerated dependent on a few key
variables. 

 Within the model, a young person living in Bourke can be classified as being in any of five states:  Young C

About the Model

This model is designed to simulate the youth population in Bourke, specifically focusing on the number of criminals and incarcerated dependent on a few key variables.

Within the model, a young person living in Bourke can be classified as being in any of five states:

Young Community Member: The portion of the youth population that is not committing crime and will not commit crime in the future. Essentially the well behaved youths. A percentage of these youths will become alienated and at risk.

Alienated and At Risk Youths: The youths of Bourke that are on the path of becoming criminals, this could be caused by disruptive home lives, alcohol and drug problems, and peer pressure, among other things.

Criminal: The youths of Bourke who are committing crimes. Of these criminals a percentage will be caught and convicted and become imprisoned, while the remainder will either go back to being at risk and commit more crimes, or change their behaviour and go back to being a behaving community member.

Imprisoned: The youths of Bourke who are currently serving time in a juvenile detention centre. Half of the imprisoned are released every period at a delay of 6 months.

Released: Those youths that have been released from a detention centre. All released youths either rehabilitate and go back to being a community member or are likely to re-offend and become an alienated and at risk youth.

The variables used in the model are:

Police- This determines the police expenditure in Bourke, which relates to the number of police officers, the investment in surveillance methods and investment in criminal investigations. The level of expenditure effects how many youths are becoming criminals and how many are being caught. An increase in police expenditure causes an increase in imprisoned youths and a decrease in criminals.

Community Engagement Programs- The level of investment in community engagement programs that are targeted to keep youths in Bourke from becoming criminals. The programs include sporting facilities and clubs, educational seminars, mentoring programs and driving lessons. Increasing the expenditure in community engagement programs causes more young community members and less criminals and at risk youths.

Community Service Programs- The level of investment in community service programs that are provided for youths released from juvenile detention to help them rehabilitate and reintegrate back into the community. An increase in community service expenditure leads to more released prisoners going back into the community, rather than continuing to be at risk. Since community service programs are giving back to the community, the model also shows that an increase in expenditure causes a decrease in the amount of at risk youths.

All three of these variables are adjustable. The number of variables has been kept at three in order to ensure the simulation runs smoothly at all times without complicated outputs, limitations have also been set on how the variables can be adjusted as the simulation does not act the same out of these boundaries.

Key Assumptions:

The model does not account for the youths’ memory or learning.

There is no differentiation in the type of criminals and the sentences they serve. Realistically, not all crimes would justify juvenile detention and some crimes would actually have a longer than six-month sentence.

The constants within in the calculations of the model have been chosen arbitrarily and should be adjusted based on actual Bourke population data if this model were to be a realistic representation of Bourke’s population.

The model assumes that there are no other factors affecting youth crime and imprisonment in Bourke.

There are 1500 youths in Bourke. At the beginning of the simulation:

Young Community Member = 700

Alienated and At Risk Youth = 300

Criminal = 300

Imprisoned = 200

Noteworthy observations:

Raising Police expenditure has a very minimal effect on the number of at risk youths. This can be clearly seen by raising Police expenditure to the maximum of twenty and leaving the other two variables at a minimum. The number of Alienated and at Risk Youths is significantly higher than the other states.

Leaving Police expenditure at the minimum of one and increasing community development programs and community service programs to their maximum values shows that, in this model, crime can be decreased to nearly zero through community initiatives alone.

Leaving all the variables at the minimum position results in a relatively large amount of crime, a very low amount of imprisoned youth, and a very large proportion of the population alienated and at risk.

An ideal and more realistic simulation can be found by using the settings: Police = 12, Community Engagement Programs = 14, Community Service Programs = 10. This results in a large proportion of the population being young community members and relatively low amounts of criminals and imprisoned.



Stephen P Dunn 2010 Book summary including Technostructure MMT PCT critical realist and managing perceptions links
Stephen P Dunn 2010 Book summary including Technostructure MMT PCT critical realist and managing perceptions links
  MODEL EXPLANATION:  This model simulates possible crime patterns
among the youth population of Bourke, where levels of alienation, policing
and community engagement expenditure can be manipulated. Here the youth in Bourke have a minimum percentage of interest to participate in community activities

MODEL EXPLANATION:

