Ross Ashby was one of the pioneers of Cybernetics. His 1956 book, An Introduction to Cybernetics, is still one of the best introductions to Cybernetics. As I was researching his journals, I came across an interesting phrase – “destruction of information.” Ashby noted:
I am not sure whether I have stated before my thesis – that the business of living things is the destruction of information.
Ashby gave several examples to explain what he meant by this. For example:
Consider a thermostat controlling a room’s temperature. If it is working well, we can get no idea, from the temperature of the room whether it is hot or cold outside. The thermostat’s job is to stop this information from reaching the occupant.
He also gave the example of an antiaircraft gun and its predictor. Suppose we observe only the error made by each shell in succession. If the predictor is perfect, we shall get the sequence of 0,0,0,0 etc. By examining this sequence, we can get no information of about how the aircraft maneuvered. Contrast this with the record of a poor predictor: 2, 1, 2, 3… -3, 0, 3 etc. By examining, this we can get quite a good idea of how the pilot maneuvered. In general, the better the predictor, the less the maneuvers show in the errors. The predictor’s job is to destroy this information.
As an observer, we learn about a living system or a phenomenon by the variety it displays. Here, variety can be loosely expressed as the number of distinct states a system has. Interestingly, the number of states or the variety is dependent upon the system demonstrating it, as well as the observer’s ability to distinguish the different states. If the observer is not able to make the needed number of distinctions, then less information is generated. On the other hand, if the system of interest is able to hide its different states, it minimizes the amount of information available for the observer. In this post, we are interested in the latter category. Ashby talks about an interesting example to further this idea:
An insect whose coloration makes it invisible will not show, by its survival or disappearance whether a predator has or has not seen it. An imperfectly colored one will reveal this fact by whether it has survived or not.
Another example, Ashby gives is that of an expert boxer:
An expert boxer, when he comes home, will show no signs of whether he had a fight in the street or not. An imperfect boxer will carry the information.
Ashby’s idea can be further looked at from an adaptation standpoint. When you adapt very well to your everchanging surroundings, you are destroying information or you are not demonstrating any information. Ashby also noted that adaptation means “destroying information.” In this manner, you know that you are adapting well, when you don’t break a sweat. A master swordsman moves effortlessly while defeating an opponent. A good runner is not out of breath after a quick sprint.
The Performance Paradox:
My take on this idea from Ashby is to express it as a form of performance paradox – When something works really well, you will not notice it, or worse you will think that it’s wasteful. The most effective and highly efficient components stay the quietest. The best spy is the one you have not ever heard of. When you try to monitor a highly performing component, you may rarely get evidence of its performance. It is almost as if it is wasteful. Another way to view this is – the imperfect components lend themselves to be monitored, while the perfect components do not. The danger in not understanding regulation from a cybernetics standpoint is to completely misread the interactions, and assume that the perfect component has no value.
I encourage the reader to read further upon these ideas here:
Edit (12/1/2020): Adding more clarity on “destruction of information”.
The phrase “destruction of information” was used by Ashby from a Shannon entropy sense. He is indicating that the agent is purposefully reducing the information entropy that would had been otherwise available. Another example is that of a good poker player, who is difficult to read.
Please maintain social distance and wear masks. Stay safe and Always keep on learning…
In today’s post, I am looking at Locard’s Exchange Principle, named after the famous French Criminologist, Edmond Locard. Succinctly put, the exchange principle can be stated as “every contact leaves a trace.” This is perhaps well explained by Paul L. Kirk in his 1953 book, Crime Investigation: Physical Evidence and the Police Laboratory:
Wherever he steps, whatever he touches, whatever he leaves, even unconsciously, will serve as a silent witness against him. Not only his fingerprints or his footprints, but his hair, the fibers from his clothes, the glass he breaks, the tool mark he leaves, the paint he scratches, the blood or semen he deposits or collects. All of these and more bear mute witness against him. This is evidence that does not forget. It is not confused by the excitement of the moment. It is not absent because human witnesses are. It is factual evidence. Physical evidence cannot be wrong, it cannot perjure itself, it cannot be wholly absent. Only human failure to find it, study and understand it can diminish its value.
