WIP Understanding pathways to observed effects complex causation Pathways Moving to Opportunity NYC example from Nate Osgood's big data lecture  youtube video  Feb 2017 Sydney.
WIP Understanding pathways to observed effects complex causation Pathways Moving to Opportunity NYC example from Nate Osgood's big data lecture youtube video Feb 2017 Sydney.
Simulate an impact of an asteroid of any Diameter at any given Speed!
Simulate an impact of an asteroid of any Diameter at any given Speed!
3 10 months ago
 Addition of multilevel system dynamics to the context mechanism outcome realist evaluation framework of Pawson and Tilley. See also multilevel holons  IM-3546

Addition of multilevel system dynamics to the context mechanism outcome realist evaluation framework of Pawson and Tilley. See also multilevel holons IM-3546



3 10 months ago
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).
Inference Robustness Assessment entails demonstrating that  the ways a model differs from the real world do not affect model based inferences.  From Jim Koopman's work on Infection Transmission Science esp  Biological Networks Book  Ch 13 p 453-4 and this accessible  paper  pdf
Inference Robustness Assessment entails demonstrating that the ways a model differs from the real world do not affect model based inferences. From Jim Koopman's work on Infection Transmission Science esp Biological Networks Book Ch 13 p 453-4 and this accessible paper pdf
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!) 
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

Based on a  book  chapter by Rosemarie Sadsad based on her  PhD Thesis . See also other Insights tagged Multiscale and Realist (  IM-3546  and IM-3834 are embedded here)
Based on a book chapter by Rosemarie Sadsad based on her PhD Thesis. See also other Insights tagged Multiscale and Realist ( IM-3546 and IM-3834 are embedded here)
11 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.      Linkin
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.
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.
Attempts to model in the social dynamics of returning players
Attempts to model in the social dynamics of returning players
3 6 months ago
A launchpad to tie together some ideas about Reality. See  wikipedia
A launchpad to tie together some ideas about Reality. See wikipedia
 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:
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
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
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

11 months ago