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.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
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 logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

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.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
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 logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

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.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
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 logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

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.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
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 logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

  Details:   <!--[if !supportLists]-->-         
<!--[endif]-->This
model shows the effect of ‘reinvestment program ‘or the expenditure on policing
and community development affects the cycles of petty-crime and youth
detention, and domestic violence and jail.  More details:   <!--[if

Details:

<!--[if !supportLists]-->-          <!--[endif]-->This model shows the effect of ‘reinvestment program ‘or the expenditure on policing and community development affects the cycles of petty-crime and youth detention, and domestic violence and jail.

More details:

<!--[if !supportLists]-->-          <!--[endif]--> Bourke is a town of 3000 people in the North West of New South Wales, about 750Km from Sydney. See the map: https://goo.gl/maps/VgNqgMNzJ7H2. It’s nowhere and there’s not much to do there if you’re young. So, a lot of kids get into mischief, and a lot of adult’s drink. Sometimes they’re violent.

 

<!--[if !supportLists]-->-          <!--[endif]-->http://www.justreinvest.org.au/justice-reinvestment-in-bourke/

Assumption:

<!--[if !supportLists]-->·       <!--[endif]-->Bourke Funding consist of Law enforcement funding and Community Development funding only

<!--[if !supportLists]-->o   <!--[endif]-->Bourke budget only has $400,000

<!--[if !supportLists]-->·       <!--[endif]-->Juvenile detention stay last for 6 months

<!--[if !supportLists]-->·       <!--[endif]-->There is only 2 options as a Youth, commit petty crime or engage in Youth development programs

<!--[if !supportLists]-->·       <!--[endif]-->1 unit of Police, Juvenile and Educational program HR and Equipment is = 0.25

<!--[if !supportLists]-->o   <!--[endif]-->1 unit increase results in an 0.25 effectiveness increase

<!--[if !supportLists]-->·       <!--[endif]-->Sport clubs, educational programs and social programs are comprised into Youth Development Program as 1 stock.

<!--[if !supportLists]-->·       <!--[endif]-->Juvenile support relies on encouraging youth who are in detention centers to join youth development programs, if not they will reoffend.

Stocks:

<!--[if !supportLists]-->o   <!--[endif]-->Home

<!--[if !supportLists]-->o   <!--[endif]-->Youth Development program

<!--[if !supportLists]-->o   <!--[endif]-->Discharged

<!--[if !supportLists]-->o   <!--[endif]-->Juvenile detention center

<!--[if !supportLists]-->o   <!--[endif]-->Petty Crime

Variable:

<!--[if !supportLists]-->·       <!--[endif]-->Reinvestment Allocation – ranges from 0 – 1 , law enforcement investment allocation is 1 – reinvestment allocation. Slide the slider through 0 to 1 to change the reinvestment allocation by 10% l

<!--[if !supportLists]-->·       <!--[endif]-->Bourke funding budget is fixed to make it seem more realistic (imagine employing a whole army of teachers or police, it wouldn’t make sense)

<!--[if !supportLists]-->·       <!--[endif]-->Youth Population varies , from 1000 to 10,000 for realism along with its time period (4 years). Slider the the slider to increase or decrease the population by 1,000s

Juvenile support effectiveness rate, Youth development program effectiveness rate, conviction rate, Police HR/ equipment, Juvenile Support HR/ equipment, Youth Development program HR/ equipment

Interrelationship and reinforcing loops

<!--[if !supportLists]-->·       <!--[endif]-->The youth population starts as as Neutral (Home) then leans towards alienation and connectedness

<!--[if !supportLists]-->·       <!--[endif]-->Alienation Reinforcing Loop -  Alienation has Conviction rate as a factor as conviction rate increase Alienation increase. This is because as youths get arrested, meaning they’ll have to stay in Detention centers, their friends are more likely to follow on due to them getting ‘bored’.

<!--[if !supportLists]-->·       <!--[endif]-->Connectedness Reinforcing Loop - The opposite exist with Connectedness, as educational program effectiveness increase so as Connectedness. This follows onto the same assumption that youth will always follow peer pressure. The more friends they have in the program, the more likely they will join aswell.

 

Analysis:

<!--[if !supportLists]-->1.       <!--[endif]-->Which loop is the youth in?

<!--[if !supportLists]-->·       <!--[endif]-->Once the allocation slider is used with its minimum or maximum value, the loop at which majority of the youth population is ‘stuck in’ becomes obvious. E.g. Once allocation = 1, the entire youth is stuck between educational program and their home, showing the effectiveness of community development funding. On the other hand, once allocation = 0, the entire youth loops around from doing Petty Crimes, spending their time in Juvenile detention centers, then getting discharged to only commit petty crimes again.

<!--[if !supportLists]-->2.       <!--[endif]-->Alienation vs. Connectedness

<!--[if !supportLists]-->·       <!--[endif]-->Set the allocation slider on 0.8, The massive difference between the youth of population feeling connected with their community and youth being alienated can be seen. The increase in Reinvestment, the increase in connectedness. Try the extremes as well, 100% reinvestment funding results in 0 Alienation rate.

<!--[if !supportLists]-->3.       <!--[endif]--> What is the Youth Engaged in ? Educational Programs or Petty Crime ?

<!--[if !supportLists]-->·       <!--[endif]-->Leaving the slider on 0.8, it can be seen that the there are more youth engaged into educational programs than petty crime. This shows that reinvestment and petty crime has a negative relationship .

<!--[if !supportLists]-->4.       <!--[endif]-->More police = safer ?

<!--[if !supportLists]-->-          <!--[endif]-->Set the slider on 0.1 , it can be seen that Conviction which has police as a factor is positively correlated to Crime. This means that an increase in conviction rate is equivalent to more youth being alienated and committing crime. Therefore, more police less safer.

 Have fun! 

 

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.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
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 logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

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.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
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 logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

Navigation to aspects of systems relevant to applying the methods to health care; adapted from John Barton's representation of a system slide
Navigation to aspects of systems relevant to applying the methods to health care; adapted from John Barton's representation of a system slide
11 months ago
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.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
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 logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

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
Builds on earlier model for the Thinking Systemically segment of STCP. Develops EaBT approach further based on NM example. Describes innovation in a service delivery/consulting organisation whose clients expect/demand "innovation" due to their own lack of ability to improve.
Builds on earlier model for the Thinking Systemically segment of STCP. Develops EaBT approach further based on NM example.
Describes innovation in a service delivery/consulting organisation whose clients expect/demand "innovation" due to their own lack of ability to improve.

           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.
 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.  

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

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