In other words, the perpetrator involved in a crime brings something into the scene and at the same time takes something with them. They both can be used against the perpetrator as forensic evidence. As a huge fan of mystery stories and shows, I was very interested when I first heard about this principle. Rather than the applications in the forensics science, I was thinking about it from a cybernetics standpoint. When two people converse with each other, their interactions can be viewed in the light of Locard’s exchange principle. Both of them bring something into the conversation, and in turn take something with them. There is a cross-transfer of ideas with successful conversations. To quote the late German philosopher, Hans-Georg Gadamer:
The true reality of human communication is such that a conversation doesn’t simply enforce one opinion over and against the other, nor does it simply add one opinion to another, as a kind of addition. Rather, true conversation transforms both viewpoints.
It may be challenged that true conversations do not always take place. However, this is something that we can strive for. At the same time, we need to be mindful not to treat information as a commodity that can be passed around. Just because we convey a message by speaking it out aloud, it does not mean that the message is conveyed. As the great cybernetician, Heinz von Foerster, would say – the hearer not the utterer determines the meaning of a message.
Claude Shannon, the father of Information Theory, looked in depth on successful transmission of messages. He noted:
The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point. Frequently the messages have meaning; that is, they refer to or are correlated according to some system with certain physical or conceptual entities. These semantic aspects of communication are irrelevant to the engineering problem. The significant aspect is that they are selected from a set of possible messages.
Shannon’s model had a source (a sender of a message), a transmission medium (a channel with noise and distortion) and a receiver. The sender had to encode the message and sent it through the medium. The receiver had to receive the message and decode the message and reconstruct the message. The receiver had to have a set of possible messages so that they were able to properly decode the message such that any distortion or noise introduced in the medium can be compensated for. Shannon came up with a quantitative measure for the amount of information in a message – entropy. This is also a measure of surprise. For a message with low entropy, there is little surprise. For a message with high entropy, there is a lot of surprise, and this requires redundancy to ensure that the message is properly conveyed. For example, if the sender is sending a message, “011”, then the sender can repeat the message three times. “011 011 011”. Thus, if the message gets distorted such as “011 001 011”, the receiver is able to still decode the message as “011”. Curiously, if the message has a full amount of surprise, then the receiver will not be able to decode the message. Thus, if the message was entirely new information, the message will not be decoded successfully, no matter how much redundancy is entered. This is the whole point of cryptic messages.
We are autopoietic entities, which means that we are informationally closed. No information can come into our organization from the outside. We are closed to information coming in. Any information is generated from within when we are exposed to perturbations from the outside. I have previously talked about this before. See here and here. We generate the information based on the perceptual network evolved specifically for us. We cannot pass information around as a commodity. Autopoeisis is the brainchild of Humberto Maturana and Francesco Varela. They noted:
Autopoietic systems do not have inputs or outputs. They can be perturbated by independent events and undergo internal structural changes which compensate these perturbations.
When we are communicating as part of being at the gemba, we have to keep in mind that we may not completely understand the meaning as the way the utterer intended. In a similar way, the hearer, the other person, may not have understood the meaning as we had intended the meaning to be. Even though we both may have heard each other 100%, we may not have communicated 100% (the way we think at least). Instead, I am interpreting what the other person is saying, and trying to respond to what I think the other person has said. The same applies to the other person. We are both interpreting each other. We are both trying to perturb each other with the hope that the meaning that is being generated has some similarity to what we want to communicate. It is here that I appreciate Locard’s Exchange Principle. We are coming in and leaving something (not the entire thing) at the scene, and at the same time, we are taking something (again not the entire thing) with us as we leave the scene. When we communicate, we are hopefully inspiring each other. Communication is never achieved 100%, but some transfer of ideas takes place resulting in transformation of existing ideas. As Gadmer indicated, when we communicate, the ideas do not get added on top of each other in an additive fashion. Rather, the ideas get transformed. When we are at the gemba, we should be keen on listening with intent. We should be open to receiving the ideas from others and be willing to transform. We should be mindful that what we are saying will not be understood the way we want it to be. We should also be mindful of our non-verbal communication. Most of the time, we can tell a lot by how a leader acts. A leader often talks the talk that we want to hear. However, their actions often talk the loudest.
