The Constraint of Custom:

I have written a lot about the problem of induction before. This was explained very well by the great Scottish philosopher, David Hume. Hume looked at the basis of beliefs that we hold such as:

  1. The sun will rise tomorrow; or
  2. If I drop this ball, it will fall to the ground

Hume noted that there is no uniformity in nature. In other words, it is not rational to believe that what has happened in the past will happen again in the future. Just because, we have seen the sun rise every single day of our lives, it does not guarantee that it will rise again tomorrow. We are using our experience of the sun rising to believe that it will rise again tomorrow. Even though, this might be irrational, Hume does not deny that we may see the belief of the sun rising as a sensible proposition. He notes:

None but a fool or madman will ever pretend to dispute the authority of experience, or to reject that great guide of human life.

It’s just that we cannot use logic to back this proposition up. We cannot conclude that the future is going to resemble the past, no matter how many examples of the past we have. We cannot simply use experience of the past because the only experience we have is of the past, and not of the future. Hume noted that to propose that the next future event will resemble the past because our most recent “future event” (the last experience event) resembled the past is circular:

All our experimental conclusions proceed upon the supposition that the future will be conformable to the past. To endeavor, therefore, the proof of this last supposition by probable arguments, or arguments regarding existence, must be evidently going in a circle, and taking that for granted, which is the very point in question.

Hume concluded that we fall prey to the problem of induction because we are creatures of habits:

For wherever the repetition of any act or operation produces a propensity to renew the same act or operation, without being impelled by any reasoning or process of the understanding, we always say, that this propensity is the effect of Custom. By employing this word, we pretend not to have given the ultimate reason of such a propensity. We only point out a principle of human nature, which is universally acknowledged, and which is well known by its effects.

In other words, it is our human nature to identify and seek patterns, use them to make predictions of the future. This is just how we are wired. We do this unconsciously. Our brains are prediction engines. We cannot help but do this. I will go further with this idea by utilizing a brilliant example from the wonderful American philosopher Charles Sanders Peirce. Peirce in 1868 wrote about an experiment to reveal the blind spot in the retina:

Does the reader know of the blind spot on the retina? Take a number of this journal, turn over the cover so as to expose the white paper, lay it sideways upon the table before which you must sit, and put two cents upon it, one near the left-hand edge, and the other to the right. Put your left hand over your left eye, and with the right eye look steadily at the left-hand cent. Then, with your right hand, move the right-hand cent (which is now plainly seen) towards the left hand. When it comes to a place near the middle of the page it will disappear—you cannot see it without turning your eye. Bring it nearer to the other cent, or carry it further away, and it will reappear; but at that particular spot it cannot be seen. Thus, it appears that there is a blind spot nearly in the middle of the retina; and this is confirmed by anatomy. It follows that the space we immediately see (when one eye is closed) is not, as we had imagined, a continuous oval, but is a ring, the filling up of which must be the work of the intellect. What more striking example could be desired of the impossibility of distinguishing intellectual results from intuitional data, by mere contemplation?

I highly encourage the reader to check this out, if they have not heard of this experiment. In fact, I welcome the reader to draw a line and then place the coin on the line. Doing so, the reader will see that the coin vanishes, however the line still remains visible in the periphery. This means that even though, our eye “sees” a ring, the brain actually fills it out and makes us see a “whole” picture. To add to this wonderful capability of our interpretative framework, the image that falls on our retina is actually upside-down. Yet, our brain makes it the “right-side” up. This would mean that newborn babies may actually see the world upside down and with voids, but at some point, the interpretative framework changes to correct it so that we see the world “correctly”.

How does our brain know to do this? The answer to this is that it was evolutionarily beneficial for our ancestors to do this, just like our custom to look for patterns. This is what Lila Gatlin would refer to as a D1 constraint. This is a context-free constraint that was evolutionarily passed down from generation to generation. This is a constraint that acts in any situation. In other words, to quote Alicia Juarrero, it is context free.

