4. Methodology: Engineering the Sh*t Out of My Midlife Crisis
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"P(A|B) = [P(A)*P(B|A)]/P(B), all the rest is commentary."
― Scott Alexander, Rationalist blogger and psychiatrist, in "Astral Codex Ten" (Blog)
"Nebulosity is pervasive. Other than in mathematics and fundamental physics, nothing is ever definitely this-or-that. Everything is always somewhat this and somewhat that. Put under high enough magnification, a stainless steel ball exhibits the same indefiniteness as a cloud. No ball can be perfectly round, nor made of perfectly pure steel, nor can one definitely say whether some particular atoms are part of it or part of its surrounds."
― David Chapman, Computer scientist and Buddhist scholar, in "The Cells of the Eggplant"
“Science can amuse and fascinate us all, but it is engineering that changes the world.”
― Isaac Asimov, Science fiction writer, in "Isaac Asimov's Book of Science and Nature Quotations"
In this chapter, we are going to define the methodology we will use to build the MSE Framework. And since we want to be hardcore about doing things from First Principles, we are going to start from the absolute beginning.
Your beginning, that is.
When we are born, we know very few things about our new reality that we suddenly find ourselves in.
But what we are really good at is experimenting with the world and learning from it. Our very life depends upon it!
We quickly learn that when we are hungry, we cry and then we get fed. When we feel wet, we cry, and we get cleaned.
Then we start to associate some faces with the feeling of being fed or cleaned. As a result, we associate feelings of joy and comfort with those faces.
Then, we notice that the faces often smile at us. So we start to mimic the same action. This makes the faces smile even more and do even more feeding and cleaning for us, so this behavior gets fixed in our head.
Days and weeks pass. We are starting to get the hang of this new place. Familiar faces, familiar surroundings, familiar responses to our actions.
Life starts to become a little more predictable and thus a little more comfortable - at least some of the times and at least as compared to the complete chaos at the beginning.
Then one day, we open our eyes and find one of those familiar faces smiling at us. We beam back as usual.
But then, as if by magic, the face suddenly vanishes right in front of our eyes!
We are confused. What happened to that face? Where did it go?
The smile on our own faces disappears and is replaced with concern.
But then, before our attention wanders off, the face reappears seemingly out of nowhere!
Yaay! We are so happy! Everything is again fine with the world and we respond with a chortle.
Then the face vanishes again. And we are again confused. Where did it go? Why does this keep happening to me?
This cycle is repeated a few times and it is a roller coaster of emotions for us, with alternating joy and confusion.
Until we get tired. Or hungry. Or need to be cleaned.
And this game is just one of countless other pleasing, annoying and confusing things that occur around us all the time.
We start to realize that the world is a neverending series of confusing phenomena, i.e. magic, as far as we can tell.
Of course, we don’t know the word "magic" yet, but we intuitively associate the underlying concept of magic with the world. We know that we can have some level of control over our fate, but the world is full of magic nonetheless.
Fast forward a few more months, and we start to figure out what’s really going on with some of these magical phenomena.
By then, due to our incessant activity, curiosity and exploration of our world, we have started forming concepts like “the world contains objects", “the objects have properties like distance, movement, and occlusion”, "most of these objects appear to persist, though they occasionally move”, "sometimes, when an object moves, it can get occluded by another object" and so on.
We don’t know what any of those words mean yet, of course, but we have started forming a tacit understanding of real-world physics. Not the definitions, formulas and equations yet, but the related attributes, behaviors and intuitions behind them.
Suddenly one day, while we are once again playing our favorite game of peek-a-boo, a light turns on inside our heads. "That face that appears and disappears, it isn’t completely vanishing from reality. It is simply hiding behind an opaque object for a second and then reappearing!"
Unfortunately, once we start to understand this, some of these games start to lose their magic for us. When someone tries to play those games again with us, our attention starts to wander, looking for other magical things in the world.
And the same phenomenon repeats over and over.
Eventually, we figure out that most things that we initially thought were magical, turned out to be not so. They were predictable physical phenomena that we thought were unexpected or magical only because we didn't understand the physics behind them. But once we do, they don't remain magical to us anymore.
I believe that this joyful experience of magic (and its eventual loss) stays with us. Sometimes our whole lives. To the extent that whenever we come across something new that confounds us, our first intuitive reaction is to think that it must somehow be magical.
We cling to that joyful magical feeling so much that sometimes we prefer to suspend our disbelief or our natural curiosity just so that we can preserve the feeling!
Luckily, for many of us, the world contains a never-ending supply of magical objects and phenomena, and the fun continues. The cycle of temporarily experiencing magic, followed by exploration and understanding, which results in the loss of that sense of magic, continues in many directions and probably never ends.
What’s interesting and truly magical here is the fact that we are naturally wired to be curious, to perform experiments with the world, to analyze their results, discover insights and find reliable methods for coming up with trustworthy explanations.
Over time, we have created formalisms around this natural tendency that we were all born with. We have identified various concepts and processes involved in it and created rigorous definitions and recipes for them. They form the ultimate basis for the methodology we will use in this book.
You guessed it, we are talking about the concepts of evidence and reason, and the processes of the mathematical, scientific and engineering methods, in order to make sense of and manipulate the world around us.
Let us take a deeper (and more formal) dive into them.
Let us rewind back to the wonderful time when you had just opened your eyes for the first time and you knew very little about this totally new world you found yourself in.
Then, as your initial shock and displeasure about being thrust into this world against your wishes started to wear off, you started noticing things. Your brain was suddenly receiving a lot of input from the various senses that you were born with: sight, sound, smell, touch, taste, and proprioception.
You didn’t know it at that time, but what your brain was doing was collecting evidence about the world (and yourself), detecting patterns in it, categorizing those patterns into objects and concepts, making logical inferences based on them, and slowly piecing together a model of the world and yourself in it.
With each new piece of evidence your brain gathered, it updated this model, bringing it more in line with how the world really is. The strength and direction of these updates depended upon how strong the new evidence was, how strong your prior understanding of the phenomena was, whether it was corroborated by multiple senses and so on. It did this most of the time without your conscious awareness.
You didn’t know it at that time, but what you were essentially doing was Bayesian Inference. In fact, one of the leading theories of how our brain works is called the Bayesian Brain Hypothesis. It is fundamental to how we understand our reality.
(Note that any such descriptions of how our brain functions tend to be somewhat simplistic approximations of what really happens in there. In reality, the brain is a lot more complex and messy, but we can still usefully describe its function at a high level in the manner I have described. Keep these facts about the messiness as well as reasonable approximations in mind though, they are highly relevant to our endeavor and we will get to them soon enough.)
Over time, the model of reality that your brain built got larger and more complex, and most importantly, more capable of coming up with better explanations as well as making better predictions about phenomena in the real world.
And, the better your model got, the better your ability to deal with your new reality, going from total confusion to mere survival to some level of comfort to some measure of predictability and even control. Not perfect, but getting better all the time.