I will stop with the great George Bernard Shaw’s wonderful quote on communication:
The biggest problem with communication is the illusion that it has occurred.
Please maintain social distance and wear masks. Stay safe and Always keep on learning…
I recently came across Dr. Donald Hoffman’s idea of Fitness-Beats-Truth or FBT Theorem. This is the idea that evolution stamps out true perceptions. In other words, an organism is more likely to survive if it does not have a true and accurate perception. As Hoffman explains it:
Suppose there is an objective reality of some kind. Then the FBT Theorem says that natural selection does not shape us to perceive the structure of that reality. It shapes us to perceive fitness points, and how to get them… The FBT Theorem has been tested and confirmed in many simulations. They reveal that Truth often goes extinct even if Fitness is far less complex.
Hoffman suggests that natural selection did not shape us to perceive the structure of an objective reality. Evolution gave us a less complex but efficient perceptual network that takes shortcuts to perceive “fitness points.” Evolution by natural selection does not favor true perceptions—it routinely drives them to extinction. Instead, natural selection favors perceptions that hide the truth and guide useful action.
An easy to way to digest this idea is to consider our ancient ancestors. If they heard a rustling sound in the grass, it benefitted them to not analyze and capture the entire surrounding to get an accurate and true model of the reality. Instead, they would survive only if they got a “quick and dirty” or good-enough model of the surrounding. They did not gain anything by having an elaborate and accurate perception. Their quick and dirty heuristics such as “if you hear a rustling on the grass, then flee” allowed them to survive and pass of their genes. In other words, their fitter perception did not comprise of a true and accurate perception of the world around them. They gained (they survived) based on fitness rather than truth. As Hoffman noted, having true perception would have been detrimental because it avoided shortcuts and heuristics that saved time. As complexity increases, heuristics work much better.
The idea of FBT aligns pretty well with the ideas of second order cybernetics (SOC) and radical constructivism. From an SOC standpoint, the emphasis for the representation of the world is not that of a model of causality, but of a model of constraints. As Ernst von Glasersfeld explains this:
In the biological theory of evolution, we speak of variability and selection, of environmental constraints and of survival. If an organism survives individually or as a species it means that, so far at least, it has been viable in the environment in which it happens to live. To survive, however, does not mean that the organism must in any sense reflect the character or the qualities of his environment. Gregory Bateson (1967) was the first who noticed that this theory of evolution, Darwin’s theory, is really a cybernetic theory because it is based on the concept of constraint rather than on the concept of causation.
In order to remain among the survivors, an organism has to ‘‘get by” the constraints which the environment poses. It has to squeeze between the bars of the constraints, to coin a metaphor. The environment does not determine how that might he achieved. It does not cause certain organisms to have certain characteristics or capabilities or to be a certain way. The environment merely eliminates those organisms that knock against its constraints. Anyone who by any means manages to get by the constraints, survives… All the environment contributes is constraints that knock out some of the changed organisms while others are left to survive. Thus, we can say that the only indication we may get of the ‘‘real” structure of the environment is through the organisms and the species that have been extinguished; the viable ones that survive merely constitute a selection of solutions among an infinity of potential solutions that might be equally viable.
Nature prefers efficient solutions that does the work most of the time, rather than effective solutions that work all of the time – solutions that prefer least energy expenditure, least number of parts etc. This approach also resonates with Occam’s razor. It is always advisable to have the least number of assumptions in your model. Another way to look at this is – the design with the least number of moving parts is always preferred.
The idea that true perceptions are not always advantageous may be counterintuitive. As complexity increases, we lack the perceptual network to truly comprehend the complexity. How we perceive our world around us depends a lot on our perceptual network, which is unique to our species. Our reality consists of omitting most of the attributes of the world around us. As Hoffman explains – the reality becomes simply a species-specific representation of fitness points on offer, and how we can act to get those points. Evolution has shaped us with perceptions that allow us to survive. But part of that involves hiding from us the stuff we don’t need to know.