To go past this constraint, we have to use second order thinking. In other words, we have to think about thinking; we have to learn about learning; we have to look at understanding understanding. I welcome the reader to look at the posts I have written on this matter. I will finish with two quotes to further meditate on this:

Only when you realize you are blind, can you see. (Paraphrasing Heinz von Foerster)

The quieter you become, the more you can hear. – Ram Dass

Please maintain social distance, wear masks and take vaccination, if able. Stay safe and always keep on learning…

In case you missed it, my last post was The Cybernetics of “Here & Now” and “There & Then”

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Copernican Revolution – Systems Thinking:

In today’s post, I am looking at “Copernican Revolution”, a phrase used by the great German philosopher, Immanuel Kant. Immanuel Kant is one of the greatest names in philosophy. I am an Engineer by profession, and I started learning philosophy after I left school. As an Engineer, I am trained to think about causality in nature – if I do this, then that happens. This is often viewed as the mechanistic view of nature and it is reliant on empiricism. Empiricism is the idea that knowledge comes from experience. In contrast, at the other end of knowledge spectrum lies rationalism. Rationalism is the idea that knowledge comes from reason (internal). An empiricist can quickly fall into the trap of induction, where you believe that there is uniformity in nature. For example, if I clapped my hand twenty times, and the light flickered each time, I can then (falsely) conclude that the next time I clap my hand the light will flicker. My mind created a causal connection to my hand clapping and the light flickering.

David Hume, another great philosopher, challenged this and identified this approach as the problem of induction. He suggested that we, humans, are creatures of habit that we assign causality to things based on repeat experience. His view was that causality is assigned by us simply by habit. His famous example of challenging whether the sun will rise tomorrow exemplifies this:

That the sun will not rise tomorrow is no less intelligible a proposition, and implies no more contradiction, than the affirmation, that it will rise.

Hume came up with two main categories for human reason, often called Hume’s fork:

  1. Matters of fact – this represents knowledge that we gain from experience (synthetic), and this happens after the fact of experience (denoted by posteriori). An example is – the ball is heavy. Thinking cannot provide the knowledge that the ball is heavy. One has to interact with the ball to learn that the ball is heavy.
  2. Relation of ideas – this represents knowledge that does not rely on experience. This knowledge can be obtained simply through reason (analytic). This was identified as a priori or from before. For example – all bachelors are unmarried. No experience is needed for this knowledge. The meaning of unmarried is predicated in the term “bachelor”.

All the objects of human reason or enquiry may naturally be divided into two kinds, to wit, relations of ideas, and matters of fact. Of the first kind are the sciences of Geometry, Algebra, and Arithmetic … [which are] discoverable by the mere operation of thought … Matters of fact, which are the second object of human reason, are not ascertained in the same manner; nor is our evidence of their truth, however great, of a like nature with the foregoing.

Hume’s fork stipulates that all necessary truths are analytical, the meaning is predicated in the statement. Similarly, knowledge regarding matters of fact indicate that the knowledge is contingent on the experience gotten from the interaction. This leads to further ideas such as – there is a separation between the external world and the knowledge about the world. The knowledge about the world would come only from the world through empiricism. One can view this as the human mind revolving around the world.

Immanuel Kant challenged the idea of Hume’s fork and came up with the idea of a priori synthetic knowledge. Kant proposed that we, humans, are bestowed with a framework for reasoning that is a priori and yet synthetic. Kant synthesized ideas from rationalism and empiricism, and added a third tine to Hume’s fork. Kant famously stated – That all our knowledge begins with experience there can be no doubt. Kant clarified that it does not follow that knowledge arises out of experience. What we come to know is based on our mental faculty. The mind plays an important role in our knowledge of the world. The synthetic a priori propositions say something about the world, and yet at the same time they say something about our mind.

