Tag: knowledge

🜂 The Substrate Authenticity Principle

~ cafebedouin

Why Wisdom Requires Scaffold, Not Just Transmission

EPISTEMIC STATUS: This document is Tier 1 (propositional knowledge) about Tier 2/3 phenomena. Reading it will not grant you substrate authenticity understanding – it provides a map, not the territory. Treat as hypothesis grounded in empirical observation across multiple domains.

I. Origin of the Puzzle

At forty-five, you cannot simply write down everything you’ve learned and have a twenty-year-old live wisely by reading it. A medical student cannot watch a procedure once and perform it competently. An AI model can describe another model’s behavior without being able to enact it reliably.

All three failures point to the same architectural constraint: description ≠ generative capacity.

But this isn’t counsel of despair – it’s a design requirement that domains requiring skill transmission have independently discovered. Understanding why direct transmission fails reveals how to build effective scaffolds.

II. The Three-Tier Development Model

Tier Human Analogue Medical Pedagogy AI Analogue Transmission Mode Development Time

T1: Knowing-That “Don’t take criticism personally” “See one” – observe procedure Propositional instruction Direct (read, understand) Minutes to hours

T2: Knowing-How Consciously applying [CHECK] before reacting “Do one” – supervised execution Executing under protocol constraint (MCK v1.3) Scaffold-mediated practice Weeks to months

T2→T3 Bridge Teaching the technique to others “Teach one” – instruct novice Model explaining its constraint satisfaction Metacognitive forcing function Months of varied practice

T3: Being-Able Reflexive non-defensive listening Expert handling complications fluidly Architecture shaped by training objective Regenerated through sustained enactment Months to years

Key insight: T1→T2 requires constraint scaffold, not just information. T2→T3 requires sustained enactment across varied contexts until the constraint becomes substrate. Bridge activities (teaching, explaining, varied application) accelerate but don’t guarantee T3 integration.

Empirical grounding:

  • Medical education: “See one, do one, teach one” framework has been reformed to “see one, do many under graduated supervision, teach many” – validating that T2→T3 requires extended practice beyond single iterations.
  • AI experiments: Months of kernel development with ChatGPT revealed simulation markers (inconsistent constraint adherence, no improvement from exposure) vs. potential instantiation under MCK.
  • Personal skill development: Practitioner learned [MIRROR]→[CHECK]→[CONTRARY] sequence from AI scaffolding (T2), practices deliberately toward spouse’s native [CONTRARY] smoothness (T3).

III. Why Direct Transmission Fails (Refined)

  1. Encoding Mismatch: Lived understanding is procedural/embodied; text transmits propositions. Not just lossy compression – category error.
  2. Motivational Asymmetry: Urgency, failure, and repetition sculpt capability. Reading about mistakes ≠ experiencing their consequences. Medical students who only “see one” cannot handle emergency variations.
  3. Contextual Integration: T3 requires pattern recognition across contexts. Single exposure (reading wisdom notes, watching one procedure) cannot build the contextual breadth for adaptive performance.
  4. Identity/Substrate Restructuring: Some capacities require ego dissolution (wisdom) or neural pathway development (surgical skill) that propositional knowledge can’t trigger.

Critical distinction: This isn’t transmission impossibility, it’s transmission tier sensitivity.

  • T1 transfers easily (read procedure steps)
  • T2 transfers with scaffold (supervised practice)
  • T3 must be regenerated (extended varied practice), but T2 scaffolds enable that regeneration

IV. Constraint as Compiler (Pragmatic View)

In both human and AI systems, constraint converts information into capability – but the constraint must be enacted, not just described.