Complexity also favors this approach of viable solutions/fitter perceptions. Hoffman notes:
We find that increasing the complexity of objective reality, or perceptual systems, or the temporal dynamics of fitness functions, increases the selection pressures against veridical perceptions.
I will add more thoughts on the FBT theorem at a later time. I encourage the readers to check out Hoffman’s book, The Case Against Reality.
Please maintain social distance and wear masks. Stay safe and Always keep on learning…
In today’s post, I am looking with more depth at the ideas of Cybernetics with relation to Ross Ashby, one of the pioneers of Cybernetics.
In particular, I am looking at one of the Ashby aphorisms:
When a machine breaks, it changes its mind.
This is a very interesting observation from a Cybernetics standpoint. Ashby defined a machine as follows:
It is a collection of parts which (a) alter in time, and (b) which interact with on one another in some determinate and known manner.
A designer designs the machine specific to an environment. This means that the designer has encoded a model of the environment into the machine so that when certain perturbations are encountered, the machine reacts in a certain manner. The variety that is estimated to be “thrown” at the machine is captured by the designer, and appropriate responses are encoded into the parts or the circuitry of the machine. The external variety is attenuated to a successful degree by the information conveyed by the machine in terms of affordances and signs on the machine. For example, a vending machine has signs on it along with pushable buttons that convey information to the user.
Ashby viewed this as the machine being successfully adapted to its environment. Ashby spoke of adaptation as being in a state of equilibrium. He referred to the stable state of equilibrium as “normal” equilibrium.
Normal equilibrium has some special properties which we must notice. Firstly, the system tends to the configuration C; so, if it is disturbed slightly from C, it will automatically develop internal actions or tendencies bringing it back to C. In other words, it opposes any disturbance from C. Further, if we disturb it in various ways, it will develop different tendencies with different disturbances, the tendencies being always adjusted to the disturbances so as to oppose them.
it must be noted that an equilibrium configuration is a· property of the organization… The equilibrium states of a machine are defined by the organization only.
From this point on, Ashby explains what the “break” means with regards to the machine.
Let us imagine a machine has “broken.” The first observation is that no matter how chaotic the result, it is, by our definition, still a machine. But it is a different machine. A break is a change of organization.
The specific organization entails what the machine can do when it is perturbed. The machine only has the initial information to deal with perturbations. When a new scenario arises, it cannot deal with it because it cannot generate new information (unlike humans). The difference with us humans is that we can generate new information as needed to deal with the new perturbation. Sometimes, this can be in the mode of the basic fight or flight response. The reaction is indeed an effort to get an equilibrium. As Ashby put it:
The drive to equilibrium forces the emergence of intelligence.
Information is described as the reduction in uncertainty. When the environment is dynamic and constantly changing, we can say that there is a usefulness quotient for the freshness of the information on hand. This is something like a “best by date” that is on the carton of milk. As Ashby put it – Any system that achieves appropriate selection (to a degree better than chance) does so as a consequence of information received. From a second order Cybernetics standpoint, information is generated by the autopoietic being. It is not something that can be transmitted in the form of a physical commodity from one person to the other. We should work on improving our ability to generate new information as needed when new perturbations arise. This provides us the requisite variety to deal with the new variety that is thrown at us. What worked in the past, and what worked at another organization may not be meaningful with the new perturbations. The generation of new information requires updating the model of the environment to some degree. This updating corresponds to isomorphism, the idea that there is a corresponding one to one relationship between the various states of the model and the environment. The better this correspondence, the better the model.
Another aspect of the statement that the machine changes its mind, is that the “mind” is embodied in the physical body also. There is a famous debate in philosophy that looks at how much the mind is separate from the body – is the mind embodied in the body or is it separate? It is believed that the mind is part of the body as much as the body being part of the mind. There is no use trying to separate the two. Ashby may be giving a gentle nod to this idea that the mind should not be separated from the body. When a machine breaks, it changes its mind!
