Hypothesis and Evidence

“A wise man proportions his belief to the evidence.”
An Enquiry Concerning Human Understanding, David Hume 1748


I would like to thank all those readers who have stuck with me through this investigation of reasoning. This is not as passion filled as a tirade against our blood-soaked entertainment or a timely warning and threat from the news of big oil and the Saudis filling our papers of late. This is looking to share something a bit more substantial, a way of understanding that might make it a bit easier to cope with the daily dose of foolishness and bad news.

Sometimes I have been able to restore compassion by using a poetic model of my fellow creatures that leaves aside the ethical sinner – saint dichotomy and dares to look a little deeper into the physiological substrate on which all human experience depends. In this model we are more like robots with a few circuits mis-wired due to traumas of every imaginable stripe. Not just robots, we are far too creative and unpredictable for that. We are robots holding wounded angels as carefully as we can, like the image I saw on a cover of a book long ago: ATAs we enthroned our machines we came to resemble them. I like this image, almost an icon of the fossil fueled industrial age. It captures where we find ourselves, about two steps from chapel perilous. Ruled by cultural values from the so-called age of reason it is important we understand just what this reasoning is before we are prepared to really get what the teachers mean by saying the mind is in the heart center and that it is the body that is centered in the skull.

Moving through the world, what is it that we humans are actually doing? We have (or are?) a nervous system hooked up to senses and a brain. It is relaying information in the form of electrical signals constantly. Some of these signals might run into the brain, others will inform glands, muscles, and a whole host of biological processes in a never ending quest to maintain homeostasis and coherency. Of those that make it to the brain a few might make it through numerous cascades of neural nets from the more primitive layers to the neocortex where our conscious lives take place, at least for the most part. Numerous poisons and handicaps can cripple this process, thwarting its proper functioning anywhere along the way from the senses to the visceral tissues involved all the way up to the brain itself. What are we going to do? This is what we have to work with.

While the pathologies are important, more so than our culture enamored as it is with health, youth and beauty may ever understand, it is not the focus of today’s post. Assume that the signals are arriving without incident, that the information they carry is delivered accurately as possible and that the reception of the electrical signals in the wetware brain’s neural nets is weighted and organized optimally. What is the brain doing with all that information?

I propose that it is making models of its experience of the world. By laying down tracks among the nets memories are being formed and reformed. These nets take the raw data and categorize and classify it in a multitude of ways. Cognitive science has been able to use imaging to confirm what we all experience; simple concepts are used to build more complex concepts which in turn are used to form even more complex thoughts along a hierarchy of emergent insights. These ladders of insights are not necessarily correct or accurate, though they tend to feel as if they are in their moments of coming together. The process of thinking is experienced as an ongoing, piecemeal, additive function energized by a sense of expanding insight. It feels like we are really figuring something out, that what was formerly vague is becoming more clear, what was formerly confusing is making more sense.

Sometimes these ladders of emergent discoveries survive the cold light of rational analysis on a Monday morning, sometimes they do not. Evolution evidently designed our brains to be these kinds of information processing machines. There is survival value in the ability to take the raw data from the inner and outer environments as conveyed through the nervous system as information channel and turn them into organized pictures of what we take to be really real. Despite how it might feel when we are soaring through the inner skies of learning, if I may paint a poetic picture of the process, the feeling is no guarantee that the actual cognitions are valid.

The survival value of this cognitive ability comes from its influence on how we make decisions about what actions we will pursue. How we react to circumstances and how we choose what is worthwhile to work and strive for are all colored by the models we have made about what the world is and what we are within it. With action comes risk. With action comes the chance of failure. With action comes the long arm of the law of cause and effect. Choosing not to act, is an act. It is inescapable.

The other inescapable fact of our existential situation is that all of this takes place in an environment of uncertainty. The building of our models, the precepts our senses first create, the noisy information channels and the specific causes and conditions accompanying a particular action are all thoroughly surrounded by uncertainty. Remember the robot and the angel?  A recognition of the basic physiological substrate and the nature of our cognitive power, which is always and everywhere embedded in one environment or another, leads me to conclude all our beliefs are of the nature of hypothesis. They are tentative, subject to alteration as needed under the influence of new evidence. Belief is how we experience the power of the evidence we have reasoned about.