How the world is to us depends on how we experience it, and thus the knowledge of the external world is dependent on the structure of our mind. This idea is often described as a pair of spectacles that we are born with. We see the world through this pair of spectacles that we cannot take off. What we see forms our knowledge of the world, but it is dependent on the pair of spectacles that is a part of us. Kant’s great idea is that our knowledge of the world does not conform to the world. Our knowledge of the world conforms not to the nature of the world, but to the nature of our internal faculties. To paraphrase Heinz von Foerster, we do not see the world as is, it is as we see it.

Nicholas Copernicus, the Polish astronomer, came up with a heliocentric view of the world. The prevalent idea at the time was that the celestial bodies, including the sun, revolved around the earth. Copernicus challenged this, and showed that the earth actually revolves around the sun. Kant, in a similar fashion, suggested that the human minds do not revolve around the world with the meanings coming into our minds. Instead, the world revolves around our minds, and we assign meanings to the objects in the world. This is explained wonderfully by Julie. E. Maybee:

Naïve science assumes that our knowledge revolves around what the world is like, but, Hume’s criticism argued, this view entails that we cannot then have knowledge of scientific causes through reason. We can reestablish a connection between reason and knowledge, however, Kant suggested, if we say—not that knowledge revolves around what the world is like—but that knowledge revolves around what we are like. For the purposes of our knowledge, Kant said, we do not revolve around the world—the world revolves around us. Because we are rational creatures, we share a cognitive structure with one another that regularizes our experiences of the world. This intersubjectively shared structure of rationality—and not the world itself—grounds our knowledge.

Systems:

We have assumed that the knowledge of the world, our cognition, conforms to the world. Kant proposes that all we have access to is the phenomena, and not the actual world. What we are learning is dependent on us. We use an as-if model to generate meaning based on our interaction with the external world. In this viewpoint, the systems are not real things in the world. The systems are concepts that we construct, and they are as-if models that we use to make sense of the phenomena. What we view as systems are the constructions we make and depends on our need for understanding.  

Alan Stewart uses a similar idea to explain his views on constructivism:

The fundamental premise of constructivism is that we humans are self-regulating organisms who live from the inside out. As a philosophical counterpoint to naive realism, constructivism suggests that we are proactive co-creators of the reality to which we respond. Underlying this concept is that perception is an active process in which we ‘bring forth distinctions’. It is our idiosyncratic distinctions which form the structure of the world(s) which each of us inhabits.”

I will finish with a great lesson from Alan Watts:

“Everything in the world is gloriously meaningless.”

To further elaborate, I will add that all meaning comes from us. From a Hume-ian sense, we are creatures of habit in that we cannot stop assigning meaning. From a Kant-ian sense we are law-makers, not law-discoverers.

From a Systems Thinking perspective, we have unique perspectives and we assign meanings based on this. We construct “systems” “as-if” the different parts work together in a way to have a purpose and a meaning, both of which are assigned by us. The meaning comes inside out, not the other way around. To further this idea, as a human collective, we cocreate an emergent phenomenal world. In this aspect, “reality” is multidimensional, and each one of us has a version that is unique to us.  

Stay safe and Always keep on learning…

In case you missed it, my last post was Hegel, Dialectics and POSIWID:

Process Validation and the Problem of Induction:

EPSON MFP image

From “The Simpsons”

Marge: I smell beer. Did you go to Moe’s?

Homer: Every time I have beer on my breath, you assume I’ve been drinking.[1]

In today’s post, I will be looking at process validation and the problem of induction.  I have looked at process validation through another philosophical angle by using the lesson of the Ship of Theseus [4] in an earlier post.

US FDA defines process validation [2] as;

“The collection and evaluation of data, from the process design stage through commercial production, which establishes scientific evidence that a process is capable of consistently delivering quality product.”

My emphases on FDA’s definition are the two words – “capability” and “consistency”. One of the misconceptions about process validation is that once the process is validated, then it achieves almost an immaculate status. One of the horror stories I have heard from my friends in the Medical Devices field is that the manufacturer stopped inspecting the product since the process was validated. The problem with validation is the problem of induction. Induction is a process in philosophy – a means to obtain knowledge by looking for patterns from observations and coming to a conclusion. For example, the swans that I have seen so far are white, thus I conclude that ALL swans are white. This is a famous example to show the problem of induction because black swans do exist. However, the data I collected showed that all of the swans in my sample were white. My process of collection and evaluation of the data appears capable and the output consistent.