Also note that you did this not just by observing what was occurring around you, but also by performing various actions and observing their results. Not only that, but you acted on your curiosity, your imagination, and inspired by them, sought out new phenomena or built new things in the world, which further improved your understanding of it.
You didn’t know this at that time either, but what you were doing is known as Active Inference. This concept is so important to all living beings that we will devote a major part of the "Life" chapter to it, later on in the book.
Observation, reasoning, memory, imagination and experimentation were the basis upon which you gathered most of your early knowledge about the world. (Apart from the knowledge that was inscribed into your genes already.)
In short, we can say that evidence and reason were the basis upon which you built your model of reality.
As you grew up, you also started acquiring knowledge in other ways, such as relying on what the adults told you or what the books said. Over time, you also learned some things via introspection (which again could be described in terms of evidence and reason, turned inwards).
The astute reader may have realized that what we are really talking about here is the area of philosophy known as Epistemology. So let's get into that next.
Philosophy textbooks define Epistemology as the branch of philosophy that deals with knowledge: what it is, how we acquire it and related details.
Since we are trying to build a fundamental framework for meaning, purpose and hope from First Principles, based on knowledge rather than myths or dogma, we have to start there: What constitutes knowledge and which methods can be used to acquire it. (I have included a deep dive into First Principles Thinking if you are unfamiliar with it).
The most widely accepted definition of Knowledge is “justified true belief”.
It is an honest admission of the fact that none of us really knows the ultimate truth of reality and all we really have in the end are beliefs with various degrees of justifications of their truthfulness. (For now, just hold that thought in mind. We will get deeper into this in the next chapter.)
So, in order for a belief to be classified as Knowledge, we need strong justification for its truthfulness.
It goes without saying that beliefs that don't have justifications should not be considered as knowledge. These are things like myths or dogma for which we don't have any basis. Note that we aren't saying that they aren't valuable in other ways. Myths and dogma play a huge role in human affairs. All we are saying is that they can't be considered to be knowledge, and since our endeavor here is to create a framework based on knowledge, we can't include them in it.
This still leaves a gap.
Not everything falls neatly into "justified" vs "not justified". For many real-world phenomena, in particular, when dealing with subjects like meaning and purpose, things aren't always so black and white. What do we do about those?
This is one of the reasons why people throw up their hands and say that these things are beyond the scope of rational analysis. But, as we have seen, this allows things like myths and dogma with even weaker bases to get in and fill the gap. We want to stop that from happening.
What we have to do in such cases is to honestly acknowledge when we don't have an incontrovertible explanation of some phenomena, but we may have a strongly justified one, and this justification is significantly better than any other.
In such cases, we can provisionally allow such phenomena into our model, with the understanding that they can be replaced if better justifications become available down the road. Also, we always remember that this inclusion is provisional and do not rely on it too much in our model.
Being able to deal with such gray or nebulous areas is an important aspect of our methodology. How we do this will become clearer as we proceed through this chapter.
To start the discussion, let us identify the types of knowledge and then sources of knowledge. This may look a little too academic, but its importance should not be understated. In fact, I would argue that many attempts at defining meaning and purpose go wrong right here.
The first type of knowledge is Knowing Facts or Know-What. Here we include things that are typically associated with science. These come in the form of evidence, propositions, formulas, algorithms, equations, models, etc. that we use to explain natural phenomena.
The second type is Knowing How or Know-How. Here we include things like the skills associated with various processes, techniques and practices. This type of knowledge is hard to put into words, and can only be learned through observation and practice or apprenticeship. Other terms that are generally associated with this type of knowledge are Tacit knowledge or Embodied knowledge. Things like learning to play a musical instrument or riding a bike fall into this category.
There is a third type of knowledge, Knowledge by Acquaintance. This includes things like knowing a person or knowing the taste of some food. This type of knowledge can't be described in terms of facts or be taught via apprenticeship. It has to be learned via direct, personal relationships or encounters with the subject of knowledge.
Just to give you a taste of another way of defining types of knowledge, I have included a deep dive into John Vervaeke's Model of Cognition, which has a significant overlap with the above, but adds further nuances.
Next, let us look at the sources of knowledge.
The Stanford Encyclopedia of Philosophy states that there are 5 sources of knowledge:
Perception,
Reason,
Memory,
Testimony, and
Introspection.
While all of these sources are valuable, since we want to build our framework from First Principles, we need to be careful about how or under what constraints we use them.
It is easy to see that Perception and Reason are highly amenable to First Principles Thinking, because we can do those things ourselves.
For the third type, Memory, we should realize that while we do have first-hand access to our own memories, our memories do tend to fade or get overwritten over time. One way to mitigate this problem is to calibrate our trust in a memory based on how fresh it is or how likely it is that it might have been clouded due to our emotions or judgments.
Since it is practically impossible for us to experience or measure everything, we have to include knowledge acquired through the fourth source, Testimony, into our model. But, in order to do so, we need to have sufficient justification that we could acquire the knowledge ourselves if we wanted to.
For example, we can rely on a result published in a scientific journal (which would fall into the category of Testimony) provided we could, in principle, replicate the result ourselves if we wanted to. Scientific results are expected to fall into this category because being able to replicate them is a standard requirement for something to be considered scientific. (We have all heard of the "replication crisis", particularly in social sciences. Such a crisis is a lot rarer in the "hard sciences", so we will focus mainly on them.)
FInally, we need to say something important about the fourth source of knowledge, Introspection. There is some debate in the scientific community about how to deal with Introspection in an objective manner given that it is a subjective phenomenon.
In order to avoid stepping into controversial areas that border on pseudoscience or woo, we will only take any evidence derived from Introspection seriously if it meets all of the following criteria:
It must be simple to describe or define. The definition should be parsimonious and in its purest possible form, i.e. it should contain only the things that are absolutely necessary. Avoid tacking on any additional baggage.
It must be very widely corroborated. This entails that it should be easy to experience by anyone (including yourself) and the vast majority of people should agree on it.
It also goes without saying that it should also not be explainable in any other way. This includes all of the other acceptable sources of knowledge we discussed above, because, in that case, we would prefer those explanations anyway.
For example, this definition allows us to include the concept of consciousness, purely as subjective or phenomenal experience, into our methodology, without any of the additional religious or spiritual baggage that often gets associated with it. It fits our criteria because it is simple to define, in its purest most parsimonious form, we can readily experience it ourselves, is very widely corroborated, and we have no other explanation for it at present. (Of course, if a better explanation becomes available down the road, we will update our framework accordingly. We will talk about this in more detail in the chapter on Consciousness.)
We will also allow for something like The Great Unknown, the idea that there may be things that are beyond our comprehension, at present and maybe forever. This includes answers to some of the deepest questions about existence such as "Why does anything exist at all" or "What is its ultimate substrate" or "What does 'existing' even mean" etc. And again, the idea of The Great Unknown in its purest form is easy to define, is parsimonious in its definition, is easy to understand, is widely corroborated, and no other explanation for it exists.
