Knowing vs. Understanding
The word knowledge can actually mean a lot of things in the vernacular. When most people say they know something they might also include the notion that they ‘understand’ that something, loosely implying that they grasp what caused that thing to be true. A more precise distinction between knowledge and understanding might go something like this. To know something means to have a veridical belief that a claim is true, what philosophers refer to as “justified, true belief.” One can know that the Great Wall of China exists, for example, or that an object let go in mid air will fall to earth because of gravity. These are demonstrable, explicit facts that have no counter evidence (to date) and are generally undisputed. One can also have a sense of knowing something more esoteric, such as that there are magic spirits living in a particularly beautiful glen in the forest. This sense of knowing, also a belief, is entirely subjective and cannot be verified or shared with others. It may have the same weight in the mind of the person possessing it as the fact of the Great Wall of China. This is likely due to the brain using the same mechanisms for knowing facts to represent more imaginative speculations, a result of the rapid evolution of our brain's capacity for imagination itself.
Understanding encompass much more than just knowing (see also my blog "What is knowledge - the noetic hierarchy"). It has the same sense or feel to the person holding the understanding as knowing a fact while retaining some of the sense of intuitions. One cannot explicitly always say why one holds an understanding but one has a sense of certainty about it. Others become experts in a field of study regarding phenomena of interest and can explicitly (consciously) express the basis for their understanding. However there is a gray area in expertise, especially in skill knowledge (muscle understanding) in which much of the understanding is tacit, learned by repetition and practice, and recalled unconsciously. Some aspects of it nevertheless spills into conscious awareness when the expert reflects on how s/he accomplished a specific task This interesting aspect of expertise became particularly cogent when artificial intelligence researchers were trying to build knowledge-based expert systems by querying experts on how they made specific decisions — the experts were often not really able to say explicitly how even though their decisions could be seen as correct.
Whether conscious or unconscious (intuition-like) understanding is much more than simply stating some fact. As an example, consider the statement: “An object released in mid air, will fall to the ground.” This is a statement of fact. It is knowledge. Every single person can make this statement because it is so obvious a part of existence. But it took the genius of Issac Newton to explain why this fact is true. It took the genius of Albert Einstein to refine that explanation in broader terms. The latter two people understood gravitation in ways that the common person does not. As a result there are interesting frameworks in which someone who only knows the fact of the statement will make errors in predicting what will happen to an object released in uncommon conditions. For example if you ask many people what will happen to an object released above the floor of an elevator (on the top floor of a very tall building) at exactly the instant that the elevator suffers a sudden release (itself plunging down due to the ‘fact’) they will be hard pressed to answer correctly. Some may intuit that the object will fall as usual, with not perceptible difference from dropping it while on stable ground. Some may intuit will fall more slowly at first because the elevator is falling fast and they will perceive that the object isn't catching up with the floor quite as fast as it might otherwise. But it took Einstein giving deep thought to the whole concept of frames of reference to grasp what would happen in this kind of situation, and be right about predictions in all unusual situations. He, as Newton had done to a lesser degree before, developed a deep understanding of gravitation that could result, for example, in predicting that the path of a light particle, a photon, could be bent by a strong gravitational field. More importantly, he could predict by how much!
Understanding, then, is much more than just knowing that something is true. It involves knowing how something (a phenomenon) works such that one can predict how the phenomenon will evolve in multiple different circumstances, under different conditions of inputs. Of course there are all scales of time, space, and complexity for phenomena. Simpler phenomena may require simple understanding. More complex ones require more complex understanding. A key part of developing understanding starts with a most fundamental understanding, a meta-understanding, of what actually constitutes a ‘phenomenon’ in order to first grasp the scale issues. That meta-understanding comes from systems thinking, or more specifically, from thinking about system boundaries (interested readers may wish to take a look at my series on systems science, the index of which can be found here). It is only with that ability that one can circumscribe the phenomenon of interest and segregate internal dynamics from input/output conditions. Systems thinking itself involves a form of native expertise in identifying meaningful boundaries in the first place. Much depends on an even larger context, essentially being able to recognize a system as part of a larger subsystem. This expertise, as that described above, is as often intuitive-like (art) as it is explicit (science). There are no absolute rules. Nevertheless, when meaningful boundaries are selected, a deep understanding of the relevant phenomenon can be gained.
At early stages this involves observation and preliminary predictions. If possible the observer may conduct controlled experiments using empirical methods to probe the phenomenon for additional information. Perceptive readers will recognize this as just what we call the scientific method. Some people actually think this way naturally. But many more need to be trained to think this way.