Most of the evidence we encounter will consist of data that is what we expected. After all we have built our models from the gathering of prior experiences and fashioned them to capture what those experiences have taught us to expect is most likely. Such data is said to have low information content. Gregory Bateson identified information as the difference that makes a difference. It is measured by the amount of uncertainty that it removes. See how all these elements are starting to come together?

We see a man drop an apple and it hits the concrete sidewalk; the outcome is thoroughly expected and our model of how things move when dropped is barely affected. Maybe it is strengthened a little but the amount of learning is minimal. On the other hand if he drops the apple and it flies up into the air then we are shocked, surprised. Now there is a crisis of sorts in the stability of our model. We wonder what the Dickens is going on. Magicians use this feature of our mental makeup all the time to deliver surprises and the unexpected.

All these elements and their relationships to learning are expressed in the incredibly conscience language of mathematics in what is known as conditional probability or Bayes theorem. Bayes theorem is used to determine how likely the proposed hypothesis is given the evidence. It transforms the prior probability into what is referred to as the posterior probability. In symbols it calculates p(hypothesis |evidence) which is read “the probability of the hypothesis given the evidence.” Consider the implications of those italicized words. It captures all rational striving for human understanding.

Bayes Theorem looks like this in which p( ) is the probability, H the hypothesis, E the evidence:eq_bayesp(H) is the prior probability that H is correct before taking into account the current evidence E.

p(E | H) is the conditional probability of seeing the evidence E given that the hypothesis H is true, often called the likelihood.

p(E) is the marginal probability of the evidence, how likely this particular evidence is without respect to the current hypothesis or under the condition of any possible hypothesis.

p(H | E) is the posterior probability, the result. It provides the probability that the hypothesis is true given the evidence and the previous belief in the hypothesis.

Next week we are going to use this machinery to graphically explore what happens as a prior encounters data that comes in all shapes and sizes. Some of the data we encounter will be expected while other data will catch us by surprise.

Measure for Measure

Our scientific understanding is powerful to the degree that it corresponds with the actual world of experience. The degree to which our understanding achieves this fit is judged by practical considerations; can we use it to predict outcomes of our investigations into what remains unknown or use it to control events in the real world? Understanding increases as we work to tune this correspondence between these bodies of knowledge and the actual structural, energetic and information patterns in the universe. Our rockets get where they’re going, our rounds fire straight.

Today we bear the fruit of three or four centuries of methodological investigation of how the things we perceive around us behave. This body of knowledge comes packaged in the form of conceptual models having, more often than not, a mathematical expression. The math holds the unique philosophy of this whole endeavor. These models are built on the ability to measure something. By measuring human beings are able to transcend their subjectivity and achieve a precision that is readily communicable. The objectivity, for all the flack it has run into in modern critiques of the philosophy of the scientific method, remains very real.

Remember the different types of yes and no we looked at last week? This is similar. One person says the stick is short, another that it is of medium length. Each is honestly reporting what they experience from within the inner jungle of their prior contexts. The power of the maths is seen when they both agree the stick measures 11 inches.

So measuring things has this useful characteristic; an ability to demonstrate an aspect of things which can be agreed on by anyone of sound mind and body. The assertion that a stick is 11 inches long is quickly verified or falsified by anyone with a ruler, in any country at any time regardless of their political or religious beliefs, the weather outside or an infinity of other variables. Access to the stick in question and a ruler marked with the agreed upon (yet ultimately arbitrary) metric is all that is required.

It would not be off the mark to explain these last few centuries of scientific exploration as an ever more extensive and subtle scramble to learn how to measure the mysterious events that surround us. Eventually Chevelier de Mere, after a particularly bruising loss dicing with friends, wondered if there might be a way to measure the seemingly random. Asking his mathematically inclined friend Blaise Pascal to look at the problem sparked a fire that started the new branches of mathematics we today associate most closely with science; probability and statistics. Try to imagine the first time something as seemingly random as tossing a pair of dice began to show its generalized behavior; that it was not random in the aggregate, only in the individual throws.

