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Calibration in Stella®

Updated: January 9, 2018December 15, 2017Filed under: Modeling Tips3 Comments

This is the first of a three-part series on calibration and optimization. The second part can be accessed here. The third part can be accessed here.

Several years ago, I wrote a post that showed how to calibrate Stella and iThink® models using PEST, a third-party calibration tool (Using PEST to Calibrate Models). Starting with version 1.5, Stella Professional and Stella Architect have optimization built in. Since calibration is a special case of optimization, we can also use this feature to calibrate models (but stay tuned: calibration will be added as its own feature in the not too distant future). Let’s review the problem that was calibrated using PEST in that post and then configure the optimization to calibrate it within Stella.

The Model

The model is a simple SIR model first presented in my blog post Limits to Growth and shown below (this version with all the optimization settings can be downloaded here). There are two parameters: infection rate and recovery rate. The Population size and the initial number of Infected people are also model parameters, but do not need to be calibrated (presumably, we know the population and the data tells us the initial number of infected people).

image

The Data Set

We will calibrate this model to the data set of the number of weekly deaths caused by the Hong Kong flu in New York City over the winter of 1968-1969 (below).

HK Flu Deaths

Here, I am using the number of deaths as a proxy for the number of people infected, which we do not know. This is reasonable because the number of deaths is directly proportional to the number of infected individuals. For this specific outbreak, less than 0.5% of infected people died, so we could multiply these numbers by 200 (1/0.005) to approximate the number of people infected. As I did not do this for the PEST calibration and wish to compare the results, I will not do this here either.

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Modeling Tips
  • calibration
  • data
  • h1n1
  • optimization
  • Stella
3 Comments

Drifting Goals

Updated: March 11, 2016March 9, 2016Filed under: Systems Thinking1 Comment

The Drifting Goals Archetype applies to situations where short-term solutions lead to the deterioration of long-term goals.  Also known as Eroding Goals, this is a special case of Shifting the Burden.  This Systems Archetype was formally identified in Appendix 2 of The Fifth Discipline by Peter Senge (1990).  The Causal Loop Diagram (CLD) is shown below.

image

When a gap exists between the current state of the system and our goal (or desired state), we take action proportional to the gap to move the system state toward our goal.  There is always a delay between the action we take and the effect on the system.  Simultaneously, pressure is exerted to instead adjust the goal to close the gap.  Adjusting the goal leads to a situation where the goal floats independently of any standard.  It often leads to goals being reduced, or eroded.

Classic examples of drifting goals include:

  • Reducing quality targets to improve measured quality performance (relative to goal) or to improve delivery schedule
  • Reducing quality of ingredients or parts below company standards to improve profits
  • Increasing time to deliver to match existing capacity and save on overtime
  • Reducing a new product’s feature set to meet deadlines; this works the other way also, i.e., extending the deadline to include all of the features
  • Reducing pollution targets when reduction implementation costs are too high
  • Increasing budget deficit limits rather than decreasing spending (or increasing taxes)
  • Adapting to unacceptable social circumstances rather than leave that environment
  • Reducing entrance requirements because not enough applicants meet them
  • Reducing level of patient care below recommended minimum due to understaffing
  • Reducing margin to spur sales and meet revenue targets
  • Lowering your own expectations in life, leading to lower personal success

Note that in many of these cases, there are competing goals and one is held more sacred than another.  Drifting Goals is an insidious process that seeks to lower your standards to the level of the current state of the system.  Stay aware of not just how the state of the system adjusts to your goal, but also of how your goal varies over time.  Changing a goal should be a conscious decision that does not undermine other objectives.

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Systems Thinking
  • archetypes
  • Causal Loop
  • CLD
  • Systems Thinking
1 Comment

System Dynamics Conference in Cambridge, MA

Updated: August 10, 2015August 7, 2015Filed under: News & Announcements15 Comments

The 33rd International System Dynamics Conference (ISDC) was an inspiring event and we met many new faces as well as many longtime friends! If you were not able to attend the ISDC, stop by our booth, or go to the workshops on Thursday, we documented some of the highlights for you. isee systems was once …

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