The epitome of understanding is often held out to be a scientific theory, like the theory of gravity. Theory, in this restricted, scientific sense, implies the existence of mathematical relations between the ‘parts’ that can be gleaned from experience. This is at least true for simpler phenomena but it is not at all clear that with our current versions of mathematics it is true for much more complex (e.g. social) phenomena. Such phenomena too often involve complex feedback loops within the system's dynamics that defeat our linear-leaning math. We can, of course, approximate to some degree parts of those dynamics and come close to a theoretical understanding of the phenomenon.
For the larger class of phenomena that are complex and wide in scales of time and space we resort to computation of models in order to make predictions.
Successful intelligence involves understanding enough about the world to be able to predict what is going to happen in the future (generally the near future) under given circumstances and initial conditions. The advantages for life are pretty obvious. Predicting danger or rewards would be advantageous for survival and lead to evolutionary pressure to increase. In a nutshell that is what drove the evolution of the mammalian and ultimately the human brains. In my post from Nov. 27, A retrospective of my early work on brain modeling I provide an explanation of the selective advantage conferred on entities with the capacity to anticipate coming events as opposed to merely reacting to current ones. There I argued that anticipation begins with forming causal links between objects and events such that the occurrence of the latter portends the occurrence of the latter, for example a whiff of odor conveys the presence of prey. As brains evolved a greater capacity for encoding many more objects and events in these causal relations the complexity of causal webs could likewise be encoded (learned) with greater experience. The human brain represents the epitome of one capable of encoding seemingly infinite complex relations (though, regrettably only seemingly).
When one can predict with reliability what is going to happen in a complex system, given information about the initial conditions and the inputs, one can say that one understands that system. This is not merely a mechanistic statement. It applies to stochastic processes as well, even non-linear ones. People are non-linear, stochastic processes, full of surprise and inconsistencies. That is alright from the perspective of understanding in that there are no claims that anything, let alone a person, will ever be fully understood in the same sense, say, that a mechanist would claim that knowing both the position and momentum of every particle in the universe s/he could predict the future. That latter is, of course not even possible in principle let alone practice. But understanding well enough to interact productively with other human beings is highly possible. Indeed, to interact well with other human beings is where the systems thinking comes into contact with sapience (the above linked index also provides links to my series on sapience). Wisdom is, thus, based on understanding to the greatest degree possible. This includes not only understanding people, but the whole world around us. Wisdom provides a means for seeing into the possible future.
Facts, Understanding, and Wisdom
The ordinary sort of knowledge most of us think of is really things like facts (the Great Wall of China) or simple relations (fire burns) which are facts themselves. Everything else we tend to relegate to intuition — we don't know how we know, we just know... But facts and relations can be encoded in the brain in multiple forms. The simplest, the one most accessible to recall, is declarative memory, which includes semantic (fact without necessary context) and episodic (facts taken within certain contexts). Implicit memories (along with what is called tacit memory) have more to do with process, for example, procedural memory involves things like how to ride a bike. These memories are formed from the integration of many repeated experiences, such as practicing riding that bike. Tacit memories are similar but with a much wider scope. They involve forming systemic patterns in subconscious memory of how things work in the world. This is where we store our general and contextually-based knowledge, our heuristic knowledge, our intuitive knowledge. It is not generally accessible to recall in the way that declarative knowledge is because it isn't the result of any one episode in our lives, but the aggregation of many variations on a systemic pattern. For example, our knowledge of how to go to a restaurant, order food, tip the waiter, etc., conventions we follow routinely in adult life, were not, as a rule, instantiated in any one visit to any one restaurant when we were children. The basic pattern might have been established with just a few instances, but all of the nuances we now have resulted from continually attaching these variations on the theme to the theme itself. The theme is script-like in that there is a basic set of rules that apply to the process. But the many variations that we picked up over the years provide for a certain kind of ad lib performance when required. Most of us gain a basic understanding of how restaurants work.
The subject of tipping the waiter allows us to examine the way in which understanding a process (ordering and paying for food in a restaurant) makes contact with wisdom. For many people, tipping is a relatively mechanical aspect. If the service was reasonable, compute 15% of the bill and leave it in change or add it to the credit card receipt. But there can be so much more to it than that. If one had been a waiter at one time in their life, they might consider the broader scope of the issue. The waiter is a real human being trying to earn a living. The customer might now be in a much better financial state than they were when they were a waiter and feel a sense of empathy with the guy who just served him. The customer could make a moral judgment, essentially thinking that he is well off and the waiter is less well off. But assuming the waiter did a good job, had a sincere and pleasing attitude, etc. why not bump the tip up a bit. Every once in a while waiters appreciate a bigger tip; it sends a message that someone appreciates their efforts. Was it wise? Well that is for events to ultimately decide. But it has all the elements required for wise judgments, empathy or moral sentiment, considering the bigger picture, etc. The judgment is not actually part of the restaurant script, but it is connected and meaningful to the participants. Both parties come away from the event a little happier. It has elements of wisdom.