Here was something new. Not a measurement that could be confirmed by someone else with a single reading but one that required reproducing a series of events. Additionally in any given series the actual outcome might differ from the predicted one but over enough trials the pattern emerges. Everything about this type of metric made its proper use, and properly understanding it, a bit tricky. Today we manage to work with these probabilities very effectively through the use of confidence intervals and margins of error. Probability is not as easy to use as a ruler but is just as objective and precise in its own way.

This act of measuring things can become surprisingly complex. The length of a shoreline depends on the scale of ‘ruggedness’ you choose, as Mandelbrot taught us. Length itself changes under relativistic conditions. But these are dwarfed by a more basic fact about measurements as they occur in the real world; few are in perfect accord with theoretical predictions. They are close enough, which is well defined, and this is good enough. It has to be, it is all we have to work with.

For example let us assume an experiment in electronic circuits. We are to measure the resistance in the circuit as per ohms law: resistance = voltage / current. A simple algebra formula gives the expected resistance in a circuit, say 9 ohms. Using a multimeter we carefully take the measurement and find 8.89 ohms. Build the same circuit a few more times and measure their resistances. Now maybe you find 9.20 and 8.922 and so on. This spread of measurements arises from the details of the actual, specific circuit being tested that are abstracted away in the simplicity of ohms law. The purity of the metal and the quality of the components are just two of the details that might be relevant in any particular case, there are thousands upon thousands of others.

With an actual measurement we encounter reality in all its uniqueness where more details, more evidence is included by the nature of the circumstances. Measurement is the bridge between theory and observation. It is writing a reality check. The data gathered will either conform to the expected result, increasing our confidence in the theoretical model or it will not. Given these spreads of observational data the question of just how close the value of the data needs to be to that predicted by theory and still be considered confirmation becomes critical. And it is just here that a funny thing happened on the way to the circus…

Turns out when you take a set of independent observations like this they disperse in that familiar pattern, the Bell Curve:

BellCurveDark blue is less than one standard deviation from the mean. For the normal distribution, this accounts for about 68% of the set, while two standard deviations from the mean (medium and dark blue) account for about 95%, and three standard deviations (light, medium, and dark blue) account for about 99.7%. (From http://en.wikipedia.org/wiki/Standard_deviation)


Regular readers will recognize the shape from last week. By including more and more of the evidence a spread of sorts arises. We are trying on systems thinking by including more and more of the relevant detail, training to sense the shape of questions and answers as they appear to us in the real world.

So what is a probability? First let’s get an intuitive grasp of the concept. The prolific, gentlemanly “Prince of Mathematicians” (Bell  1937) Carl Friedrich Gauss at one time concerned himself with the errors that accompany astronomical observations. He published a few comments that Laplace immediately recognized the importance of. Laplace developed them and laid the foundation for modern probability theory. An astronomer records the latitude and longitude of a star’s location. Each observation differs in each direction from previous observations by some amount. How then should we consider this situation? For centuries the concern was that the errors of each of the observations would multiply. Astronomers such as Tycho Brahe had been averaging the observations for centuries. They seemed to have discovered by empirical means that instead of multiplying out of control the errors seemed to cancel out. It was Gauss who gave the mathematical proof that this is indeed the case. In a small comment he derived what we today call the Gaussian – Laplace curve. Most everyone is familiar with this figure; it is the normal or bell curve ubiquitous throughout statistics. The families of such curves are referred to as probability density functions.

Instead of saying that the star is really at the mid-point this curve describes the spread of uncertainty inherent in the collection of observations. The actual position can be anywhere within the scope of the curve though each position entails differing degrees of probability. Here a probability is a measure of the uncertainty both of our measurements and our understanding of causes. Other times a probability might be measuring an objective characteristic of the external world as, for example when measuring radioactive decay. Probability as a distribution was an amazing insight that was to play a fundamental role in the evolution of modern quantum mechanics where probability waves are used as a model of atomic structure.

Concern with the size of errors in collected data is the field of sampling theory and its significance tests. The correct hypothesis is known – the position of the star as determined by many previous observations and my star chart. The question concerns the data. Are the observations I record with my new telescopic alignment indicating it is properly calibrated? This is the type of question that concerned the creators of probability theory in the 18th and 19th centuries. They wanted to capture what could be said about the data to be expected when randomly drawing from a sample population. This is familiar to anyone who has taken a course in statistics. Every course introduces the ubiquitous, if morbid, Urn; an Urn contains 50 white balls and 20 red, what are the chances of drawing at least one red ball if 5 balls are drawn from the Urn and not replaced? The hypothesis is known, the contents of the Urn, and what we want to know is the distribution of the evidence we can expect from sampling it.