At the same time, we cannot include introspective reports of many other religious concepts such as the existence of supernatural beings with specific features or actions or commandments supposedly coming from them. This is because they aren't widely corroborated in the sense that people don't agree on any of their specific features or actions or commandments, often even getting into conflicts due to such disagreements. Moreover, we have been finding other explanations for many of their supposed features or actions. As a result, we will not be able to include such reports of introspection in our model.
One could say that all such religious ideas ultimately have a common, parsimonious core, which, as it turns out, is essentially the same as the idea of the Great Unknown described earlier. So while we do include that in our model, we don't include any of the other attributes associated with any religion.
Yes, it is true that we have also developed other ways of experiencing and dealing with reality besides evidence and reason, such as emotions and intuition. Many people believe that these are fundamentally different from reason and may even be superior.
But more and more work in neuroscience, evolutionary psychology, cognitive science, and even machine learning is leading us to believe that all of them have logic or reason as their ultimate basis. It is just a lot more complex and messier. One can think of intuitions and emotions as extremely complex networks of logical inference that we can't always explain in terms of words. And possibly these networks have some emergent properties. But there is no other unknown or magical ingredient involved.
Essentially, unless one believes in magic, there is really no other possible source of these phenomena.
Moreover, we often find our intuitions and emotions failing us. This is because the validity of intuitions and emotions is heavily dependent upon the context you are in.
For example, our intuition might tell us that we should always go for high caloric foods. This is because this intuition has evolved in a time period during our evolution when nutrition wasn't as readily available as it is today. So while the intuition may have been valid at that time, it is not valid today.
The same can be said about emotions. It is a common experience for all of us that there are times when an emotion is appropriate and when it is not.
So reason is still the arbiter that decides whether the context we are in is appropriate for relying on our intuition or emotion. And since we are trying to develop a general framework for understanding reality and finding meaning, purpose and hope in it, it makes sense for us to rely on reason.
And even reason has a boss: The ultimate basis of everything is always evidence. Reason is just a process we use to explain the evidence, compress and represent it in our mind, and make predictions about any future evidence we expect to encounter.
All of this discussion on epistemology may have just been a long-winded way of me saying that we are going to base the framework only on the principles of Evidence and Reason, without relying on any faith or dogma or magic or opinion. (Yes, I am aware that there are some strong objections to using this approach for explaining reality, and in particular things like meaning, purpose and hope, and we will address them immediately after this section.)
It is true that taking this approach does mean we lose some of the sense of magic in our lives that we may have enjoyed in the past, but luckily we keep discovering new instances of unexplained or magical phenomena to entertain ourselves!
Also, an extremely important point to note is that any of the concepts that we do include in our framework can be challenged and even replaced if something better comes along. Nothing that is included here should be seen as final. In fact, being flexible and constantly improving, or being "alive", is an essential aspect of the framework.
Knowledge and epistemology in general are of course very deep topics in philosophy and volumes have been written on it. I am not really doing full justice to it here by describing them in one section. But I felt it is important to start there since we want to be absolutely sure that we aren't going to depend upon anything that does not have a strong justification for it, and we have thought about this aspect carefully.
This type of analysis is exactly what our insistence on rationality and Thinking from First Principles demands. (Now you may understand why I am unhappy with the other ways of finding meaning in our lives. They do not rise anywhere close to this standard.)
Still, an honest application of rationality itself requires that we recognize its limits, and come up with rational ways of dealing with them. In other words, we want to follow rational thinking as much as we can, but avoid becoming zealous followers of rationalism.
First off, as I have already acknowledged in the previous chapter, this endeavor of trying to find meaning, purpose and hope using evidence and reason alone is incredibly audacious and we need to avoid any appearance of naiveté or hubris while tackling it.
The dictionary defines rationalism as "a belief or theory that opinions and actions should be based on reason and knowledge rather than on religious belief or emotional response".
That sounds a lot like what we are trying to do here, isn't it?
But it is also known that taking this approach too far causes serious problems. In fact, one of the strongest criticisms of rationalism is exactly that its adherents suffer from naiveté on the one hand or hubris on the other.
At a higher level, one can say that every good idea that people have ever come up with eventually gets driven off the cliff by its most ardent followers! The concept of rationality or rationalism is no exception to this rule. History is full of self-described rationalists driving themselves and their faithful followers off cliffs.
This occurs because many rationalists have a tendency to make it sound like reality is far simpler than it really is or that our rational thinking abilities are far stronger than they really are.
Rationalists usually fall in love with some abstract idea or model or "theory of everything" and really want to believe that reality must conform to it. They try to impose those clean and pure ideas or models on real phenomena that are highly nebulous. Or they try to make predictions about things that are inherently unpredictable due to them being too complex or even computationally intractable. They often come up with recommendations, based on their models, that look good on paper but fail miserably in reality. And sometimes they reach conclusions that are just absurd but still stick to them.
What they don't realize is that they are actually falling for the same trap that the non-rationalists fall for, taking leaps of faith! Their faith in their model is so strong that they ignore all the complexity and nebulosity of reality.
Ironically, other rationalists have analyzed this phenomenon, and come up with names for a set of psychological biases that many ardent rationalists suffer from:
Legibility bias: The tendency to favor information that is clear, well-organized, and easily comprehensible over more complex or ambiguous content.
Formality or Elegance bias: The inclination to prioritize solutions or ideas that are aesthetically pleasing or sophisticated in presentation, sometimes at the expense of practicality or functionality.
Systematicity bias: The preference for structured and systematic approaches, often leading to a tendency to overlook or undervalue more flexible or chaotic methods.
We need to be careful to avoid these pitfalls of over-reliance on rational thinking. We know that reality is not always legible, formal, elegant or systematic.
We also need to distinguish between using rational methods in a bottom-up (or First Principles-based) manner vs using them in a top-down manner to prove some idea that the rationalist holds as sacred. The latter is basically the same as what is commonly known as "rationalization" and is not a scientific method. It is more of a method of advocacy, which is not what we are attempting to do here. We only want to create a framework that anyone can validate for themselves without anyone needing to advocate for it.
Finally, we need to acknowledge that reality seems to contain some regularities or patterns, but also a lot of nebulosity as well as many unknowns. And while rationality can help us come to grips with the patterned aspects of reality, it fails when dealing with nebulosity or unknowns. (This idea is critically important to the book, and I have included a Deep Dive into Pattern and Nebulosity at the end of the chapter to delve into some of its details.)
At an even more fundamental level, opponents of rationalism point to Gödel's Incompleteness Theorem as the ultimate proof of the futility of using rationality to understand reality. (I have included a Deep Dive into Gödel's Incompleteness Theorem at the end of the chapter, in case you are unfamiliar with it).
When people encounter these objections to rational thinking, their knee-jerk reaction is to totally give up on it and go back to far more irrational concepts to justify their beliefs (such as faith or emotions) or stop thinking altogether.