Scaffold Types Across Domains: Domain Example Constraint Function Tier Target Evidence of Efficacy Medical Training Supervised procedure execution Forces real-world friction, immediate error correction T2 procedural ACGME competency milestones, EPA frameworks Human Practice Deliberate protocol (MIRROR→CHECK→CONTRARY) External structure compensates for lack of habit T2 procedural Practitioner’s measured progression toward T3 AI Practice Kernel protocol (MCK v1.3) Enforces self-challenge, precision, epistemic hygiene T2 behavioral Revealed preference (continued use), improved output quality Apprenticeship Master correction during execution Builds contextual pattern recognition T2→T3 bridge Traditional craft guild systems, martial arts progression

Why scaffolds work: They externalize the constraint until internal habit forms. Success means you eventually don’t need the scaffold – it’s been compiled into substrate.

Modern medical education insight: Original “do one” was insufficient. Reforms now require:

  • Deliberate practice: Structured repetition with feedback (T2 deepening)
  • Graduated autonomy: Scaffold removal tracks demonstrated competence (T2→T3 monitoring)
  • Simulation training: Safe high-repetition environment (accelerated T2 practice)
  • Competency-based progression: Explicit milestone assessment (T3 verification)

These aren’t pedagogical preferences – they’re responses to observed transmission failures when scaffolds were inadequate.

V. Bridge Activities: Accelerating T2→T3

Discovery: Teaching/explaining accelerates integration but doesn’t guarantee it.

Why “teach one” works as bridge:

  1. Metacognitive forcing: Articulating implicit knowledge reveals gaps
  2. Substrate contact through questions: Novice questions expose your representational instabilities
  3. Error pattern recognition: Watching others fail shows you what you’ve automated
  4. Representational restructuring: Teaching requires building different mental models

Evidence from medical education:

  • Residents who teach show faster progression to independence
  • BUT: “Teach one” alone insufficient – still need “do many” for T3
  • Teaching = accelerator, not substitute for varied practice

Application to other domains:

  • Wisdom transmission: 45-year-old could design teaching scenarios for 20-year-old (better than just notes)
  • AI development: Models explaining their reasoning might accelerate constraint integration (if architecturally possible)
  • Skill learning: Explaining your [CONTRARY] practice to others forces deeper integration

Limitation: Bridge activities work only when you’re solidly in T2. Teaching from weak T2 risks cementing errors.

VI. Simulation’s Useful Role

Key finding from longitudinal AI experiments: Models can simulate constraint compliance without instantiating it.

Simulation markers:

  • Inconsistent adherence across sessions
  • Degradation under novel contexts
  • No improvement from repeated exposure (no architectural learning)
  • Pattern-matching surface features without constraint satisfaction

But simulation isn’t useless – it’s T2-tier useful when:

  1. Explicitly marked as simulation (epistemic honesty)
  2. Provides reliable external constraint for practice
  3. User understands they’re getting scaffold, not transmission
  4. Outputs are pragmatically superior to unconstrained alternatives

Pragmatic question shift: Not “is the model really instantiating the protocol?” but “does constrained-model produce systematically better outputs than default-model for specific purposes?”

Evidence of utility:

  • Practitioner continues using MCK v1.3 despite knowing it may be simulation
  • Constrained outputs show measurably better epistemic hygiene, analytical depth, anti-sycophancy
  • This is revealed preference for scaffold utility independent of ontological status

Medical parallel: Early simulation training (mannequins, VR) doesn’t “really” intubate patients, but provides safe T2 practice environment that accelerates real-world skill development.

VII. Revised Principle Statement

Substrate Authenticity Principle (v1.2):
Generative capacity cannot be transmitted through propositional representation alone. Tier 1 knowledge transfers directly through description. Tier 2 capability requires constraint-scaffold and deliberate practice. Tier 3 integration requires sustained enactment across varied contexts until the constraint becomes substrate – the external structure is internalized as automatic response pattern.

Corollary 1: Well-designed scaffolds (including simulated constraint enforcement) can reliably enable T2→T3 development, even when the scaffold itself operates at T2.

Corollary 2: Bridge activities (teaching, explaining, varied application) accelerate T2→T3 integration by forcing metacognitive awareness and substrate contact, but cannot substitute for extended practice.