Sampling is the only means available to investigate the enormous complexity of the biosphere. The richness of the specifically existing actual objects and relationships exceeds our grasp any other way. But the roots of probability run even deeper than that. Many of the neurophysiological processing algorithms our senses use seem to rely on probability as well. It is not just the measuring but that which is measuring too, both are intimately and inescapably entwined with probabilities. Perhaps the most well-known is how the human eye has a blind spot where the optic nerve passes through the eyeball yet we do not see a black spot, void of anything. Instead the networks of neurons involved in processing optic signals interpolates what it expects would be in the external environment if it could see in this spot and fills the spot in with pure imagination. The brain performs a fundamentally probabilistic operation, guessing what is most probably there where it cannot actually see. An Amazon reviewer of Vision and Brain: How We Perceive the World put it well when they wrote, “human vision is a highly efficient guessing machine.” Indeed some researchers find that the roots of probability run even deeper than our sensory processing all the way down into how our brains do what they do. Bayesian Brains: Probabilistic Approaches to Neural Coding provides an approachable overview for those interested in taking a deeper look.

It should be obvious why these matters are important to the concerns of this blog. The majority of the evidence about the ecological crises presents itself to us in terms of probability. The IPCC report on climate change includes detailed treatment of the terms it uses for dealing with uncertainty. It is worth a substantial quote:

“Three different approaches are used to describe uncertainties each with a distinct form of language. Choices among and within these three approaches depend on both the nature of the information available and the authors’ expert judgment of the correctness and completeness of current scientific understanding.

Where uncertainty is assessed qualitatively, it is characterised by providing a relative sense of the amount and quality of evidence (that is, information from theory, observations or models indicating whether a belief or proposition is true or valid) and the degree of agreement (that is, the level of concurrence in the literature on a particular finding). This approach is used by WG III through a series of self-explanatory terms such as: high agreement, much evidence; high agreement, medium evidence; medium agreement, medium evidence; etc.

Where uncertainty is assessed more quantitatively using expert judgement of the correctness of underlying data, models or analyses, then the following scale of confidence levels is used to express the assessed chance of a finding being correct: very high confidence at least 9 out of 10; high confidence about 8 out of 10; medium confidence about 5 out of 10; low confidence about 2 out of 10; and very low confidence less than 1 out of 10.

Where uncertainty in specific outcomes is assessed using expert judgment and statistical analysis of a body of evidence (e.g. observations or model results), then the following likelihood ranges are used to express the assessed probability of occurrence: virtually certain >99%; extremely likely >95%; very likely >90%; likely >66%; more likely than not > 50%; about as likely as not 33% to 66%; unlikely <33%; very unlikely <10%; extremely unlikely <5%; exceptionally unlikely <1%.”

We saw in an earlier post how calculus provided science with a useful set of tools for creating mathematical models of events in a world of constant change. Probability provides an equally critical foundation for modern science with methods that are needed for logically interpreting the meaning of data gathered. Through the use of the rigor only mathematics can provide a consensus has been reached for these numerical operations that are no less objective in principal than the one we found with the ruler measuring the 11 inch stick.

The Spectrum of Yes, No, and Maybe

“Realism does not mean that we are able to state correct propositions about the real world. Instead, it means that reality is too real to be translated without remainder into any sentence, perception, practical action, or anything else. To worship the content of propositions is to become a dogmatist. The dogmatist is someone who cannot weigh the quality of thoughts or statements except by agreeing or disagreeing with them.”
Weird Realism: Lovecraft and Philosophy, Graham Harman, italics in original


In our quest to apprehend the really real as best we might, careful reasoning is an indispensable ally. The reality of the ecological situation of the earth today presents itself to us in a collection of fairly complex observations contextualized to be meaningful within a fairly complex set of theories. Any medium which tends towards simplifying complex issues into sound bites will serve the hope of spreading the word about the intensity of the ecological crises poorly. Sound bites might deliver passionate rage but time and again we have seen historically these all too often become just so much sound and fury, signifying nothing. Conversely any medium that tends towards an appreciation of complexity and care in analysis can be a useful ally in the great work of spreading the word in such a way that people take the message to heart and change their lifestyles. Unlike the fading fashion of emotion dominated sound and fury, a certainty gained through rational analysis remains strong and inspiring despite the changes in the social weather.

