Tag Archives: prediction

Richard Feynman Explains the PDSA Cycle

Ok, really Richard Feynman Explains the scientific method. But his thoughts make the similarity between the PDSA cycle and the scientific method obvious.

1) Plan, hypothesis.
You make a guess about a theory (in using the PDSA cycle this step is often missed, while in the scientific method this is of the highest priority). You make a prediction based on that theory.

2) Do the experiment

3) Study the results

If the results disprove the theory you were wrong. If they results don’t disprove the theory you may have a useful theory (it can also be that your theory is still wrong, but this experiment happened not to provide results that disprove it).

Step 4, Act, only exists for PDSA. In science the aim is to learn and confirm laws. While the PDSA cycle has an aim to learn and adopt methods that achieve the desired results.

Richard Feynman: “If it disagrees with experiment it is wrong, in that simple statement is the key to science, it doesn’t make any difference how beautiful your guess is, it doesn’t make a difference how smart you are (who made the guess), or what his name is, if it disagrees with experiment it is wrong.”

Actually far to often “PDSA” fails to adopt this understanding. Instead it become PA: no study of the results, just implement and we all already agree it is going to work so don’t bother wasting time testing that it actually does. Some organization do remember to study results of the pilot experiments but then forget to study the results when the new ideas are adopted on a broader scale.

Related: Does the Data Deluge Make the Scientific Method Obsolete?Video of Young Richard Feynman Talking About Scientific ThinkingHow to Use of the PDSA Improvement Cycle Most EffectivelyUsing Design of Experiments

All Models Are Wrong But Some Are Useful

“All Models Are Wrong But Some Are Useful” -George Box

A great quote. Here is the source: George E.P. Box, Robustness in the strategy of scientific model building, page 202 of Robustness in Statistics, R.L. Launer and G.N. Wilkinson, Editors. 1979.

Related: Dangers of Forgetting the Proxy Nature of Dataarticles by George BoxQuotes by Dr. W. Edwards Deming

Management is Prediction

re: post on prediction [link broken, so removed] on the Deming Electronic Network:

Petter Ogland wrote:

…that intelligence more or less boils down to updating a predictive model of the world. As far as I can see, this is the C.I. Lewis epistemology that Shewhart and Deming based their philosophy upon.

…but is there any kind of operational definition for ‘prediction’ that would explain what Deming means when he uses this word in various contexts?

I think your first point is correct, which I see as: learning by predicting, then looking at the result and then adjusting understanding to this new information is very powerful.

I believe Deming’s thoughts about prediction are most effectively put into action using the PDSA cycle. Specifically, you must predict the results in the planning phase (prior to piloting improvements). I find that this is rarely done. I don’t think the form of that prediction is critical (narrative with loose numerical guesses, precise numerical prediction…). The critical issue is making the prediction, then comparing the results to that prediction and then figuring out how your original understanding can be improved based on the new data.
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Theory of Knowledge

Deming’s Management System, as expressed in his book: The New Economics has four interdependent parts:

  • Appreciation for a System (systems thinking)
  • Knowledge about Variation (see: variation definition)
  • Theory of Knowledge
  • Psychology (the human element of management systems)

Quotes on the Theory of Knowledge portion from The New Economics by W. Edwards Deming.

  • Management is Prediction. The theory of knowledge that management in any form is prediction” (Page 101)
  • Knowledge is built upon theory… Rational prediction requires theory and build knowledge through systematic revision and comparison of theory based on comparison of prediction with observation.” (Page 102).

It took me many years to appreciate the importance of prediction and theory to aid in learning and improvement. When managing many fail to predict when attempting to test improvement ideas through what should be experiments (often they are just changes without verification the change produced a desired effect, any learning or study of the results of the change). Without prediction learning is much less (if there is any at all) than it would be with such prediction.

In addition, when it is understood that management is based on prediction then the impact of the other 3 areas of the system of profound knowledge are clearer.

By exploring the basis for the prediction one must understand the theory they are operating with to make the prediction. Most often managers fail to develop a theory that allows them to predict. They fail to predict the results of an attempt to improve (PDSA), they fail to analyze the results of that improvement experiment, they fail to learn about the system that they are managing (since they fail to predict and then learn) and therefore cannot refine their theory based on their learning. When failing to do these things it is a surprise that learning is very ineffective? And without learning how can effective improvement be expected?

With, even a fairly simple understanding of the theory of knowledge the effectiveness of management improvement efforts are greatly increased. This topic is difficult for most to understand, I recommend reading chapter four of the New Economics. And I recommend returning to that chapter periodically as you apply management improvement techniques and learn and grow as a manager.

More from the Curious Cat Management Glossary: Theory of Knowledge