This is throwing the baby out with the bathwater. We don't want to do that.
In order to address all these limitations of using rationality to explain or deal with reality, we need to put some constraints on how we will apply it and also qualify the results we come up with.
We will do this in three ways.
Needless to say that reality is full of complexity, nebulosity and unknowns. In spite of the great strides we have made, our ability to model reality accurately or influence it predictably is limited.
This can be easily demonstrated if you take a simple action that many of us do regularly, such as making dinner. Depending upon what you want to make, you will need a variety of choices of ingredients and cooking utensils, and they will be in different locations in your kitchen. If you're like me, sometimes you will have to search for some of them. Once you have gathered everything you need, there will be prep work which involves handling various ingredients, cutting or crushing them, and so on. Then there is the recipe you need to follow, but you may not be able to follow it exactly as specified because something you have may not exactly meet the specifications. For example, the water might be too cold. Or the spice may have lost some of its flavor. Or the butter may be too hard. Or the phone might ring in the middle of cooking. And so on. One has to both follow the recipe but also keep adjusting it depending upon all such unforeseen factors.
Add to that the large variety of kitchen configurations, recipes, ingredients and utensils, it should be no wonder that, in spite of a huge and lucrative potential market, we have not yet managed to create a general-purpose cooking robot.
These problems have been widely recognized and studied by many rationalists who had the misfortune of taking their beautiful formulas and diagrams and building something real in the real world. Such as a house. Or a sewage pipeline. Or, indeed, a whole city. This is the domain of engineering, rather than science.
One of the most prominent voices among such people was that of Herbert Simon, an American political scientist who has the rare distinction of winning both the Nobel and Turing awards. He formulated an approach known as Bounded Rationality to deal with these constraints.
This approach recognizes the limits to our knowledge of reality, the cognitive constraints of our intellectual ability, and the complexity of the problem.
As we all know, the problem we are undertaking, that of defining the MSE Framework, is one such complex problem, and would definitely benefit from this approach.
Bounded Rationality involves the incorporation of heuristics, tacit or embodied knowledge and evidence-based practices that can’t always be formulated precisely in terms of scientific formulas. But note that we still need to be bound by the constraints of evidence and reason, not leaps of faith and wishful thinking.
Many engineering practices had already relied on ideas from Bounded Rationality even before this approach was formalized. And the approach gained in prominence in recent decades after people realized that their over-reliance on pure rationality caused many failures when they were met by the complexity and nebulosity inherent in reality.
The next pillar of our methodology is the Supremacy of Evidence.
One of the largest pitfalls of rationalist thinking throughout history has been that people have sometimes become so confident in their process of reasoning that they have neglected clear and present evidence that was contradictory to the results of their reasoning.
The well-known failures of the cultural and architectural movement of Modernism, as well as those of Rational Choice theory in economics and its eventual replacement by Behavioral Economics, have been blamed on rationalism.
We want to avoid this trap.
It seems silly to have to say this, but, as a result of such traps, it becomes important to say it anyway: We must always treat evidence as the ultimate arbiter, no matter how confident we are of our reasoning process.
Here is a famous quote that captures this sentiment perfectly:
"It doesn't matter how beautiful your theory is, it doesn't matter how smart you are. If it doesn't agree with experiment, it's wrong."
― Richard Feynman, American Physicist
Evidence is where the rubber meets the road. If the evidence does not agree with the results of our reasoning, then we must go back and deeply investigate our entire reasoning process. We need to reevaluate our starting point or look for any piece of evidence that we may have missed along the way and update our process accordingly.
Of course, when we start talking about the importance of having good starting points, the next question that arises is: How do we decide what is a good starting point?
Many models of reality rely on starting points that are abstractions or "somewhere out there" e.g. some abstract principles (including "The Great Unknown") or values (including "human values") or ultimate goals (including "human flourishing"). I am not saying that these starting points are necessarily wrong, but they are often been taken for granted without providing any supporting evidence for why those starting points and not some others. If we did that here, this whole project would become meaningless.
We need a starting point that is a lot more defensible and real.
As a result, I have chosen to go with something much more concrete and trustworthy as a starting point: the present moment.
If there is one thing I can be most sure of, it is that I exist right here, right now. I am alive, awake and experiencing this present moment. And the same applies to you.
So it makes sense to start from there instead of from some abstract concepts for which we may have no evidence.
We have a far better understanding of our present situation and the forces at play in the current moment than any other situation or moment. It is well acknowledged that our ability to predict things too far into the future (or remember things from the past, for that matter) goes down rapidly as the timespan increases. Same for physical distance or difference in contexts in general.
This is because reality is extremely complex, involving too many entities and forces acting on them along too many dimensions. Trying to apply our relatively limited knowledge of reality to this complex soup quickly leads to combinatorial explosions. Real-world systems are also extremely sensitive to initial conditions, which we may not be able to measure accurately. As a result, the longer the time or distance horizon or along any other dimension, the less we should trust our ability to make any statements about it.
It is not just that none of us can say exactly what will happen tomorrow or whether our account of some distant event in the past is accurate in all its aspects. Even well-known scientific theories, such as the origin of the universe or its presumed end aren't as clear-cut as we think they are. There are serious challenges to either the whole or parts of these theories.
In the MSE Framework, the way we deal with this limitation is to put most of our trust in the present moment, and our present location in space, and our current context or location along all other dimensions of reality that we can think of. We want to base as much of our thinking on phenomena we can observe "right here, right now".
Note that this does not mean we can't look at the long-term or distant phenomena at all. We can and certainly do make long-term plans and strategies when going through our lives. But all our actions are always in the present. And given that everything is constantly changing, we may have to adjust our planned course of action. This adjustment, again, occurs in the present moment.
It is important to realize that there is really no getting away from the present moment! Everything else is an abstraction, i.e., a simplified and often inaccurate or even completely untrue, model of reality.
This is clearly in contrast with most other ways of thinking about meaning, purpose and hope. Their insistence on abstract or far-off phenomena (such as the beginning or the end of the world, or heaven and hell, or some supernatural power etc.) necessarily involves taking leaps of faith. Often the distance from our present situation to those phenomena is so large that, if we are honest, we are simply unable to apply any sort of rational analysis to them. (Interestingly, this allows their proponents to claim that rational analysis is useless for such purposes. This is circular reasoning!)
Starting from and focusing on the here and now makes our methodology far more robust and amenable to rational analysis as compared to the alternatives.
By now, this should go without saying, but it is important to state: It is really important to remain open-minded and humble throughout this endeavor.
One of the most important failures of rationalists in the past has been overconfidence and hubris. This is what made them go out and rearrange the world based on their ideas and ignore clear evidence that things weren't quite working as they had imagined. And then they kept pushing ahead even after their ideas had completely failed.
One of my favorite saying is "All good ideas die at the hands of their most ardent supporters."
We obviously want to avoid this fate.