As we consider the most probable future which the trends we see around us are bringing about and as we consider our response to this ignorance busy sawing off the very evolutionary limb on which we sit, a level of analysis is called for that respects the complexity of the task. Even with the utmost care in our crafting of theory and our collection of data the subject we are dealing with is the biosphere, an object far beyond our ability to completely capture in our models.

The problem we are trying to solve in both mindfulness and ecology is a lack of proper appreciation for the predicament we are in. The threats to what we hold dear are so dire and so irreversible, properly apprehended they force a person of goodwill to change their lifestyle. The majority of our friends and neighbors are not moved to make such voluntary adjustments because, at least in part, it is so easy not to see the full reality of the situation when you only consider it within a context of abstract generalities.

“Alas, the oceans are dying.”

Let’s consider the possible meaning behind such a statement as construed by two people, one a generalist and the other with a bit more specificity. The first in their moment of biophilia brings to mind a few sound bites and images of garbage in the ocean and maybe what happens to beaches during an oil spill. It is a generalized picture of a problem that fits comfortably beside concerns about tax hikes and every other story in the news. The second person knows about the garbage and the oil spills but they have read books and journals or watched in-depth documentaries and they also know that 90% of the large fish in the ocean are now gone, that acidification is proceeding apace, that acidification threatens the very base of the ocean food chain and scores of other aspects of the current state of the oceans. While still somewhat abstract, this view is less general. By adding more evidence a gripping specificity begins to form in the person’s understanding.

See it is very easy for us to lose sight of the richness of the actual while distracted by non-stop abstractions, generalizations. Compound this with the widespread nature deficit disorder and it leaves us ungrounded in facts as we make our choices both individually and as a society.

Of course the most specific is the actual ocean perceived by the senses. During these times concepts are seen to be much, much smaller than the thing-in-itself. The generalizations that our concepts encourage cannot capture the rich depths of existence within the actual ocean our senses encounter. Even though while standing on the shore the concepts are less, they still provide the context, the mental atmosphere by which these two differently educated persons experience their encounter. Widen this difference between these two by including the other sensory encounters with air, water, weather, forests, soils, biodiversity and mass extinction and the true extent of what is at stake here begins to appear a bit more clearly.

It is useful then to think about how we can take practical advantage of knowing the difference that makes a difference in changing people’s actions and study how to include a more evidence rich perspective. A model of how we come around to understanding and believing as we do would be quite helpful. We are going to explore a model that has full mathematical rigor by using a number of pictures and very few equations. The pictures are the more important part for our purposes.

Mindfulness practice is about changing the way one is aware of the world and all the wonders in it. With these pictures and this model an alternative is being developed to the typical attitude normally taken when considering people’s knowledge and beliefs. The domination of knowledge by experts unfortunately leaves the impression that simple answers are available if only we can find the right sources. It leaves the experience of our inner world of thinking as bare as a bureaucratic form with check boxes for yes / no or as barren as a multiple choice test with a single answer. This model and these pictures we are about to encounter are designed to replace this simplistic notion with one more attuned to the actual way a human being holds a position. The inner landscape is more like a jungle; vibrant with life, rich in patterns and shot through with interdependence, cooperation and mystery that reaches beyond what can be captured in conceptual thought without remainder.

I find that the model and the pictures that illustrate it aid my study. I think it can aid anyone on the contemplative paths where we care very much about what valid and invalid cognitions are. They also help in studying the sciences, becoming a more careful listener, and while striving to understand the news of the day and the behavior of my friends, neighbors and enemies. It is a fairly simple model far as these types of things go yet having it in the background has brought me all these benefits and more over the years. Your mileage may vary but all I can do as a writer is share those things that have worked most powerfully for me as a modern, western individual on the path. Without further introduction we jump now into a question and our first set of pictures:

Do you think the government is doing a good job?

