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).
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. 
 ==edited by Prasiantoro Tusono and Rio Swarawan Putra==     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 a
==edited by Prasiantoro Tusono and Rio Swarawan Putra==

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:
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.
WIP Based on Gene's Enabling a Better Tomorrow Map  IM-2879  this is a Specific Health Care version based on the archived  Systemswiki Health Care  material. The focus is on Models and Simulation, with videos and discussion in the fullness of time. I am following Gene's   Adventures in Wonderland  f
WIP Based on Gene's Enabling a Better Tomorrow Map IM-2879 this is a Specific Health Care version based on the archived Systemswiki Health Care material. The focus is on Models and Simulation, with videos and discussion in the fullness of time. I am following Gene's  Adventures in Wonderland framework. Revised for More Complex AnyLogic transition at IM-57331
 An overview of Thomas A Goudge's Book on The Thought of CS Peirce Dover NY 1950 and Thomas Knight's Book Charles Peirce NY 1965. See also  causality insight

An overview of Thomas A Goudge's Book on The Thought of CS Peirce Dover NY 1950 and Thomas Knight's Book Charles Peirce NY 1965. See also causality insight

           This version of the   CAPABILITY DEMONSTRATION   model has been further calibrated (additional calibration phases will occur as better standardized data becomes available).  Note that the net causal interactions have been effectively captured in a very scoped and/or simplified format.  Re
This version of the CAPABILITY DEMONSTRATION model has been further calibrated (additional calibration phases will occur as better standardized data becomes available).  Note that the net causal interactions have been effectively captured in a very scoped and/or simplified format.  Relative magnitudes and durations of impact remain in need of further data & adjustment (calibration). In the interests of maintaining steady progress and respecting budget & time constraints, significant simplifying assumptions have been made: assumptions that mitigate both completeness & accuracy of the outputs.  This model meets the criteria for a Capability demonstration model, but should not be taken as complete or realistic in terms of specific magnitudes of effect or sufficient build out of causal dynamics.  Rather, the model demonstrates the interplay of a minimum set of causal forces on a net student progress construct -- as informed and extrapolated from the non-causal research literature.
Provided further interest and funding, this  basic capability model may further de-abstracted and built out to: higher provenance levels -- coupled with increased factorization, rigorous causal inclusion and improved parameterization.
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
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 as one of the clusters of interacting methods for improving health services network design and delivery using complex decision technologies IM-17952
This model simulates the tradeoff between the total costs and total benefits of using AI. The model shows the investment rate in comparison to the effectiveness and efficiency rate of the AI and we can visualize this relationship with our graph to see the cost and benefits of AI.
This model simulates the tradeoff between the total costs and total benefits of using AI. The model shows the investment rate in comparison to the effectiveness and efficiency rate of the AI and we can visualize this relationship with our graph to see the cost and benefits of AI.
 We start with an SEIR social virality model and adapt it to model social media adoption of Playcast Hosts.  *Note that this model does not attempt to model WOM emergent virality.  

We start with an SEIR social virality model and adapt it to model social media adoption of Playcast Hosts.  *Note that this model does not attempt to model WOM emergent virality.  

21 8 months ago
Summary of the History of Pragmatism mostly based on Cheryl Misak's Books and reviews   See also  Insight  Peircean Truth and the end of Inquiry
Summary of the History of Pragmatism mostly based on Cheryl Misak's Books and reviews   See also Insight Peircean Truth and the end of Inquiry
10 months ago
Hoffman and Klein IEEE Intelligent systems 2017-18 series of articles on decision making and computing, including macrocognition 1  theoretical foundations  abstract  2  empirical foundations  abstract  3  causal landscape s abstract  4  deep n ets abstract   See also 2018 Gary Klein  podcast  and 
Hoffman and Klein IEEE Intelligent systems 2017-18 series of articles on decision making and computing, including macrocognition
causal landscapes abstract
deep nets abstract 
See also 2018 Gary Klein podcast and the process of explaining insight
  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.