Combining all of these ideas then allows us to come up with a meaningful name for our methodology: "Present-Bounded Rationality".
Let us formally state what we mean by it.
As the name suggests, Present-Bounded Rationality combines elements of Bounded Rationality with the idea of starting from and focusing on the present moment. Both of these are well-known concepts, though, as far as I know, this particular combination appears to be new.
Let us now formally define what we mean by Present-Bounded Rationality by looking at its key characteristics:
It should go without saying that fundamentally we are still talking about a rational method of knowledge acquisition, but with a few modifications. So it is still primarily about using evidence and reason.
This means using the well-known techniques of Thinking from First Principles, Bayesian Inference and the Scientific Method. (These should be quite familiar to many people, but Deep Dives on each of them are included below for completeness.)
As we have already discussed, there are some caveats to this, which brings us to the next point.
We have already acknowledged that reality is extremely complex, contains a lot of nebulosity, unpredictability and even unknowns, and our cognitive ability is limited. As a result, our ability to deal with reality has some limits.
Even when we discover some patterns in reality, we may not be able to meaningfully extrapolate them too far beyond a certain point. We may be able to say some things with a high level of certainty, but some of the details may be fuzzy, and may even become meaningless if stretched too far.
Needless to say, we still need to create practical, realistic and rational explanations and make useful decisions within these constraints. The next two characteristics of our methodology address these constraints.
Satisficing is a combination of satisfying and sufficing. This is a well-known idea in real-world problem-solving, particularly in engineering.
The idea here is that sometimes, due to the limitations described above, coming up with perfectly optimal solutions to some problems may not be feasible. In such cases, we may need to settle for solutions that are "good enough" and keep improving them over time.
This is basically what we do pretty much all the time in our real lives. We are constantly making decisions in a complex and dynamic environment based on very limited knowledge. In fact, it has been shown that the total amount of knowledge, as we expand further out from our immediate context, is so incredibly large that there is simply no way we could analyze it rationally. We are almost always forced to satisfice.
There is ample evidence that everything in nature itself follows this approach. No system in nature can afford to look for the perfect solution, settling instead for something that is good enough and incrementally getting improved via the process of evolution.
Some of the most well-known examples of satisficing are the human reproductive system, the way pollen gets spread by bees, and indeed the process of rapid iterative development with constant feedback that is recommended for startup companies.
A major flaw in how rationality is applied by many ardent rationalists is that they have a strong belief in some abstraction and they try to fit reality to it instead of the other way around. In many cases, they continue to do this even when their flaws become known because the abstractions are a lot easier to manage and intellectually appealing than the messiness of reality.
What we will aim to do in our methodology is to always remain grounded in reality and try to fit our models to it rather than the other way around.
We also need to maintain a certain level of detachment from our emotions and feelings while doing so. (We shouldn't have to say this, but this is one of the most common flaws in other models of reality.)
But even non-judgmental grounding in reality isn't sufficient. We need to be clear about what point within it that we have the most confidence in.
Starting from and focusing on what is occurring "here and now" or "clear and present" reduces our dependence on the (potentially flawed or incomplete) accounts of the past or inherently unpredictable extrapolations about the future or indeed, the unjustifiable jumps from reality to myths or abstractions.
This concept can also be generalized to other dimensions besides time, such as physical distance or the number of hops in a network etc.
Primarily focusing our attention on our local vicinity along all these dimensions drastically reduces the complexity of the problem we are dealing with, ensuring we have a far stronger basis for our thinking and far better effectiveness in our actions.
And, again, by doing so, if we are able to create good enough explanations and solutions, and they can always be improved as we learn more, then that works for our purposes.
I won't belabor the point again, but including this here again, since it is such an important aspect of our methodology. Remaining humble and open-minded are extremely important pillars of Present-Bounded Rationality.
Since that was probably a little too long-winded (though, IMHO, necessary), I have included a cheat sheet below as a deep dive.
Before we proceed, I want to emphasize two interesting points.
I am not the first one to suggest ideas like non-judgemental focus on the present moment (though, as far as I know, I am using it in this way for the first time.)
In fact, what I am talking about looks a lot like the practice of Mindfulness. This connection between the Present-Bounded Rationality methodology and Mindfulness isn't accidental either. There is a strong scientific basis for this connection, and we will explore this in much depth in the chapter on Life.
Also, note that the ideas of grounding in reality and non-judgmental observation are central to the Western tradition of Stoicism.
I believe that the fact that our Present-Bounded Rationality methodology has so much in common with well-established practices in two totally unrelated traditions lends further credibility to the methodology. We will rely on this fact much later in the book when we talk about developing practices based on our model.
The second point I want to address is a common misconception about the relationship between science and engineering.
You may have noticed that I keep mentioning engineering in the same breath whenever I talk about science.
This is very much intentional. There are some interesting and critical differences between science and engineering.
The most obvious may be that the methodology of Present-Bounded Rationality is actually closer to engineering than to science. One can say Present-Bounded Rationality has the same relationship to engineering that pure rationality has to science.
But this idea goes a lot deeper than that.
We usually think of Engineering as the application of science and math to some real-world problem. For example, Wikipedia defines Engineering as follows:
"Engineering is the practice of using natural science, mathematics, and the engineering design process to solve technical problems, increase efficiency and productivity, and improve systems.
― Wikipedia
This way of defining engineering makes us believe that science and math were developed first and then engineering was developed as their application. But this is not true.
What do you call the activity that honeybees or spiders or birds perform when they build or fix up a hive or a web or a nest? What do you call the activities that human beings were engaging in when they started using the first tools or practicing agriculture or building shelters long before there was any notion of science? What do you call the activities that go on in your own body when it is repairing a broken bone or patching broken skin?
Going even deeper, when you take a close look at the activities that go on inside a living cell, such as various proteins performing various tasks such as building tracks for transporting molecules from one part of the cell to another or making copies of DNA and so on, a lot of them look exactly like what we would typically call engineering.
Every biological cell, as well as the organ or organism they are a part of is a nano-factory.
Don't these activities involve building or fixing complex structures in a methodical and repeatable way? Don't all of them involve complex technical problem-solving based on an understanding of requirements, physical principles and local conditions?
In other words, doesn't it all look like engineering?
What's interesting here is that neither honeybees nor spiders nor birds nor even the living cells in our bodies have any explicit or formal understanding of science or math. One could say that they have embodied or tacit knowledge of them, which they have acquired through the trial and error method of evolution, but none of that knowledge is in any form that we can recognize as an explicit scientific formula or model.
On the other hand, in spite of this seeming lack of understanding of science or math, all of these entities are able to deal with complex, nebulous and even unknown aspects of reality, which science and math struggle with!
And their methodology for doing that looks a lot like Present-Bounded Rationality!
I think that engineering, seen in this light, is a much broader and older discipline than what we have typically believed it to be.
One could even say that engineering, and its basis, Present-Bounded Rationality, are the primary activities all living creatures engage in.