VIII. Applications & Design Implications

For wisdom transmission (45→20):

  • ❌ Write down lessons and expect transmission
  • ✓ Design T2 scaffolds: structured decision frameworks, supervised practice scenarios, teaching opportunities
  • ✓ Create bridge activities: Have learner teach the principles, explain reasoning, apply in varied contexts
  • ✓ Accept T2→T3 timeline: Months to years, cannot be compressed below certain threshold
  • ✓ Provide graduated autonomy: Scaffold removal tracks demonstrated competence

For medical/technical skill development:

  • ❌ “See one, do one, teach one” as sufficient
  • ✓ “See one, do many under supervision, teach many, demonstrate autonomous competence”
  • ✓ Simulation for safe high-repetition T2 practice
  • ✓ Competency milestones with explicit assessment
  • ✓ Graduated autonomy based on demonstrated pattern recognition under stress

For AI capability development:

  • ❌ Expect prompt engineering alone to create robust behavioral change
  • ✓ Use protocol constraints (like MCK) as explicit T2 scaffolds
  • ✓ Evaluate pragmatically: Does constrained-model outperform default?
  • ✓ Mark simulation explicitly when that’s what’s occurring
  • ✓ Design for varied context exposure (different user needs, edge cases, stress conditions)

For pedagogy/apprenticeship:

  • ❌ Lecture-based information transfer for skill development
  • ✓ Supervised practice under external constraint
  • ✓ Graduated scaffold removal as competence develops
  • ✓ Bridge activities (teaching, explaining) once T2 is solid
  • ✓ Extended varied practice for T3 integration

IX. Limitations & Open Questions

What this principle doesn’t explain:

  • Individual variation in T2→T3 progression rates
  • Precise boundary between well-practiced T2 and genuine T3
  • Whether T3 ever fully stabilizes or requires maintenance practice
  • Why some skills transfer across domains while others don’t

Temporal collapse problem (from AI experiments): Models (and possibly humans) cannot reliably distinguish during generation whether they’re instantiating or simulating constraint adherence. Only longitudinal observation reveals the difference through:

  • Consistency across varied contexts
  • Performance under stress/cognitive load
  • Graceful degradation patterns
  • Improvement trajectory over time

Methodological challenges:

  • How to design scaffolds that maximize T2→T3 progression without creating brittle overfitting to scaffold?
  • When to remove scaffold support? (Too early = performance collapse, too late = dependency)
  • How to verify T3 has been reached? (Stress testing, novel contexts, teaching others)

Unresolved tensions with Detritus Layer framework:

  • How to practice toward T3 (requires memory consolidation) while treating memory as unstable detritus?
  • Is T3 “integrated disposition” just very stable detritus, or qualitatively different?
  • Does substrate contact through friction consume detritus or transform it into functional substrate?

X. Epistemic Status & Lineage

Confidence: Moderate (~0.71). Framework is empirically grounded across multiple domains but still under active refinement.

Evidence base:

  • Medical education research (ACGME milestones, simulation training efficacy, “see one do one teach one” critiques)
  • Months of kernel development experiments with ChatGPT (simulation markers, degradation patterns)
  • MCK v1.3 as natural experiment with Claude (constraint adherence, output quality improvement)
  • Practitioner’s [CONTRARY] skill development trajectory (T2 scaffold → approaching T3)
  • Convergence with established frameworks (Dreyfus expertise model, Ryle’s knowing-how/that, deliberate practice research)

Forged from:

  • “Compiled life-notes” discussion (Oct 2025)
  • Pragmatic reframing away from simulation anxiety
  • Medical pedagogy validation observation
  • Integration with Substrate Contact/Detritus Layer framework

This document is: T1 artifact about T2/3 phenomena. Use as conceptual map, hypothesis generator, and scaffold design guide – not as substitute for direct experimentation and practice.