The misconception that the manufacturer had in the example above was the assumption that the process is going to remain the same and thus the output also will remain the same. This is the assumption that the future and present are going to resemble the past. This type of thinking is termed the assumption of “uniformity of nature” in philosophy. This problem of induction was first thoroughly questioned and looked at by the great Scottish philosopher David Hume (1711-1776). He was an empiricist who believed that knowledge should be based on one’s sense based experience.

One way of looking at process validation is to view the validation as a means to develop a process where it is optimized such that it can withstand the variations of the inputs. Validation is strictly based on the inputs at the time of validation. The 6 inputs – man, machine, method, materials, inspection process and the environment, all can suffer variation as time goes on. These variations reveal the problem of induction – the results are not going to stay the same. There is no uniformity of nature. The uniformities observed in the past are not going to hold for the present and future as well.

In general, when we are doing induction, we should try to meet five conditions;

  1. Use a large sample size that is statistically valid
  2. Make observations under different and extreme circumstances
  3. Ensure that none of the observations/data points contradict
  4. Try to make predictions based on your model
  5. Look for ways and test your model to fail

The use of statistics is considered as a must for process validation. The use of a statistically valid sample size ensures that we make meaningful inferences from the data. The use of different and extreme circumstances is the gist of operational qualification or OQ. OQ is the second qualification phase of process validation. Above all, we should understand how the model works. This helps us to predict how the process works and thus any contradicting data point must be evaluated. This helps us to listen to the process when it is talking. We should keep looking for ways to see where it fails in order to understand the boundary conditions. Ultimately, the more you try to make your model to fail, the better and more refined it becomes.

The FDA’s guidance on process validation [2] and the GHTF (Global Harmonized Task Force) [3] guidance on process validation both try to address the problem of induction through “Continued Process Verification” and “Maintaining a State of Validation”. We should continue monitoring the process to ensure that it remains in a state of validation. Anytime any of the inputs are changed, or if the outputs show a trend of decline, we should evaluate the possibility of revalidation as a remedy for the problem of induction. This brings into mind the quote “Trust but verify”. It is said that Ronald Reagan got this quote from Suzanne Massie, a Russian writer. The original quote is “Doveryai, no proveryai”.

I will finish off with a story from the great Indian epic Mahabharata, which points to the lack of uniformity in nature.

Once a beggar asked for some help from Yudhishthir, the eldest of the Pandavas. Yudhishthir told him to come on the next day. The beggar went away. At the time of this conversation, Yudhishthir’s younger brother Bhima was present. He took one big drum and started walking towards the city, beating the drum furiously. Yudhishthir was surprised.

He asked the reason for this. Bhima told him:
“I want to declare that our revered Yudhishthir has won the battle against time (Kaala). You told that beggar to come the next day. How do you know that you will be there tomorrow? How do you know that beggar would still be alive tomorrow? Even if you both are alive, you might not be in a position to give anything. Or, the beggar might not even need anything tomorrow. How did you know that you both can even meet tomorrow? You are the first person in this world who has won the time. I want to tell the people of Indraprastha about this.”

Yudhishthir got the message behind this talk and called that beggar right away to give the necessary help.

Always keep on learning…

In case you missed it, my last post was If a Lion Could Talk:

[1] The Simpsons – Season 27; Episode 575; Every Man’s Dream

[2] https://www.fda.gov/downloads/drugs/guidances/ucm070336.pdf

[3] https://www.fda.gov/OHRMS/DOCKETS/98fr/04d-0001-bkg0001-10-sg3_n99-10_edition2.pdf

[4] https://harishsnotebook.wordpress.com/2015/03/08/ship-of-theseus-and-process-validation/

[5] Non-uniformity of Nature Clock drawing by Annie Jose