Answer yes or no but either way I will ask, really? If you take the time to examine all the ways you think the government is or is not doing a good job isn’t it obvious your position is a bit more nuanced than a simple yes or no? Perhaps you oppose the wars of late and hate the Wall Street bailout but appreciate the general reliability of paved roads, that the water running out of your tap is clean enough to drink and the handiness of someone else dealing with your daily wastes.

How might we draw a picture of this more nuanced answer to the question, ‘Do you think the government is doing a good job?’ Instead of just the binary yes or no let us allow for a spectrum of responses. The spectrum will run from raging no to raving yes. Actually it will run from raging no to solid no through mostly no and slightly no before reaching the balance point and continuing from slightly yes through mostly yes and solid yes to raving yes:  YesNo1This is sufficient to capture the differences between the radical revolutionary and the super-patriot as well as all those whose opinion falls somewhere in between. The fact that the yes or no could be strong or weak is no longer lost in the simplicity of the binary yes or no. We are accounting for more of the actual evidence.

However, the bulk of the evidence is still not being accounted for. There is a whole other dimension, the vertical as it were. Government is an abstract noun, a generalization, an umbrella term for what are actually a number of different features and functions in the real world we can see and touch. In our attempt to think carefully about the role of government in our lives we work our way slowly across everything we have encountered, the whole laundry list of governmental functions. If we put a small block on our spectrum for each aspect of government before long the vertical dimension will also begin to express additional information. Here, for example might be the look of things after the person mentioned above placed the blocks for war and bailouts but then admitted the usefulness of un-poisoned tap water:  YesNo2_3blocksAs this process continues the analysis includes more and more features of government. Thought is given to details around foreign affairs, law and justice, keeping the peace, conducting elections, protecting civil liberties and all the other aspects of government that are relevant and important to any given persons’ analysis. For the sake of this introduction each person gets 16 blocks to place to express their answer to the question. Our example citizen opposed to war and bailouts but a fan of fresh tap water might end up something like this: YesNo3_NoWe can sum these details with a curve that captures more of this example of a middle of the road displeasure with the functioning of government then we could capture with the spectrum alone. Saying that everyone polled had a collection of 16 blocks to use is the same as saying mathematically the area under the curves needs to be the same. With the curves we gain the expressiveness of the detailed analysis in a streamlined form. What kinds of political positions do you expect owners of these curves to hold?  YesNoCurve3_avg YesNoCurve2_yesWhat the curves are capturing here are the differences in the spreads of various people’s opinions. Those with the basically wide patterned mental states expressed by the curves shown so far stand in stark contrast with either the super-patriot or the radical. These more extremes views bulk their blocks at one end of the spectrum. They have the same number of blocks, the area under the curve remains constant, but their curves are tall and narrow compared to what we have seen so far:  YesNoCurve4_NO YesNoCurve5_YESHow much agreement do you think the people represented by these last two curves are going to have?

Engaging in rational debate requires of both parties a commitment to an honest appraisal of our situation. Part of this includes the willingness of all involved to admit there may well be factors critical in the real world yet missing or misunderstood within our analysis. An intellectual humility is comfortable with that and instinctively understands why conversations bound to integrity necessarily include a background of probability. That is, we can say this or that is most likely or least likely, that this or that is almost certain, or almost certainly impossible. As we will see next week this is what we were capturing with our spectrum and curves between no and yes.

The intellectual position of the corporate shills denying anthropomorphic climate change, for example, rests on the most improbable interpretation of evidence imaginable. The model of reasoning we are going to develop over the next few posts, in my opinion, is the single most effective counter argument to those who are insisting business as usual can continue for another fifty years or so. By taking a careful look at their positions we find that they simply do not have a leg to stand on.

Come, let us reason together

Is there any book you wish all incoming freshmen at Harvard would read?
Kathryn Schulz’s “Being Wrong” advocates doubt as a skill and praises error as the foundation of wisdom. Her book would reinforce my encouragement of Harvard’s accomplished and successful freshmen to embrace risk and even failure.”
Drew Gilpin Faust, president of Harvard in N.Y.T.’s By the Book


The ability of humans to reason is Promethean. Some praise it as our gift from the Enlightenment and are sure it can better man and society. Others curse it as the trickster that lead to the cold-hearted blindness and hubris of the death camps. Some are sure the dignity of human beings lies in our ability to reason and others are sure it is nothing more than a tool of imperialism, empire and chauvinism. Reasoning has had a rough work-over by the philosophers as well. Hume’s problem of inference, for many people who care about such things, remains a terminal blow to the edifice of scientific method.