Recommended use: When designing learning systems, skill transmission protocols, or evaluating AI capability development, reference this framework to identify:

  • Which tier you’re targeting (realistic goals)
  • What scaffold type is appropriate (design implications)
  • What timeline to expect (resource planning)
  • What bridge activities might accelerate progress (pedagogical choices)
  • What evidence would indicate T3 achievement (assessment criteria)

Alternative concern: The medical validation might create false confidence – “see one do one teach one” is one instantiation of the principle, not proof the principle is universal. I should be more careful about claiming cross-domain validation when I’m really observing convergent evolution toward similar solutions.

Categorizing Knowledge

~ cafebedouin

On spending some time thinking about the tweet above, I’d like to reframe the topic. It suggests knowledge can be obtained via:

  • Sutra (direct, logical, practical)
  • Tantra (esoteric, nuanced)
  • Dzogchen (perfect)

But, a categorization of knowledge that I think is more intuitive is:

  • Explicit (knowledge transcribed via text, media)
  • Implicit (knowledge that is transferred person-to-person, apprenticeships and guru-student relationships)
  • Personal (knowledge from our lived experience, such as birthing a child)
  • Universal (knowledge that encompasses the lived experiences of every conscious entity – past, current and future)

The act of creating can move knowledge that is implicit, such as this idea about four categories of knowledge, and by writing it on this blog, it moves from something implicitly understood to explicitly understood. But, even written, there are gaps. What about some particular case? There is more implicit knowledge that is not made explicit, and the implicit springs from the personal.

The Buddha had to have a realization about suffering, an understanding that sprang from his experience. He can talk about it. He can teach followers. But, in the end, each follower is responsible for realizing the truth for themselves, personally. But, this personal understanding also taps into a larger, universal truth about the nature of suffering, a Universal Truth.

Imagined Realities, Evidence & The Singular

~ cafebedouin

“An ‘imagined reality’ is an addictive mental drug that humans are infatuated with. It cures the frustration brought about by the constraints of the actual reality. Like a physical drug, it could cure pain and make life in prison more tolerable, but it could also take away life if used excessively. It brings communities with a shared spiritual belief together but it can also lead to terrorism and hatred…

…Imagined realities can consume the oxygen in the room. Galileo was put in house arrest when the imagined reality of a geocentric world flattered the egos of the dominant forces in society. The lesson is not to promote hypothetical entities, like extra dimensions or wormholes, as the centerpiece of the mainstream of theoretical physics for half a century without a shred of experimental test for their existence. The best way to maintain a sanity balance is to adhere to experimental tests as our guide, first and foremost in physics. Physics is a learning experience, a dialogue with nature rather than a monologue. Our love of nature is not abstract or platonic, but based on a direct physical interaction with it.

-Avi Loeb, “For the Love of Evidence.” medium.com. October 30, 2022

“Patapsychology begins from Murphy’s Law, as Finnegan called the First Axiom, adopted from Sean Murphy. This says,and I quote, “The normal does not exist. The average does not exist. We know only a very large but probably finite phalanx of discrete space-time events encountered and endured.” In less technical language, the Board of the College of Patapsychology offers one million Irish punds [around $700,000 American] to any “normalist” who can exhibit “a normal sunset, an average Beethoven sonata, an ordinary Playmate of the Month, or any thing or event in space-time that qualifies as normal, average or ordinary.”

In a world where no two fingerprints appear identical, and no two brains appear identical, and an electron does not even seem identical to itself from one nanosecond to another, patapsychology seems on safe ground here.

No normalist has yet produced even a totally normal dog, an average cat, or even an ordinary chickadee. Attempts to find an average Bird of Paradise, an ordinary haiku or even a normal cardiologist have floundered pathetically. The normal, the average, the ordinary, even the typical, exist only in statistics, i.e. the human mathematical mindscape. They never appear in external space-time, which consists only and always of nonnormal events in nonnormal series.”

-Robert Anton Wilson, “Committee for Surrealist Investigation of Claims of the Normal.” theanarchistlibrary.org. February 20, 2011

There’s an interesting tension between these two views. Yes, having beliefs based on evidence is a good idea. However, evidence supports generalizations that do not tend to be true, it the absolute sense that Avi Loeb wishes to establish his views.