Only for intelligent creatures like ourselves, science and math emerge as secondary activities that aid and formalize (and eventually improve) parts of various engineering methods and practices, instead of the other way around.
An engineer’s ultimate responsibility isn’t to discover scientific explanations or mathematical bases for phenomena but to solve a real problem in the world in a systematic way. And often, this has been achieved irrespective of whether the scientific or mathematical basis for the method used to solve the problem is known in advance.
Of course, such explanations or basis can always come later and may help improve the engineering method in turn, but it is not essential to the activity itself.
The point I am trying to make here is that engineering is far more powerful than even math or science, particularly when it comes to dealing with the real world or even the deeper questions of life.
One can say that engineering isn't just an application of science and math to real-world problems, but science and math are formalizations of engineering methods!
There is a general belief that math and science are inadequate for the task of dealing with many aspects of reality, including things like meaning, purpose and hope. The reason why I keep bringing up engineering is exactly because I think this inadequacy can be effectively addressed by adding engineering (and the related methodology of Present-Bounded Rationality) into the mix.
Now, I know what you're thinking. "But engineering or technology has created its own problems!"
Yes, that's true. Some of the major evils that plague us today have been blamed on technology: Everything from nuclear weapons to factory farming to overfishing to social media to the potential for the dreaded "AI apocalypse".
This is where, once again, some of the important aspects of Present-Bounded Rationality come into the picture. If you constrain your engineering efforts by grounding them in the present and being humble and open-minded, you can overcome the aforementioned evils.
In fact, I am going to go ahead and coin another term "Mindful Engineering", to capture this. One can think of Mindful Engineering as the realization or embodiment of Present-Bounded Rationality.
In other words, Mindful Engineering is the practice of Present-Bounded Rationality i.e. systematically building things or fixing problems in the real world, while practicing Bounded Rationality, grounding in the present moment, the supremacy of Evidence, humility and open-mindedness.
This reminds me of something.
At the beginning of the chapter, I included a meme about The Martian. Let me explain why.
In the movie by that name, Matt Damon's character finds himself stranded on Mars, alone and without any hope of being rescued anytime soon. He only has a few days' worth of rations, and more importantly, oxygen, left.
So, what does he do? In his words that have achieved meme status,
"In the face of overwhelming odds, I am left with only one option: I'm going to have to science the sh*t out of this!"
― Matt Damon, American actor, in "The Martian"
He then proceeds to calculate how to make his rations last longer, how to grow his own food, and even make his own oxygen! Eventually, he figures out how to take off using a broken rocket.
What is not obvious to a lot of people is that he isn't really "science-ing" much. He is mostly "engineering" it!
While there is certainly a good amount of science involved in what he does, most of his activities actually fall into the domain of engineering: Quickly and constantly assessing his overall situation, determining his constraints and requirements, figuring out and scheduling the critical path to survival, conducting field experiments when necessary, many of which fail and have to be redesigned. Not aim for the luxury of perfection but for the reality of "satisficing". Not discovering new concepts or models, but applying existing concepts and making them work in the messy and unpredictable world, to build something useful.
This is pretty much the textbook definition of engineering.
Of course, there is a lot of overlap between "science-ing" and "engineering", but if you had to choose only one word to describe what he was doing, you would call it engineering.
I included this here to further emphasize the distinct identity and supremacy of engineering as compared to science.
(Ahem. "Identity" and "Supremacy" and Matt Damon? Sorry, couldn't help it.)
I suppose my emphasis on engineering rather than science may look a little unusual to many people, so it makes sense for me to address one of the main objections I can think of.
In some ways, our methodology of Present-Bounded Rationality feels like a compromise.
We have been led to believe that we need a complete and perfect understanding of reality before we can talk about things like meaning and purpose. This belief has led people to create the illusion of complete and perfect knowledge by invoking some supernatural power with various attributes and behaviors to fill the gaps in our knowledge.
But do we really need that? I don't think so.
Let us look at some illustrative examples to clarify what I mean.
Cells in your body contain amazingly complex structures and execute equally complex processes that allow them to feed themselves, fix problems as they occur, and replicate when appropriate. They do this even though they are immersed in a complex, nebulous and largely unknown reality. They have no notion of any kind of supernatural power and that doesn't stop them.
Zooming up to the human level, we are able to manipulate the functioning of our own cells at the molecular level, etch nano-scale circuits on semiconductors, and land spacecraft on distant asteroids moving at astronomical speeds, in spite of the fact that we do not have complete and perfect knowledge of reality.
All these examples are proof that being able to evolve extremely complex structures and processes or solve extremely complex problems does not require complete and perfect knowledge.
Moreover, if you look at how all of these phenomena came into existence, you will see something very much like Present-Bounded Rationality at work. In fact, the methodology is simply a formalization of what we can see at work everywhere in nature.
So this methodology is not a compromise at all. In fact, it looks like that's the only methdology that works in the real world.
Isn’t that so much better than taking leaps of faith or taking someone’s overconfident but baseless proclamations as the truth? Or throwing up your hands that you aren't able to create a complete and perfect model of reality that explains everything?
It looks like a no-brainer to me.
Still, I am not against people who want to continue on the path of faith. This book is not about the raging debates on religion vs science or faith vs fact. I actually prefer a world where many competing approaches to the ultimate truth flourish and learn from each other. And each of these ways of thinking does have things to teach each other.
All I am saying is that, if someone does want to stick to the path of evidence and reason, it is possible to go a lot further with it than what we have been led to believe. More importantly, you aren't going to miss out on anything that the other paths give you.
Here is an interesting quote from Laplace, a well-known French scholar from the 18th century, that is highly instructive in this regard:
When Napoleon asked Laplace why he had not mentioned the Creator in his book on the system of the universe, Laplace said
“Sire, I had no need of that hypothesis.”
― Quoted by Augustus De Morgan in “Budget of Paradoxes”.
Similarly, I am also claiming that we do not need to hypothesize a magical source of complete and perfect knowledge in order to reach our goal of finding meaning, purpose and hope in our lives.
That concludes the discussion on the methodology that we will use to build the MSE Framework.
As you would expect for any framework based on First Principles Thinking, we need to start building it by first defining our model of Ultimate Reality. We will do that in the next chapter and then go on stepwise from there.
But before we do that, here are some deep dives into some of the concepts mentioned in this chapter. Most of this should be pretty familiar to readers who have a STEM background, so feel free to quickly skim over them if so.
As already mentioned (probably too many times!), we want to build everything from First Principles.
We start with the simplest and smallest possible number of self-evident phenomena that have no other known explanations. There is a scientific term for them - Axioms.
The axioms still need to satisfy the constraints of the epistemology as described above, including the types and sources of knowledge we are allowed to use. Also, even our axioms can be challenged and replaced if something better or deeper is discovered.
Starting from these axioms, we follow Bayesian Inferencing and the Scientific Method to build our model of reality.