Ashby’s approach of tying adaptation/intelligence to the idea of stable equilibrium is unique. I will finish off with his explanation regarding this:
Finally, there is one point of fundamental importance which must be grasped. It is that stable equilibrium is necessary for existence, and that systems in unstable equilibrium inevitably destroy themselves. Consequently, if we find that a system persists, in spite of the usual small disturbances which affect every physical body, then we may draw the conclusion with absolute certainty that the system must be in stable equilibrium. This may sound dogmatic, but I can see no escape from this deduction.
Please maintain social distance and wear masks. Stay safe and Always keep on learning… In case you missed it, my last post was Cybernetics Ideas from a Thermostat:
The thermostat is a simple device that is often used to describe the basic ideas of Cybernetics. Cybernetics is the art of steering. Simply put, a goal is identified and the “system” acts to get closer to the goal. In the example of the thermostat, the user specifies the setpoint for the thermostat such that when the temperature goes below the setpoint, it kicks on the furnace and stops when the internal temperature of the house meets the desired temperature. In a similar fashion, when the temperature goes above a setpoint, the thermostat kicks on the air conditioner to bring down the internal temperature. The thermostat acts as a medium for achieving a constant temperature inside the house. This is also the idea of homeostasis. In order to achieve what the thermostat does, it needs to have a closed loop. It needs to read the internal temperature at specified frequencies, and act as needed depending upon this information. If it was an open loop, no information is fed back into the system, and thus no homeostasis is achieved. An example of an open loop is a campfire without anyone to manage it. The fire continues to burn until it goes out.
Ernst von Glasersfeld, the father of radical constructivism, talked about these ideas in his short paper, Reflections on Cybernetics (2000):
The good old thermostat, the favorite example in the early literature of cybernetics, is still a useful explanatory tool. In it a temperature is set as the goal-state the user desires for the room. The thermostat knows nothing of the room or of desirable temperatures. It is designed to eliminate any discrepancy between a set reference value and the feedback it receives from its sensory organ, namely the value indicated by its thermometer. If the sensed value is too low, it switches on the heater, if it is too high, it switches on the cooling system. Employing Gordon Pask’s clever distinction (Pask, 1969, p.23–24): from the user’s point of view, the thermostat has a purpose for, i.e. to maintain a desired temperature, whereas the purpose in the device is to eliminate a difference.
The idea that the thermostat’s purpose is simply to eliminate a difference is most important here. I have written about this here.
Von Galsersfeld continues:
This example may also help to clarify a second cybernetic feature that is rarely stressed. Imagine a thermostat that has an extremely sensitive thermometer. If it senses a temperature that is a fraction below the reference value, it switches on the heater. The moment the temperature begins to rise above the reference, it switches on the cooling system –and thus it enters into an interminable oscillation. This would hardly be desirable. Therefore, it is important to design the device so that it has an area of inaction around the reference value where neither the one nor the other response is triggered. In other words, rather than a single switching point, there have to be two, with some space for equilibrium in between.
Homeostasis does not refer to a fine line it needs to maintain. It is often a band or a range. The wider the band, the easier it is to maintain homeostasis. It is more efficient to define the “stable conditions” to be between a range of values. A good example for this is a bicycle lane. It is not easy, if not impossible, to ride a bicycle in a straight line. However, it is easy to ride a bicycle in a somewhat wider lane. With the thermostat, this region is sometimes referred to as a “deadband.” This is the range of the temperature, within which the thermostat does not act (stays OFF). Below the lower limit, the thermostat will kick on the furnace, and above the upper limit, the thermostat will kick on the air conditioner.
Another important lesson from a thermostat is that if you want to change the room temperature, there is no point in moving the thermostat value to an extreme setpoint. Let’s say that you want to cool the room down. It is of no use if you put the thermostat value at 40 degrees F (4.44 degrees C). The house will not get colder faster with this approach. The thermostat controls the temperature inside the house, but not the speed with which it achieves this.