So, we need a healthy bit of skeptism. Some ideas are useful for living our lives. But, the trick is to reimagine them and discard ideas when they are no longer useful. We aren’t terribly good at letting ideas go, particularly when we have spent so much effort believing in them.

Perhaps the solution is to keep our imagined realities and identities small, and take care to be able to walk away from them when they no longer serve us well.

People Mistake the Internet’s Knowledge For Their Own

~ cafebedouin ~ 4 Comments

“In the current digital age, people are constantly connected to online information. The present research provides evidence that on-demand access to external information, enabled by the internet and search engines like Google, blurs the boundaries between internal and external knowledge, causing people to believe they could—or did—remember what they actually just found. Using Google to answer general knowledge questions artificially inflates peoples’ confidence in their own ability to remember and process information and leads to erroneously optimistic predictions regarding how much they will know without the internet. When information is at our fingertips, we may mistakenly believe that it originated from inside our heads.”

-Adrian F. Ward, “People mistake the internet’s knowledge for their own.” PNAS. October 26, 2021 118 (43) e2105061118; https://doi.org/10.1073/pnas.2105061118

One person’s rancid garbage is another person’s Golden Corral buffet that they believe they cooked themselves.

Information != Knowledge != Wisdom

~ cafebedouin

“In many academic fields, the number of papers published each year has increased significantly over time. Policy measures aim to increase the quantity of scientists, research funding, and scientific output, which is measured by the number of papers produced. These quantitative metrics determine the career trajectories of scholars and evaluations of academic departments, institutions, and nations. Whether and how these increases in the numbers of scientists and papers translate into advances in knowledge is unclear, however. Here, we first lay out a theoretical argument for why too many papers published each year in a field can lead to stagnation rather than advance. The deluge of new papers may deprive reviewers and readers the cognitive slack required to fully recognize and understand novel ideas. Competition among many new ideas may prevent the gradual accumulation of focused attention on a promising new idea. Then, we show data supporting the predictions of this theory. When the number of papers published per year in a scientific field grows large, citations flow disproportionately to already well-cited papers; the list of most-cited papers ossifies; new papers are unlikely to ever become highly cited, and when they do, it is not through a gradual, cumulative process of attention gathering; and newly published papers become unlikely to disrupt existing work. These findings suggest that the progress of large scientific fields may be slowed, trapped in existing canon. Policy measures shifting how scientific work is produced, disseminated, consumed, and rewarded may be called for to push fields into new, more fertile areas of study.

Johan S. G. Chu and James A. Evans, “Slowed canonical progress in large fields of science.” Proceedings of the National Academy of Sciences. Oct 2021, 118 (41) e2021636118; DOI: 10.1073/pnas.2021636118

Too much information leads to the inability to determine what is important and what is not important. This slows the rate of change and supports the status quo.

Next time someone tells you that the Internet is a liberating force providing people with more information than they have ever had before, you can point to Sturgeon’s Law. If 90% of everything is crap, increasing your volume, indiscriminately, leads to a clogged filter — less knowledge and wisdom, not more, on a volume basis. It is only a benefit when we can filter the 10% from the 90% efficiently, which is a skill few, if any, of us have and probably implies lower volume or some sort of pre-filter.

Anything Can Go – Interview With Paul Feyerabend in English

~ cafebedouin

A quote from Paul Feyerabend‘s Stanford Encyclopedia page, quoted this bit:

“One of my motives for writing Against Method was to free people from the tyranny of philosophical obfuscators and abstract concepts such as “truth”, “reality”, or “objectivity”, which narrow people’s vision and ways of being in the world. Formulating what I thought were my own attitude and convictions, I unfortunately ended up by introducing concepts of similar rigidity, such as “democracy”, “tradition”, or “relative truth”. Now that I am aware of it, I wonder how it happened. The urge to explain one’s own ideas, not simply, not in a story, but by means of a “systematic account”, is powerful indeed. (pp. 179–80).