Typically, the above methods will result in a formula or algorithm or a crisply defined model. Typically they fall into some branch of Science.
But sometimes, a phenomenon may be too nebulous or complex to be captured in this manner. We need to acknowledge that methods of rationality have some limits when using our relatively limited intellectual capacity to deal with something as complex as reality. So, we need to introduce the ideas of Satisficing, Heuristics, Approximations, Optimizations, Grounding in the present moment, Open-Mindedness and Humility into the mix. We are calling this version of rationality as Present-Bounded Rationality.
This methodology, along with practices that emerge from it, including those that utilize tacit or embodied knowledge that are still systematic and backed by evidence, is the essence of Mindful Engineering.
Moreover, as progress in the areas of science and engineering continues, it will continue to improve our conceptualizations of reality, including the definitions of meaning, purpose and hope that we come up with. In other words, none of this is frozen in time. We must also always try to remain open-minded, humble, and honest, and accept the fact that we don’t know everything.
Given our honesty in accepting that we do not know everything, we need to address the next logical question:
Be comfortable with not knowing and avoiding the temptation to take leaps of faith or rely on unjustified opinions. This is the most important aspect of any methodology based on rationality.
Treat the unknown phenomenon as a black box and study it. Create hypotheses or pedagogical devices to understand the unknown phenomena, but never confuse these constructs with the truth.
See if our lack of knowledge is because of some mental block or cultural baggage. If so, challenge such preconceived notions and see if better explanations can be found. (This is basically what we are doing when we ask whether meaning and purpose can be defined without invoking a supernatural power.)
Keep hacking at the phenomena using the methodology mentioned above to incrementally understand and explain it over time.
As we have already noted, the above methodology closely resembles that of science, and even more so, engineering.
Canadian cognitive scientist and philosopher John Vervaeke has proposed an integrative framework designed to understand and explain various aspects of human cognition. I am including here a deep dive into his model to give us a slightly different way of looking at essentially the same ideas I described in earlier in the section on Epistemology.
This model consists of 4Ps and 3Rs. The 4Ps define 4 types of "knowing" that we are familiar with:
Propositional Knowledge: This is knowledge about facts or "knowing that" something is the case. It is often verbal and can be communicated directly through statements or propositions.
Procedural Knowledge: This refers to "knowing how" to do something and involves skills and procedures. It is not primarily about facts but about processes and is often acquired through practice and experience.
Perspectival Knowledge: This involves "knowing what it is like" to have a particular experience from a specific perspective. It encompasses the subjective, first-person point of view, providing a context that frames our understanding of the world.
Participatory Knowledge: This is the knowledge that emerges from being in a relationship with something, where both the knower and the known are co-transformed. It's a deeper, more embodied form of knowing that involves shaping and being shaped by our engagement with the world, people, and practices.
But simply learning about the 4 types of knowing is insufficient because reality is like a firehose of information and we would be unable to function unless we can continuously evaluate what is relevant to us and what is not.
According to Vervake, we do this via a process he calls "Recursive Relevance Realization" (3Rs):
Relevance: As we discussed above, Relevance refers to separating the wheat from the chaff, or determining what is salient and important from the sea of information that surrounds us. This allows us to adequately model their current situation and respond effectively.
Realization: This term can be seen in two different ways. In order to deal effectively with reality, one has to "realize" it i.e. to grasp their situation well enough as well as to "realize" something i.e. do something real in the real world in order to respond to it.
Recursive: This refers to the fact that the process occurs at multiple levels, particularly in a complex organism such as ourselves. This may involve multiple levels of feedback loops both within the system and between the agent and the environment.
Vervaeke argues that cognition is not just about processing information or solving problems in isolation. Instead, it's deeply intertwined with our embodied engagement with the world, our perspectives, and our relationships, both with others and with our environment.
First Principles Thinking basically means “Don’t take someone else’s word for it, check it out yourself!”
The following diagram depicts this process in short:
Typically, when faced with a problem or the need to build something, we tend to follow conventional wisdom. But if we do that, and conventional wisdom is either wrong or has become outdated, then we end up repeating its mistakes.
So instead of doing that, we need to start from fundamental principles that are verifiable and incontrovertible and build everything up from there in rigorous, logical steps. Along the way, avoid making unjustified assumptions or jumping to conclusions.
This is known as Thinking from First Principles.
This idea has actually been around since Aristotle, and various famous people have been associated with it, including Richard Feynman, Charlie Munger and Elon Musk.
Needless to say, for the MSE Framework, we are taking a First Principles approach because we want to solve the crisis created by the existing solutions. It is conventional wisdom that has brought us here so we don't want to rely on it. We want to start from scratch and rethink the problem of meaning, purpose and hope, and see if we can end up with a better approach to solving it.
According to David Deutsch, physicist, the father of quantum computing and author of many fascinating books, the pursuit of the ultimate truth is an ongoing process that requires an open-minded and critical approach to knowledge.
We accomplish this by creating more and more accurate explanations of reality.
According to him, explanations are the fundamental building blocks of knowledge and are critical for understanding the world. He argues that the search for the ultimate truth is essentially the search for the best explanation.
In his view, explanations are not just descriptions of phenomena but are also predictive, testable, and falsifiable. Explanations provide a framework for understanding the world and allow us to make predictions about future observations.
He also says that there is no limit to the explanatory power of human knowledge, meaning that we can always strive to create better explanations of the world around us. What is important is the process of seeking and refining our explanations of reality through a rigorous and iterative process of testing, refining, and updating our understanding of the world.
Bayesian Inference is one of the most fundamental methods for building a model robust of reality. One of the leading theories about how our brains function is based on this idea and is actually known as the Bayesian Brain Hypothesis.
Let us look at a simple example to understand this process.
When an infant is playing with its toys, even when it appears as if it just randomly throwing things around, it is actually slowly gathering evidence about the characteristics of physical objects in its environment. It is piecing together concepts like object permanence, 3D space, weight, size, softness, and so on.
For example, when it repeatedly drops some toy on the floor in spite of you repeatedly complaining about it, it is actually coming to grips with gravity on its own.
All of us have been through this process and we continue it throughout our lives to learn about new things in our environment.
This is nothing but Bayesian Inference.
Let us look at a diagram to understand this process a bit more formally.
Step 1 (Initial Model): There is a model of reality inside your brain. One of the things that the model contains is the probability or likelihood of occurrence of some event.
For example, suppose you wake up on a beautiful summer morning and wonder if it is going to be sunny today. You know that it is the middle of summer so there is a high probability that the day is in fact going to be sunny. This is known as your “prior”.
Step 2 (Evidence): Each new signal that reaches your brain from any of your senses creates a new piece of evidence. Your brain receives and analyzes each such piece of evidence.
In our example, maybe you look out the window and you see that the roads and pavements are all wet. This is a new piece of evidence.
Step 3 (Bayes’ Rule): You weigh this new evidence, known as the “likelihood”, against your prior.