We are living in a Faustian age when engineering and science are serving as the repositories of our ultimate allegiance. We trust them to uncover the truth, reveal what is really real. As the mighty continue to fall and the public turns ever more against its current gods of corporate science and engineering another potential threat to our dignity makes its appearance. With the fall of the corporate, military-industrial complex’s research labs may come the witch hunts that fail to separate the (scientific) method from the (corporate) madness. We have already looked at threats to human dignity arising from the ecological crises when we examined the current circumstance of overshoot. Now we are going to start taking a look at this thing we call reason. If we are to successfully preserve what is valuable through the coming collapse, and I believe our advances in understanding reasoning is certainly worth preserving, it helps to define it carefully. Further, it is my contention that when defined well, reasoning carries with it its own defense of human dignity. There is grandeur in this view of mind, in this view of the inner world … as I hope to demonstrate over the next cycle of posts.

Wisdom entails seeing through delusions, seeing you were wrong about something. It is an astonishing experience to admit to being fully mistaken, completely wrong about a set of ideas that you had previously held dear. The spectrum of what we can be wrong about runs from the trivial to the very things we have dedicated our lives to. Consider the not uncommon case of someone who lives a religious life for 70 years and then comes to doubt its truth. This is an example of the extreme version of seeing through a delusion. The impact on the personality is shattering; life changes after that point, never to return again to the state of comfortable, easy belief. It is what some call an initiation.

That the deepest beliefs of a ‘self’ can be destroyed by another part of one’s ‘self’ opens a rich compost heap of fertile questions about just what this ‘self’ actually is.

What we are interested in here are valid cognitions; tools to separate truth from falsehood. Valid cognitions correspond to the environment; they capture an element of what is real about the external or internal worlds we find ourselves a part of. Valid cognitions are thoughts, concepts, and sets of ideas that have some degree of correspondence with the external or internal environment.

What is it that empowers us to see through our delusions? A whole host of psychological factors play into the details of just when and how such an undoing of delusions unfolds in any individual’s life but I am going to suggest that at its core all these experiences share a type of reasoning as their defining characteristic. The weight of evidence against the delusional set of beliefs grows, as it must since they are out of touch with what is real. Eventually we enter the realm where a choice must be made between cognitive dissonance, snapping and liberation. We are talking about changing the mind and like fire, it can harm or heal.

Removing mystification from the reasoning process clears the deck for our understanding one of the most profound yet taken for granted aspects of human experience. The existential core of our deepest questing after what is really real and truly true leads directly to a confrontation with a psychic power beyond our ability to manipulate – that which makes the real seem real. We have now come to the cornerstone of my philosophy; that which makes what seems real to me, to seem real to me, is the god within before whom I bow. It can be challenging to communicate clearly the felt sense that accompanies my understanding of this point. Though I just used theological language words are fundamentally inadequate. The philosophy of epistemology comes close as it studies how we know what we know but this too typically falls shy of the felt sense that accompanies the insights. It is why this blog site includes a poetics section.

There is a functional type of reality-sense operating in our sensory field of awareness (we know a hallucination to be a hallucination) and in our cognitive operations and classifications that cleanly divides the world between that which is real and that which is imagination. This reality-sense operates at multiple scales; there is not one monolithic, capital ‘T’ truth. So we have a sense for what is real at the atomic level of analysis, or molecular, cellular, that of ecosystems and so on. The reality-sense runs like a thread through our every conscious experience. It provides the contrast by which we recognize when we are dreaming. It provides the contrast by which we classify reality separately from illusion.

These two characteristics of reasoning are the proper context to appreciate the study of reason about to be undertaken. The first is that it can lead a person to change their mind, a most amazing thing (is this not what is being sought on a quest for enlightenment?) Second is that it is not a process that answers to our whims and fancies. We cannot make ourselves believe something we “know” to be fake and often we are powerless to maintain our most cherished beliefs in light of the evidence of our experiences however much we may want to, or even feel the need to in order to maintain our very sense of identity. It brings to mind Thomas Kuhn’s observation about science advancing only as the old guards of the previous generation die out. The paradigm change he describes in The Structure of Scientific Revolutions is all about us culturally changing our minds about what is real.