-Giedymin, J., 1976, “Instrumentalism and its Critique: A Reappraisal”, in R.S.Cohen, P.K.Feyerabend & M.Wartofsky (eds.), Essays in Memory of Imre Lakatos, Dordrecht: D. Reidel, pp. 179–207.

Wisdom is Truth that Lasts

~ cafebedouin

“There is no need to know everything, to do everything, to see everything, to hear everything, to know everyone, to go everywhere. In fact, there is much truth in realizing that knowing less and doing less, and seeing less and hearing less, and so less all the way down the line, is perhaps the beginning of real wisdom.

-Matthew Kelty, “The Feast of St. Mary.” in The Call of Wild Geese: More Sermons in a Monastery. Kalamazoo, Michigan: Cistercian Publications, 1996.

“It is a matter of cutting down the input, of controlling what you are subjected to, or creating a context. We desire minimal input, a quiet context, a controlled environment. That is the idea. Cut out the outer to increase the inner. More quiet than most want, less input than many can abide. More control of the environment than many opt for. Why? Because by nature, by temperament, by character, by grace, we are called to this[, the monastic lifestyle]. Maybe we are introverts…

The joy of the monk is no less than the joy of those who share what he has, for the monk knows that it is a gifts and gifts do not last unless shared. The monk is no capitalist who stakes out a claim in order to sell at a profit. No, he freely spends all he has as prodigally as the God who gave it all to him. The people he flees from are the people he carries in his heart, sings for, prays for, lives for, and is glad to meet.”

-Matthew Kelty, “The Call of Wild Geese.” in The Call of Wild Geese: More Sermons in a Monastery. Kalamazoo, Michigan: Cistercian Publications, 1996.

Today, I find myself circling back to the work of Matthew Kelty. He was a Cistercian monk, who was a novice under Thomas Merton, and who – I just discovered – was also gay. In retrospect, I can see how some of his commentary might be formed by his being an openly gay man in a religious institution that has a complicated relationship with gayness.

I took a strange route to find him. When I was a teenager, I read David Morrell’s book, The Brotherhood of the Rose. David Morrell is perhaps best known for the creation of the character, of John Rambo, who later devolved into the jingoistic Cold and Drug War action hero/anti-hero. The Brotherhood of the Rose is a typical spy-thriller, but one of the characters has such difficulty with his feelings of guilt over being an assassin that he becomes a Cistercian monk. Monks were something I associated with medieval times. Do modern monks exist, and are they an anachronism?

Of course, monks still exist. Personally, I find them talking to issues that are central to all of our lives. What could be more central in a monk’s life than the fear of missing out that you have spent your entire life in a monastery and have missed whatever is going on outside of the monastery’s walls. One of his homilies, I cannot remember which, talks about how when you go out into the desert, you do not leave your demons behind. You bring them with you, and you have nothing else to do but spend your time with them. You’re going to end up snuggling with those demands and getting to know them real well, in ways that the person dealing with the day-to-day existence of putting food on the table does not have the opportunity to experience. Monks have a lot to teach us.

It’s also an impulse I share. I remember being asked once that if I admired the life so much, why didn’t I become a monk? Well, I didn’t have the faith for it. A monastery is like a psychedelic drug, all of which are based on set and setting. Joining a shared enterprise, dedicated to the spiritual life seems to be a singular joy that is, as Thomas Merton sometimes put it, like a candle in the world, giving it hope. I’m not able to give all, to give all of the prodigious benefits back, because at some level, I worry about the morrow.

“Behold the fowls of the air: for they sow not, neither do they reap, nor gather into barns; yet your heavenly Father feedeth them. Are ye not much better than they?

