This model demonstrates the balance between the resources an AI system consumes and the tangible operational benefits it produces, particularly through task completion over course of 1 year. By capturing resource costs in terms of  Computational Power ,  Energy Consumption ,  Data Storage , and  AI

This model demonstrates the balance between the resources an AI system consumes and the tangible operational benefits it produces, particularly through task completion over course of 1 year. By capturing resource costs in terms of Computational Power, Energy Consumption, Data Storage, and AI Efficiency, the model illustrates how these core resources support AI operations and how they are allocated to maximize effective task processing.

At the heart of the model is the tradeoff between increasing AI efficiency and the corresponding rise in resource demands. Each stock accumulates according to inflows that represent resource additions, like new hardware investments, and depletes based on outflows that simulate ongoing consumption and task execution requirements. Through these dynamic flows, the model provides a framework to explore the impact of resource allocation strategies on AI effectiveness, helping identify how varying levels of energy, compute power, and data storage affect AI’s task completion over time.

By focusing on time-sensitive resource usage, this model highlights AI’s operational sustainability, showcasing which strategies best balance energy, data, and computational power to achieve a steady output of completed tasks. It allows for experimenting with different resource levels to understand the tipping points at which increasing costs may yield diminishing returns, providing insight into sustainable scaling and efficient task management.

This model was designed and directed with the assistance of ChatGPT to visualize resource allocation and efficiency dynamics in AI operations.

11 months ago
 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:
 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:
Based on the Market and Price simulation model in System Zoo 3, Z504. In this model the profit calculations were not realistic. They were based on the per unit profit, which does not take items not sold into account. Also the model was not very clear on profit since it was included in the total prod
Based on the Market and Price simulation model in System Zoo 3, Z504. In this model the profit calculations were not realistic. They were based on the per unit profit, which does not take items not sold into account. Also the model was not very clear on profit since it was included in the total production costs and consequently in the unit costs and subsequently profit was calculated by subtracting unit costs of the market price. Thus profit had a double layer which does not make the model better accessible. I have tried to remedy both in this simplified version.
12 months ago
  MODEL EXPLANATION:  This model simulates possible crime patterns
among the youth population of Bourke, where levels of alienation, policing
and community engagement expenditure can be manipulated. Here the youth in Bourke have a minimum percentage of interest to participate in community activities

MODEL EXPLANATION:

This model simulates possible crime patterns among the youth population of Bourke, where levels of alienation, policing and community engagement expenditure can be manipulated. Here the youth in Bourke have a minimum percentage of interest to participate in community activities in which the government aims to improve their lifestyle and therefore reduce the rate of criminal activity. ASSUMPTIONS:There are 1500 youths of Bourke in the population susceptible to committing crime and simulations of criminal tendencies are only based the factors presented, no external influences.
VARIABLES:“Alienation” includes any factors that can increase the likelihood of youths to commit crime such as exposure to domestic violence, household income, education level, and family background‘Community engagement Expenditure’ is the total monies budgeted into community activities to develop youths in and out of Juvenile detention‘Policing’ is the amount of police placed onto patrol in the town of Bourke to reinforce safety and that the law is abided by. STOCKS:Conviction rate is set to 60%A juvenile detention sentence for convicted criminals is set to 3 monthsThe top 30% of the most severe offenders are sent to rehabilitation for 3 months, to which they return to Bourke, assumingly in a better state and less likely to repeat a petty crimeCommunity activities are set to last for 3 months to align with the seasons: these could be sporting clubs or youth groupsCommunity participants have a 20% chance of being disengaged as it may not align with their interestsInvestments into policing are felt immediately& community engagement expenditure has a delay of 3 months
INTERESTING FINDS:1.    Alienation set to max (0.2), policing and community engagement set to minimum shows a simulation whereby all criminals are in town rather than being expedited and placed into juvenile detention, even after a base value of 200 youths placed into juvenile detention – this shows that budget is required to control the overwhelming number of criminal youths as they overrun Bourke2.    Set community activity to 0.01, policing to max & Alienation to max. A lack of community activity can produce high disengagement amongst youths regardless of police enforcement to the town of Bourke that has a high criminal rate. Juvenile detention only lasts for so long and not all youths can be rehabilitated, so they are released back into Bourke with chances of re-committing crime. 3.    Alienation plays a major role in affecting youths to consider committing crime. To keep criminal activity to a minimum, ideally the maximum rates of budget in policing and community engagement within youths highly at risk of committing crime should be pushed. Realistically, budget is a sensitive case within a small town and may not be practical. 4. Set policing to 0.25, community engagement to 0.2 & alienation to 0.04. Moderate expenditure to community activities and policing can produce high engagement rates and improved youths in the town of Bourke.



 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:
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