​ The Model      The model displayed depicts the interaction that the youth of Bourke has with the justice system and focuses on how factors like policing and community development affect the crime rate within this area. Bourke is a rural town that has a significant crime rate among youth. Local com
The Model

The model displayed depicts the interaction that the youth of Bourke has with the justice system and focuses on how factors like policing and community development affect the crime rate within this area. Bourke is a rural town that has a significant crime rate among youth. Local community members call for action to be taken in regards to this, meaning that steps must be taken to reduce the crime rate. This simple model explores how the amount of police and the investment of community development can have an effect on the town in regards to its issue of crime among youth.


Assumptions
  • Bourke's youth population is 1200, with 700 in town, 200 committing crimes and 300 already in jail
  • The amount of police, the expenditure on community development, and the domestic violence rate are the factors which have the potential to influence youth to commit crimes. The domestic violence rate is also influenced by the expenditure on community development.
  • Sporting clubs, interpersonal relationships between youth and police, and teaching trade skills all make up community expenditure
  • Activities relating to expenditure on community development run throughout the year, indicating that there is no delay where youth are not involved in these activities.
  • Every 6 months, only 60% of jailed youth are released. This may be for various factors such as committing crime in jail or being issued with lengthier sentences due to the severity of the crime(s) committed
  • 10% of youth who agree that domestic violence is an issue at home will commit crime
  • There is a delay of 1 month before youth go to jail for crime(s) committed. This model assumes that youth who have committed crime either return home (by decision or by not being caught) or go to jail. It also assumes that other punishments such as community service refer to returning back home.
  • The simulation takes place over a duration of 5 years (60 months)
  • Adults have little effect on the youth. Only where domestic violence is concerned do they play a factor within this model

How the Model Works

The model begins with the assumptions previously stated. Youth have the potential to commit a crime. 3 main variables influence this decision, including the amount of police, expenditure on community development, and domestic violence rate (which is influenced by the previous variable). These 3 variables are able to be adjusted using the relevant sliders with 0.5 indicating a low investment and 0.9 indicating a high investment. Police also have an influence on this decision. This variable is also able to be adjusted by a slider. Last of all, the domestic violence rate also contributes to this decision and this variable is negatively influenced by community development.

Once a youth has committed a crime they are either convicted and sent to jail or return back to town. The conviction rate is also influenced by the amount of police in town, as youth are more likely to get caught and thus jailed. Once again, the Police variable is able to be adjusted via the slider. This process takes a month.

From here, youth typically spend 6 months in jail. After this time period 60% are released while the remaining 40% remain in jail either due to lengthier sentences for more severe crimes or due to incidents within jail. The process then repeats.


Parameter Settings and Results
  • Initially there is a state of fluctuation within this model. It may be a good idea to ignore it and pay attention to how variables change over time from their initial state
  • Increasing the amount of police will raise the amount of people jailed and decrease crime
  • Increasing the community development variables from a minimal investment (i.e. set at 0.5) to a high investment (i.e. set at 0.9) will reduce both the crime rate and the conviction rate. It is worth noting that the community development variable also influences the domestic violence rate variable which also has an effect on the results
  • If only 2 of the 3 community development variables have a high investment then there is not much effect on the crime rate or jail rate. All 3 variables should be given the same level of investment to give us a desired outcome
  • The model does allow for a maximum of 40 police (as we do not want to spend more money on police than we already have in the past), as well as the maximum investment for community development. When choosing settings it may be necessary to ponder if it is financially realistic to maintain both a large number of police as well as investing heavily into community development
Based on the Market and Price simulation model in System Zoo 3, Z504. I made some more intrusive changes that make the model more realistic, or more 'economic', in another version 'simplified and improved'. 
Based on the Market and Price simulation model in System Zoo 3, Z504. I made some more intrusive changes that make the model more realistic, or more 'economic', in another version 'simplified and improved'. 
5 12 months ago
Multilevel context mechanisms and outcomes for hospital infection control
Multilevel context mechanisms and outcomes for hospital infection control
 Spring, 2020:       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-mod
Spring, 2020:

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=1.27x10-6, we recover 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:
 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.  

           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.
           This version 8B of the   CAPABILITY DEMONSTRATION   model. A net Benefit ROI has been added. The Compare results feature allows comparison of alternative intervention portfolios.  Note that the net causal interactions have been effectively captured in a very scoped and/or simplified forma
This version 8B of the CAPABILITY DEMONSTRATION model. A net Benefit ROI has been added. The Compare results feature allows comparison of alternative intervention portfolios.  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 developed and built out to: higher provenance levels -- coupled with increased factorization, rigorous causal inclusion and improved parameterization.
           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.
This simulation allows you to compare different approaches to influence flow, the Flow Times and the throughput of a work process.   By adjusting the sliders below you can    observe the work process  without  any work in process limitations ( WIP Limits ),   with process step specific WIP Limits* (
This simulation allows you to compare different approaches to influence flow, the Flow Times and the throughput of a work process.

By adjusting the sliders 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), 
  • or you may want to see the impact of the Tameflow approach with Kanban Token and Replenishment Token 
  • or see the impact of the Drum-Buffer-Rope** method. 
* Well know in (agile) Kanban
** Known in the physical world of factory production

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

You can also simulate the effects of PUSH instead of PULL. 

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" software delivery process. 

From left to right you find the following ten process steps. 
  1. Input Queue (Backlog)
  2. Selected for work (waiting for analysis or work break down)
  3. Analyse, break down and understand
  4. Waiting for development
  5. In development
  6. Waiting for review
  7. In review
  8. Waiting for deployment
  9. In deployment
  10. Done
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
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. 
  1. Ideas
  2. Selected ideas (waiting)
  3. Initiate and pitch
  4. Waiting for preparation
  5. Prepare
  6. Waiting for delivery
  7. Deliver
  8. Waiting for closure
  9. Close and communicate
  10. Closed
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.
Simple box model for atmospheric and ocean carbon cycle, with surface and deep water, including DIC system, carbonate alkalinity, weathering, O2, and PO4 feedbacks.
Simple box model for atmospheric and ocean carbon cycle, with surface and deep water, including DIC system, carbonate alkalinity, weathering, O2, and PO4 feedbacks.