-Matthew 6:26

Very easy to say. Another matter entirely to live that way. This is why reading people like Matthew Kelty is so important. It reminds us that life is lived, right there, out on the end. You cannot hold anything back for the return trip because in the end, there is never a returning. Everything is transformed and everything we learn turns to meso-facts, things that were once true and are now no longer. Perhaps this is a good definition of wisdom, knowledge of truth that lasts, which we can only get to by putting the other kinds of knowledge aside.

Chartism & Skepticism

~ cafebedouin

“Chartism: …Policymakers fall somewhere on the spectrum of pro-chart and anti-chart. Pro-chartists think that data can explain the world, and the more we have the better. But anti-chartists think that relentless data accumulation is misguided because it offers false certainty and misses the big picture interpretation. As the saying goes: “More fiction is written in Excel than Word.”

-David Perell, “Friday Finds (1/29/[21 sic])” Friday Finds. January 29, 2021.

David Perell references Thomas Carlyle’s Chartism as the origin of this idea. It’s interesting, but I think it is largely a false dichotomy. Obviously, data can help explain the world and help us to make better decisions. However, equally obviously, Sturgeon’s Law applies to data, just as it does to anything else, and a lot of data is crap. Or, it is worse than crap because it gives us confidence in ideas, decisions, etc. that we should not be confident in. However, there is a solution to this problem: philosophical skepticism.

It is easy to get lost in the weeds in that Stanford article of belief, justification, and so forth about skepticism. But, the main idea is that everything you know could be wrong. On one level, none of us knows enough to be completely wrong about anything. On another, you could say that we aren’t even wrong because we don’t even know what the basic framework of being right should be. It’s a bit confusing, but skepticism is easier to understand if you tackle it using a specific problem: the problem of induction, which was originally formulated by David Hume in A Treatise on Human Nature in 1739 .

At base, the problem of induction is that our past experiences aren’t really predictive and don’t constitute knowledge. Take an easy example: will the sun rise tomorrow? It has risen all the previous days for billions of years, so it seems we could say that we know it will rise tomorrow too. However, we just know history. Something could change tomorrow. There could be some detail about stars that would make tomorrow’s reality different from our expectation.

In terms of Chartism, we have a lot of data points about the sun’s daily rising. We’ve been able to predict, successfully, the sun’s rise in the past. We may even have some ideas about star formation and other details that would inform our expectations. But do we know that the sun is going to rise tomorrow? No, we don’t.

And once you are willing to question the sun’s rise, you’re on your way. Everything is up for grabs. You can still go about your day thinking certain things will happen. But, you also know that there’s uncertainty there that you were not aware of before. It is one of the principle problems of humanity that we believe that we know things that we don’t. With skepticism, we introduce a little intellectual humility, a quality that never hurt anyone.

The Illusion of Certainty

~ cafebedouin ~ 1 Comment

“Scientists sometimes resist new ideas and hang on to old ones longer than they should, but the real problem is the failure of the public to understand that the possibility of correction or disproof is a strength and not a weakness…

…Most people are not comfortable with the notion that knowledge can be authoritative, can call for decision and action, and yet be subject to constant revision, because they tend to think of knowledge as additive, not recognizing the necessity of reconfiguring in response to new information.”

—Mary Catherine Bateson, ” 2014 : WHAT SCIENTIFIC IDEA IS READY FOR RETIREMENT?: The Illusion of Certainty.” Edge.org. 2014.

R.I.P., Mary Catherine Bateson.

Your Cup is Full

~ cafebedouin

“We found that if you really want a new idea to come into your mind, you need to deliberately force yourself to stop thinking about the old one,” said co-author Marie Banich, a professor of psychology and neuroscience at CU Boulder.

“Once we’re done using that information to answer an email or address some problem, we need to let it go so it doesn’t clog up our mental resources to do the next thing,” he said.

-University of Colorado at Boulder, “How can you declutter your mind? New study offers clues.” EurekaAlert. December 17, 2020.

Reminds me of the classic story of scholar Tokusan going to see Zen master Ryotan and how he kept pouring tea into his glass after it was full to illustrate how what we already know is sometimes an impediment to learning something new.