Insight diagram
Simulation d'un MRU d'un corps qui avance avec une régulation de vitesse réaliste.
Autre version
serie 08a ex4 Une regulation de vitesse plus realiste V2
Insight diagram

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.  

Social Media Virality
Insight diagram
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
https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

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:
  1. http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
  2. https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model
Clone of Coronavirus: A Simple SIR (Susceptible, Infected, Recovered) with death
Insight diagram
EK map - Motivation
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The ecosystem of a game park can be made more realistic by introducing financial allocation for active game park quality upgrades, and to reduce the negative results of a high tourist influx. The total amount of money collected is a variable that is dependent on the number of tourists coming to a park until the number levels off at a state of maximum tourist congestion
A model of management decisions with cash reinvestment
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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.
AI
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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.
Version 6A NULL PRACTICE Student-Home-Teachers-Classroom
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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.
Version 8B: Calibrated Student-Home-Teachers-Classroom-LEA-Spending
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Model of the autonomi network economics
autonomi
Insight diagram
Summary of Ray Pawson's Book The Science of Evaluation: A Realist Manifesto See also lse review
Clone of The Science of Evaluation
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Clone of PlayCast Analytics
11 months ago
Insight diagram
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.
Version 6A: Calibrated Student-Home-Teachers-Classroom
3 8 months ago
Insight diagram
This model demonstrates sustainable recycling and the effects it has on the environment as well as us. We modelled this using realistic statistics and estimates from gridwatch.ca and the Ontario Baseline and Waste & Recycling Report (2023). 

[Purple]: Metal demands on a region and the associated environmental and economic factors of production and recycling.

[Pink]: Demand of total residential household and business waste and energy demands on the system.

[Green]: Physical waste produced by human activity in the region.

[Teal]: The outflow of energy produced through waste recycling and its impact of energy production and demand in the region. The Durham-York Energy Center (DYEC) is a facility that combusts garbage into energy which is highlighted in teal, which accumulates with energy produced.

[Orange]: Total energy produced through all means of power generation including modelling of the impact that recycling waste has.

[Yellow]: Carbon emissions of energy generation from energy production methods. (Excluding Wind & Hydro)

Overall, this model examines and compares waste accumulation to energy production and the release of emissions.
scenario 1
Insight diagram

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.  

Clone of Social Media Virality
Insight diagram

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.  

Social Media Virality3
Insight diagram
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
Kanban Board Simulation - WIP Limit, Tameflow Kanban Token and Drum-Buffer-Rope
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Overview of Part E Ch 20 to 24 of Mitchell Wray and Watts Textbook see IM-164967 for book overview
Economic policy in an open economy
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This model simulates the basic dynamics of a water reservoir, including the impact of rainfall, community water consumption, conservation efforts, and evaporation. The model shows how the reservoir’s water level changes over time based on natural inflows and human , nature water use.
Water Reservoir System (Basic)
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Wetland governance_S&F revision_Oct 15
15 2 weeks ago
Insight diagram
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.
Version 6B: Calibrated Student-Home-Teachers-Classroom
Insight diagram

Rich picture version of Tanner's Clinical Judgment Model, with the addition of clinical reasoning cycle concepts from T Levett-Jones et al Nurse Education Today 30 (2010) 515-520

Digital Literacy & Electronic Healthcare Record Integration
Insight diagram
This model analyzes the growth and dynamics of Oshawa’s population using a logistic approach. Starting with an initial population of 170,000 and an increased carrying capacity of 180,000, it evaluates how the addition of new neighbourhoods, planned to accommodate an extra 10,000 residents over the next 10-15 years (or whatever time period) affects population changes. Key factors include the Oshawa Residents Death/Emigration Rate of 0.8% (realistic percent approximation), accounting for natural deaths and emigration, and the Oshawa Residents Birth/Immigration Rate of 2.4% (also a realistic percent approximation), reflecting new residents through births and immigration. The model tracks the net population change, providing insights into how Oshawa's population might grow or stabilize as it approaches its new carrying capacity!
Logistic Model of Oshawa's Population Growth with Increased Residential Carrying Capacity
Insight diagram
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
[Published] Effect of starting work too soon
Insight diagram
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
https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

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:
  1. http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
  2. https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model
SD India