The issue of scope, both time and space, might be illustrated by looking at a systems diagram. In the below figure is a subsystem of the world (light dashed circle), itself comprised of five subsystems or processes. Each process receives inputs from somewhere, those that interface with the outside world get inputs from outside sources and some may send outputs to outside sinks. The rest interface with one another.
Along with each process we see a rectangle meant to represent some important attribute or parameter (measurable attribute) that can be established for that process at any instance in time. The input and output flow rates can also presumably be measured and known. The process in the upper right has three heavy dashed ovals surrounding things we might call facts. What goes in, what comes out, what the state of the process is, etc. These facts, what the relevant measures are and what values they have relative to one another, constitute our knowledge of that piece of the system. Indeed, that piece is a system, which is why we can know anything about it.
If we apply the same kind of analysis to the other subsystems we will eventually come to know the whole system. Originally, we might say we understand the first subsystem, and then expand our understanding to the system as a whole. For eventually we will take measures of all of the inputs from outside sources and outputs to outside sinks, and with our understanding of the inner workings of this system (from our understanding of the workings of each individual and their collective interactions) we will be able to predict the outputs given the inputs.
This still has a mechanistic feel to it. But all you have to do is consider that all of these processes might be stochastic and some non-linear such that the behavior is predictable but only within some margin of error. Nevertheless, our understanding does allow us to anticipate a certain range of behaviors if we know what the inputs are along with the initial conditions of the parameters.
But this argument is recursive. It applies to all those sources and sinks that we identified but did not actually consider as part of the system. What we expect is that they are, along with the system we just came to understand, part of a larger system. We can expand our understanding by exploring that larger system in the same fashion we did this one. We will have expanded our scope to a broader world.
The waiter, in the above restaurant example, is like one of the inputs to the restaurant process (the whole system). Once we have begun to expand our model of the world outward to incorporate those kinds of inputs (and output sinks) we begin to form a more complete grasp of how the world works. Amazingly, it turns out that there are not that many different themes or script-like models in all of nature. The model in the above figure could just be one instance of a large family of models that all have similar features but vary in small ways. In some one subsystem might behave slightly differently. In another there might be a wholly unique kind of subsystem that modifies the overall pattern but does not make it fully different. After one has had many experiences with many (possibly even most) of these families of models it is conceivable that one becomes pretty good at intuiting good judgments when faced with decisions of how to interact with these systems.
The Extent of Mental Models of the World
This is really what is meant by wisdom. It takes sapience to guide the acquisition of knowledge, to be sure. And sapience is a function of the brain (in the above link I explain the brain basis of sapience and how it impacts the attainment of wisdom). Many people, unfortunately, go through life failing to see the similarities and differences that make the patterns. They fail to connect the dots because their brains are not quite strong enough to do so. Sapience guides the acquisition of tacit knowledge and understanding. It also guides the process of using that knowledge; not quite the same as memory recall for declarative knowledge, though likely using similar neural mechanisms. Sapient individuals are possessed with much greater understanding of all aspects of the world. They are not experts in any specific process necessarily (though they certainly can have a specialty just like anyone else). But they are able to see the broad, effective patterns of life regardless of culture or complexity. They are able to encode a global mental model that employs a number of sub-models just as the world system is comprised of subsystems.
Our mental models are really very much like the model in the above figure, though certainly more complex. Our models do not necessarily involve explicit measurements of state, the way we would require, say, in a computer model. But perceptive individuals do have the capacity to ascertain the general state of subsystems, again, based on their prior experiences. Some of us have the ability to see the patterns after sufficient exposures to many instances in real life. Some of us don't. And the capability goes along a range. At one end are those who simply see life as a disconnected set of happenings, most with very little meaning other than how does this help me right now. Some begin to see larger scales and get that some patterns affect them through more complex linkages, as when a college student realizes that studying (not fun) now might lead to a better career later on. And then a few grasp a much larger scale of systems and subsystems and their effects not only on themselves, but the world as a whole. Such people seem to be rare, unfortunately. Because the whole world is interconnected, everything to everything, even if only weakly through multiple chains of causality and feedback. This recognition is not unlike the Vedic tradition of Karma. Eventually feedback from the world we live in will affect our lives, sooner or later. Unless more people come to appreciate this understanding the majority are all doomed to suffer the fates bestowed on the world by the masses, those who do not understand. And that certainly seems to be the situation now. I anticipate trouble. On the other hand, if you do understand on the broadest possible scale, you will see the solution.