To be economically efficient, the thermostat must be aligned with the external temperature. For example, in colder weather conditions, the heat setpoint should be reduced (for example 67 degrees F or 19.4 degrees C), and similarly during warmer weather conditions the cool set point should be raised. Even though, the thermostat is the regulator, the user determines how this regulation is achieved. The thermostat as a regulator must also follow the Good Regulator Theorem. All good regulators must be a model of the system that it tries to regulate. The model of how to maintain the internal temperature constant (within the deadband) is programmed into the thermostat. It also follows the law of Requisite Variety. The thermostat must have the requisite variety to adjust the internal temperature based on the external perturbations. The thermostat must be able to differentiate the states of “below the setpoint temperature” or “above the setpoint temperature” to achieve the requisite variety and maintain the internal temperature. Both the Good Regulator Theorem and the Law of Requisite Variety are at utmost importance in Cybernetics, and they are both the contributions of one of the pioneers of Cybernetics, Ross Ashby.
I will finish this with some great aphorisms from Ross Ashby:
The drive to equilibrium forces the emergence of intelligence.
That the brain matches its environment is no more surprising than the matching of the two ends of a broken stick.
Every piece of wisdom is the worst folly in the opposite environment. Change the environment to its opposite and every piece of wisdom becomes the worst of folly.
The rule for decision is: Use what you know to narrow the field as far as possible: after that, do as you please.
Any system that achieves appropriate selection (to a degree better than chance) does so as a consequence of information received.
Please maintain social distance and wear masks. Stay safe and Always keep on learning…
In today’s post, I am using the ideas of the great American pragmatist philosopher, Richard Rorty. Rorty’s most famous work is Contingency, Irony and Solidarity. Rorty as a pragmatist follows the idea of an anti-essentialist. This basically means that there is no intrinsic essence to a phenomenon. Take for example, the idea of “Truth”. The general notion of Truth is that it can be found independent of human cognition. Rorty points out that this idea is not at all useful.
Rorty states:
Truth cannot be out there – cannot exist independently of the human mind – because sentences cannot so exist, or be out there. The world is out there, but descriptions of the world are not. Only descriptions of the world can be true of false. The world on its own – unaided by the describing activities of human beings – cannot.
The suggestion that truth, as well as the world, is out there is a legacy of an age in which the world was seen as the creation of a being who had a language of his own.
A key idea that Rorty brings up is the contingency of language. We may see language as this wonderful thing that enables us to communicate. Rorty describes language as contingent. This means that language is actually something we invented rather than discovered. And that language is really a tool we use to describe what is around us and our ideas. It is contingent because it is historically and geographically based. It is also contingent because we are engaged in language games, and meaning is an emergent phenomenon from our language games. This idea of language games is inspired by Ludwig Wittgenstein. If we see language as contingent, then we can prepare ourselves to not fall prey to the idea that truth is out there in the world, and that it is something that we can find. When we realize that language is contingent, we stop believing in dogmas and doctrines stipulated to us. We stop asking questions such as “What is it to be a human being?” Instead we ask, “What is it to inhabit a twenty first century democratic society?”
The idea of contingency slowly reveals us that sentences are no longer important. We should focus on vocabularies. Rorty explains that vocabularies allow us describe and re-describe the world. It is a holistic notion. When the notion of a “description of the world” is moved from the level of criterion-governed sentences within language games to language games as wholes, games which we do not choose between by reference to criteria, the idea that the world decides which descriptions are true can no longer be given a clear sense. It becomes hard to think that, that vocabulary is somehow already out there in the world, waiting for us to discover it. Languages are made rather than found, and truth is a property of linguistic entities (sentences).
As a pragmatist, Rorty’s view is that language, and in turn vocabulary, is a tool that is useful in a particular context. It does not have an intrinsic nature on its own because it is contingent on us, the users. Rorty wonderfully explains this as – the fact that Newton’s vocabulary lets us predict the world more easily than Aristotle’s does not mean that the world speaks Newtonian.