You know that wet roads and pavements means that it may have recently rained. This means that maybe it is one of those fluke summer days when it rains, and so there is a higher likelihood that it might rain again. So, while your original belief was that it should be sunny today, the wet grass is making you reconsider that belief.
Step 4 (Updated model): So, you update your model of reality accordingly.
In the above case, you lower your probability that it is going to be sunny today. This is known as the “posterior”.
This process gets repeated for every new piece of evidence that presents itself. And, accordingly, your model of reality gets more and more accurate over time.
In our example, you might suddenly remember that the president is coming to visit your city today and whenever a high-level government person visits, your city sends out crews to wash the streets. This makes you reverse course and raise your prediction of a sunny day back up.
A lot of our learning is basically a result of repeated application of this process and each time, we are likely to be improving our model of our reality.
As shown in the picture, the scientific method consists of making observations, creating a hypothesis based on those observations, and conducting experiments to see if the hypothesis proves to be correct or not.
If it is proven to be correct, then the hypothesis becomes a scientific fact or model. If not, then the results of the experiments are analyzed and may lead to further questions or insights and then you go through the whole cycle again.
Of course, in reality, not all progress in science actually follows this exact process. Many times, things are a lot messier, with accidents or sudden sparks of insights or just fits and starts. But even in those cases, the scientific method is still the best way to explain the process after the fact or to replicate the results.
Since the entire process is based on evidence and reasoning, and can always be replicated or tested, we have a lot of confidence in it, we can make predictions based on it and we can defend it if challenged.
A hypothesis that gets proven to be correct is basically an abstract model that encapsulates (all of or possibly some aspects of) all the evidence that went into it. All of science is basically a constantly evolving set of such models. The set is evolving because the universe is vast and complex and we keep discovering new phenomena and new evidence all the time.
This unfortunately means that science is never really “done” or “complete”.
This fact causes a lot of people a lot of discomfort because human beings are constantly looking for certainty. So much so, that we will believe things that may not even be true if they are given an aura of authority and certainty.
But science affords us a different kind of authority and certainty.
Science is authoritative because, it is impossible to defy it, in the areas where it is “settled”. And wherever it is still not completely settled, it tells us the limits of its reach. So, we can always rely on it as long as we stay within those limits.
And science also provides a different type of certainty because its methods are future-proof. While the body of science as a whole may evolve as we collect more and more evidence, the underlying scientific method we rely on to process that evidence and improve our models will continue to be trustworthy.
Many subjective experiences are self-evident to ourselves. We can honestly say that they are occurring, but the inner world of our minds is mostly opaque to anyone other than ourselves, so there is really no way to prove to someone else anything about any of the phenomena we may be experiencing inside our minds.
In other words, it is really not possible to say anything objectively about our subjective experience.
Unfortunately, this has meant that science has not made a lot of progress in analyzing our internal experience, resulting in a major gap in our understanding. This is particularly relevant to concepts like consciousness, meaning and purpose because they are only felt internally.
But lately, many scientists have come around to accept that such subjective phenomena can be analyzed scientifically if we can ensure that the subjective experience is widely corroborated.
For the MSE Framework, we will take evidence of introspective phenomena seriously if it is widely corroborated and if there is no other explanation for it.
It goes without saying that the existence of consciousness is pretty much universally corroborated, and has found no other explanation, so we will include it in our models.
Of course, if an objective explanation for consciousness is found down the road, we will adjust our models accordingly.
David Chapman, a computer scientist and Buddhist scholar, has written two phenomenal books, "In the Cells of the Eggplant" and "Meaningness". In these books, he talks extensively about the fact that reality seems to contain both pattern as well as nebulosity, and how that affects our ideas of rationalism and meaning.
Science has shown us that reality does seem to contain many patterns, i.e. aspects that are clear, definite and structured. In many cases, we have managed to capture these patterns in terms of formulas and equations with very high levels of predictability and accuracy.
For example, in Physics we learn about various particles, their properties, forces that act on them and laws that govern them. (We will have a lot to say about all of these in the chapter on Physical Reality.)
But, at the same time, we also have ample evidence that many aspects of reality do not seem to be so easy to capture in terms of formulas. They are too nebulous or inherently indeterminate, fluid, and ambiguous.
Once again, Physics itself tells us that, at the bottom of it all, we have quantum fields that are inherently nebulous. We also have the Heisenberg Uncertainty Principle that tells us that there is a hard limit on how accurately we can measure the position and momentum of a particle at the same time.
Even above the level of quantum fields and particles, we have such complex interactions among the unimaginably large number of particles of various types, that it would be practically impossible to compute their exact future properties even after a very short time interval into the future.
At an even higher level, Chapman gives a great example of a cloud. No matter which physical or chemical properties you consider, trying to exactly describe a cloud is impossible. It is inherently nebulous. And we deal with many such concepts on a regular basis.
Chapman argues that pure Rationalism tends to overemphasize the patterned aspects of reality while ignoring the nebulous ones. As a result, while we should not abandon Rationalism, we definitely need to look beyond it to understand or deal with reality.
While I came across these ideas through Chapman's books (which are based on Buddhist philosophy), they actually have many close parallels in other bodies of thinking, such as:
Shiva and Shakti in Hindu / Vedic philosophy
Emptiness and Form in Zen philosophy
Apollonian and Dionysian forces in Western philosophy
Gödel’s Incompleteness Theorem proves that no mathematical system can be complete as well as self-consistent.
Many people have used this as proof that trying to use reason or rationality to understand reality is doomed right at the core, because it has mathematical concepts at its core. Essentially the claim is that rationality is a mathematical system and as such, it can not provide an explanation of reality that is both consistent and complete.
The way the MSE Framework addresses this problem is as follows:
The MSE Framework relies not on pure rationality, but on Present-Bounded Rationality. This methodology takes into account the inherent limitations of rationality and accommodates them by using heuristics, satisficing, starting from and focusing on the present, and focusing on grounding in reality rather than abstractions.
We are not claiming to build a complete model of reality. In fact, we admit that reality contains many unknowns and also complexity and nebulosity that we are unable to capture in terms of mathematical concepts.
We rely on axioms while building our model, which again means we are not claiming to build a complete model. We admit that we cannot peek behind the axioms.
Moreover, we are not aiming to create a complete and final solution to our problem of defining meaning, purpose and hope either. We explicitly say that our methodology assumes that any solution we come up with, while it will be good enough to solve the problem in the best way possible currently, would still remain open to learning and modification in the future.
Sometimes, the people who bring up Gödel’s Theorem and the limits of rationality in general want you to just give up on rationality and take leaps of faith with them or accept some dogma or poetic ideas.
But the flaw in that argument is that even if one admits all these limits of rationality, that does not give you permission to suddenly turn around and run in the opposite direction where your only choice is to take leaps of faith or rely on even less firmly established concepts. Just because you only have a good enough but less-than-perfect solution does not mean you should suddenly jump to something with no substantiated basis at all!