This model simulates possible crime patterns among the youth population of Bourke, where levels of alienation, policing and community engagement expenditure can be manipulated. Here the youth in Bourke have a minimum percentage of interest to participate in community activities in which the government aims to improve their lifestyle and therefore reduce the rate of criminal activity. ASSUMPTIONS:There are 1500 youths of Bourke in the population susceptible to committing crime and simulations of criminal tendencies are only based the factors presented, no external influences.
VARIABLES:“Alienation” includes any factors that can increase the likelihood of youths to commit crime such as exposure to domestic violence, household income, education level, and family background‘Community engagement Expenditure’ is the total monies budgeted into community activities to develop youths in and out of Juvenile detention‘Policing’ is the amount of police placed onto patrol in the town of Bourke to reinforce safety and that the law is abided by. STOCKS:Conviction rate is set to 60%A juvenile detention sentence for convicted criminals is set to 3 monthsThe top 30% of the most severe offenders are sent to rehabilitation for 3 months, to which they return to Bourke, assumingly in a better state and less likely to repeat a petty crimeCommunity activities are set to last for 3 months to align with the seasons: these could be sporting clubs or youth groupsCommunity participants have a 20% chance of being disengaged as it may not align with their interestsInvestments into policing are felt immediately& community engagement expenditure has a delay of 3 months
INTERESTING FINDS:1.    Alienation set to max (0.2), policing and community engagement set to minimum shows a simulation whereby all criminals are in town rather than being expedited and placed into juvenile detention, even after a base value of 200 youths placed into juvenile detention – this shows that budget is required to control the overwhelming number of criminal youths as they overrun Bourke2.    Set community activity to 0.01, policing to max & Alienation to max. A lack of community activity can produce high disengagement amongst youths regardless of police enforcement to the town of Bourke that has a high criminal rate. Juvenile detention only lasts for so long and not all youths can be rehabilitated, so they are released back into Bourke with chances of re-committing crime. 3.    Alienation plays a major role in affecting youths to consider committing crime. To keep criminal activity to a minimum, ideally the maximum rates of budget in policing and community engagement within youths highly at risk of committing crime should be pushed. Realistically, budget is a sensitive case within a small town and may not be practical. 4. Set policing to 0.25, community engagement to 0.2 & alienation to 0.04. Moderate expenditure to community activities and policing can produce high engagement rates and improved youths in the town of Bourke.



A simple three way predator prey model (Polar bears; Seals; Fish) including change in fish death rates at a set time point due to an external factor (e.g. human fishing).    Coefficients assigned to make model work rather than being based on any evidence.       Model created for descriptive basis; n
A simple three way predator prey model (Polar bears; Seals; Fish) including change in fish death rates at a set time point due to an external factor (e.g. human fishing).

Coefficients assigned to make model work rather than being based on any evidence.  

Model created for descriptive basis; not realistic modelling.


The model is designed to provide a general understanding of the wear and tear on roads or a community's circulation system as a result of vehicle traffic generated by development within and outside of a community. It is not based on realistic assumptions regarding those impacts, it simply attempts t
The model is designed to provide a general understanding of the wear and tear on roads or a community's circulation system as a result of vehicle traffic generated by development within and outside of a community. It is not based on realistic assumptions regarding those impacts, it simply attempts to convey the flow of influence.

The imaginary city has a set area of roads measured in linear yards (width of roads is ignored) and an assumed number of vehicles on those roads set at 30,000 (per day). With those assumptions the wear and tear requiring repair is .02 or 2% Vehicle wear based on the 30,000 per year. There is also a calculated replacement cost of an additional 3% plus through vehicle wear or 5% per year.  An increase in vehicles increases this vehicle wear impact exponentially. The model assumes that there will not be less than 30,000 vehicles.

Expenditures for repair or replacement are set to balance out on an as needed based on 30,000 vehicles. An minimum additional 50 cars from external sources is then assumed. Adding New Homes and/or New Businesses places an even greater burden on the circulation system. 

The model does not consider additional funding. This will be added as a political factor but would need to consider the possibility of decreasing funding for other purposes.

Future additions to the model will include an inflation factor. Unfunded road work will get increasingly more expensive over time. Also a diminished revenue factor. A lack of capacity of the community's roads could likely result in a diminishment of the community's business sector thus reducing sales and property taxes and municipal revenue to expend on the roads. 
There has been an ongoing effort to find a means of making systems thinking accessible and readily adopted by others not familiar with systems thinking. One line of thinking places a good deal of the blame on systems thinkers themselves, the problem is that they have not found a good enough method o
There has been an ongoing effort to find a means of making systems thinking accessible and readily adopted by others not familiar with systems thinking. One line of thinking places a good deal of the blame on systems thinkers themselves, the problem is that they have not found a good enough method of explaining it and its benefits yet. 

Another possibility though is the extent to which those who are to be helped feel besieged by the situation in which they find themselves making them extremely wary about trying something new. 

This model is not realistic, at least it is hoped that there isn't anyplace where things are this bad. Different communities will be better or worse off in different categories and some will be succeeding in all areas. Those are the communities we need to learn from.

More explanation can be found under the information icons associated with each of the elements.
Summary of 2017 IEEE Computer graphics article  (abstract)  which could be applied to almost any chronic persistent health or social problem
Summary of 2017 IEEE Computer graphics article (abstract) which could be applied to almost any chronic persistent health or social problem
In this model we seek to show how Formula 1 can bring there Co2 emissions down to zero by 2030 (six years from now).
In this model we seek to show how Formula 1 can bring there Co2 emissions down to zero by 2030 (six years from now).
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.
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.
 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
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

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:
Summary mostly of Cheryl Misak's 2004  Book  Truth and the End of Inquiry: A Peircean Account of Truth See also broader history of  Pragmatism insight , mostly  from Cheryl Misak's other works and reviews
Summary mostly of Cheryl Misak's 2004 Book Truth and the End of Inquiry: A Peircean Account of Truth
See also broader history of Pragmatism insight, mostly  from Cheryl Misak's other works and reviews

10 months ago
This simulation makes the negative effects of starting work too soon visible. You can play around with the parameters.    Find the full story behind this simulation  here .      If you have questions or feedback get in touch via  @swilluda
This simulation makes the negative effects of starting work too soon visible. You can play around with the parameters.

Find the full story behind this simulation here

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