Another idea that Rorty proposes is that of the final vocabulary. Rorty says that we all have final vocabularies. All human beings carry about a set of words which they employ to justify their actions, their beliefs, and their lives. These are the words in which we formulate praise for our friends and contempt for our enemies, our long-term projects, our deepest self-doubts and our highest hopes… It is “final” in the sense that if doubt is cast on the worth of these words, their user has no noncircular argumentative recourse. Those words are as far as he can go with language; beyond them there is only helpless passivity or a resort to a force. A small part of a final vocabulary is made up of thin, flexible, and ubiquitous terms such as “true,” “good,” “right,” and “beautiful. ” The larger part contains thicker, more rigid, and more parochial terms, for example, “Christ,” “England,” “professional standards,” “decency,” “kindness,” “the Revolution,” “the Church,” “progressive,” “rigorous,” “creative.” The more parochial terms do most of the work.
Let’s look at what we have discussed so far and look at systems thinking. Pragmatism is not foreign to systems thinking. The pioneer of soft systems approach, C. West. Churchman was a pragmatist. He advised us that systems approach starts when we view the world through the eyes of another. The general commonsense view of systems is that they are real, and everyone sees the “system” objectively which helps to address the problem. The “system” can be drawn and described accurately. The system can be optimized to achieve maximum performance. This is the “hard systems” approach which utilizes a mechanistic view. However, as we start applying the pragmatist ideas we have looked at, we start to challenge this. “Systems” are not real entities but mental constructs by an observer to aid in understanding of a phenomenon of interest. “Systems” no longer become a necessity, but become contingent on the observer constructing it. When one says that the “healthcare system” is broken, we no longer look at the sentence in isolation, but rather we start looking at the vocabularies. The idea of contingency brings the non-objective nature of reality into the front. How one sees or experiences something depends on his or her contingency and their final vocabulary. From this standpoint, a system has nothing that the observer does not put into it. The intrinsic nature of a system is actually the properties assigned by the observer and contingent on his or her final vocabulary.
Similar ideas are present in Cybernetics and Systems Thinking:
We exist in language using language for our explanations- Humberto Maturana
The environment as we perceive it is our invention. – Heinz von Foerster
If contingency of language is an issue, then how does one do systems thinking then? Here I will introduce another idea from Rorty. This is the idea of an “ironist”. Rorty said:
I shall define an “ironist” as someone who fulfills three conditions : ( 1 ) She has radical and continuing doubts about the final vocabulary she currently uses, because she has been impressed by other vocabularies, vocabularies taken as final by people or books she has encountered; (2) she realizes that argument phrased in her present vocabulary can neither underwrite nor dissolve these doubts ; (3 ) insofar as she philosophizes about her situation, she does not think that her vocabulary is closer to reality than others, that it is in touch with a power not herself. Ironists who are inclined to philosophize see the choice between vocabularies as made neither within a neutral and universal metavocabulary nor by an attempt to fight one’s way past appearances to the real, but simply by playing the new off against the old.
Rorty adds:
The ironist spends her time worrying about the possibility that she has been initiated into the wrong tribe, taught to play the wrong language game. She worries that the process of socialization which turned her into a human being by giving her a language may have given her the wrong language, and so turned her into the wrong kind of human being. But she cannot give a criterion of wrongness. So, the more she is driven to articulate her situation in philosophical terms, the more she reminds herself of her rootlessness by constantly using terms like “Weltanschauung,” “perspective,” “dialectic,” “conceptual framework, “historical epoch,” “language game,” “redescription,” “vocabulary,” and “irony.”
From a second order Cybernetics standpoint, the idea of an ironist is self-referential. The observer is aware of their final vocabulary. Moreover, they are aware that their final vocabulary is perhaps incomplete or incorrect. They are historicist in the sense they understand that their language is contingent based on the time, place and society they were born into. They are also aware that others do not share their vocabulary. From this standpoint, what they can do is to seek understanding and ask leading questions to expose others to their contingencies of their vocabulary. They understand that truth is a function of agreement within language games. They don’t look at sentences in isolation, but at vocabularies in a holistic fashion. They realize that ideas are dynamic and do not have a fixed essence because vocabularies themselves are dynamic. They are open to changing their vocabularies without the fear of going against ideas they once held on to. They understand in a pragmatist sense that all models are wrong but the practical question is how wrong do they have to be to not be useful. (George Box)
I will finish with a quote from Fredrich Nietzsche:
“Truths are illusions about which one has forgotten that this is what they are; metaphors which are worn out and without sensuous power; coins which have lost their pictures and now matter only as metal, no longer as coins.”