Now I think we can better appreciate the role of reason as it is more commonly understood. This ‘sense’ that separates for us what we will consider really real from what we will consider false is the engine of wisdom-building. We feed it with our curiosity. As curious creatures we try this and that, observe this and that and use the process to feed data into this reality-sense. We are driven to seek out what it needs to know to perform its vital role in our continual survival. We are fashioned in such a way that concerns about properly understanding our environments are vital. The aborigine on the walkabout and the scientist in their research lab are both gathering data within this type of process.

This newly acquired data does not exist in isolation. The data either confirms or disconfirms sets of ideas or beliefs the aborigine or scientist had before the event which provided new data occurred. These prior beliefs play a key role in the model of reasoning we will be exploring. It could be that we have a number of conflicting opinions about the truth of a matter without a justifiable preference for one or the other. This is not a lack of a prior belief but a special case where the prior beliefs contain the maximum possible entropy.

A funny feature of this reality-sense is that it has a reading on everything, even subjects we actually know little about. It is never the case of a blank slate confronting data since we always bring our current understanding with us. The role of data then is one of strengthening or weakening our previously acquired beliefs. Imagine a set of pans on a balance beam. On one side there is the weight of prior experience, study, thought, theory and data while on the other side the pan holds the new data to be assessed. There is a chance that even when the new data does not fit the prior set of ideas its weight will be sufficient to tip the scales. When the scales shift a new set of ideas are sensed as more true, we have changed our minds.

This is what Darwin did with his theory of evolution by natural selection; a gathering of evidence shifted the scales. Eventually it shifted the culture into the secular worldview we now live in where the need to appeal to a creator no longer enjoys the intellectual support it once did. This is also what the climate scientists’ warnings about climate change are doing right now alongside the whole host of ecologically educated producers of evidence for the ongoing eco-crises. There is value in working to share the truth as we understand it and letting the collective balance beams do their thing.

There is not a human alive who has not experienced being mistaken, believing something that is just not so. This universal experience opens the possibility that other beliefs being held with equal assurance may one day turn out to be equally delusional. In our heart of hearts we know this; it is a universal human experience. Humans can easily make vocal noises insisting they know something is absolutely true or absolutely false but since all assertions rely on a whole host of supporting ideas and we have seen where such ideas could be wrong, these claims to possess absolute knowledge are dishonest. They are dishonest both in what humans subjectively experience around the sense of what is really real and objectively in claiming a result reasoning is incapable of arriving at.

The key to thinking clearly about reasoning while giving proper weight to the characteristics just outlined is to frame our understanding within the ideas of probability. In a circumstance where absolute knowledge remains inaccessible (if not incoherent) the field is open to continual refinements of what can be considered to be most probably really real and most probably truly true. With probability we can have degrees of belief running between 0 and 100. I can be 80% sure the Declaration of Independence under glass in Washington D.C. is the original document and maybe 99.9% sure that if I drop my pen right now it will fall to the floor. Why not 100% sure in this later case? Maybe there will be an explosion nearby the moment I drop my pen and its trajectory gets blasted sideways or a cosmic gravity wave from a sudden inflationary black hole alters the surrounding space-time or maybe even my mischievous friends tied a very thin thread to my pen so when I drop it they can laugh with glee as I stare at it, astonished. The point is: with the idea that complex human understanding entails degrees of belief the absolute false of 0 and the absolute truth of 100 are traded for a wealth of possibilities. Importantly it also provides an effective means by which people can persuade one another about what is real and what is not.

Learning to speak in terms of what is most probable could renew the moribund national conversation. Learning to habitually frame our debates in such contexts could return dignity and respect to our interactions. Regardless of the probability of probability impacting social norms, individuals can benefit from adding this tool to their cognitive tool belt. A transparent, coherent and complete model of such a core constituent of our makeup as reasoning promotes a certain peace of mind and an easier acceptance of the human condition. It also fine tunes our B.S. detectors.

There is another point. The ethical question of our time comes couched in terms of probability: What is the most probable future facing humankind? The corollary is also couched in terms of probability: What can we do today that has the greatest chance of making tomorrow better?