Please maintain social distance and wear masks. Stay safe and Always keep on learning…
In today’s post, I am following the theme of cybernetic explanation that I talked about in my last post – The Monkey’s Prose – Cybernetic Explanation. I recently listened to the talks given as part of the Tenth International Conference on Complex Systems. I really enjoyed the keynote speech by the Herb. A. Simon award winner, Melanie Mitchell. She told the story of a project that her student did where the AI was able to recognize whether there was an animal in a picture or not with good accuracy. Her student dug deep into the AI’s model. The AI is taught to identify a characteristic by showing a large number of datasets (in this case pictures with and without animals). The AI is shown which picture has an animal and which picture does not. The AI comes up with an algorithm based on the large dataset. The correct answers reinforce the algorithm, and the wrong answers tweaks the algorithm as needed with the assigned weights to the “incorrectness”. This is very much like how we learn. What Mitchell’s student found was that the AI is assigning probabilities based on whether the background is blurry or not. When the background is blurry, it is more likely that there is an animal in the picture. In other words, it is not looking for an animal, it is just looking to see whether the background is blurry or not. Depending upon the statistical probability, the AI will answer that there is or there is not an animal in the picture.
We, humans, assign the meaning to the AI’s output, and believe that the AI is able to differentiate whether there is an animal in the picture or not. In actuality, the AI is merely using statistical probabilities of whether the background is blurry or not. We cannot help but assign meanings to things. We say that nature has a purpose, or that evolution has a purpose. We assign causality to phenomenon. It is interesting to think about whether it truly matters that the AI is not really identifying the animal in the picture. The outcome still has the appearance that the AI is able to tell whether there is an animal or not in the picture. We are able to bring in more concepts that the AI cannot. Mitchell discusses the difference between concepts and perceptual categories. What the AI is doing is constructing perceptual categories that are limited in nature, whereas what we construct are concepts that may be linked to other concepts. The example that Mitchell provided was that of a bridge. For us, a bridge can mean many things based on the linguistic application. We can say that a person is able to “bridge the gap” or that our nose has a bridge. The capacity for AI, at this time at least, is to stick to the bridge being a perceptual category based on the context of the data it has. We can talk in metaphors that the AI cannot understand. A bridge can be a concept or an actual physical thing for us. For a simple task such as the question of an animal in the picture carries no risk. However, as we up the ante to a task such as autonomous driving, we can no longer rely on the appearances of the AI being able to carry out the task. This is demonstrated in the morality or ethics debate with regards to AI, and how it should carry out probability calculations in the event of a hazard. This involves questions such as the ones in the trolley problem.
This also leads to another idea that has the cybernetic explanation embedded in it. This is the idea of “do no harm”. The requirement is not specifically to do good deeds, but to not do things that will cause harm to others. As the English philosopher, John Stuart Mill put it:
That the only purpose for which power can be rightfully exercised over any member of a civilized community, against his will, is to prevent harm to others.
This is also what Isaac Asimov referred to as the first of the three laws of robotics in his 1942 short story, Runaround:
A robot may not injure a human being or, through inaction, allow a human being to come to harm.
The other two laws are circularly referenced to the first law:
2. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.
The idea of cybernetic explanation gives us another perspective to purpose and meaning. Our natural disposition is to assign meaning and purpose, as I indicated earlier. We tend to believe that Truth is out there or that there is an objective reality. As the great Cybernetician Heinz von Foerster put it – “The environment contains no information; the environment is as it is”. Truth or descriptions of reality is our creation with our vocabulary. And most importantly, there are other beings describing realities with their vocabularies as well. I will finish with some wise words from Friedrich Nietzsche.
“It is we alone who have devised cause, sequence, for-each-other, relativity, constraint, number, law, freedom, motive, and purpose; and when we project and mix this symbol world into things as if it existed ‘in itself’, we act once more as we have always acted—mythologically.”
Please maintain social distance and wear masks. Stay safe and Always keep on learning…
Harish's Notebook - My notes... Lean, Cybernetics, Quality & Data Science. 