Cyclic Rationality
A methodology for grounded open-mindedness. Qualitative dreaming before quantitative verification. Recovering imagination.
A methodology for grounded open-mindedness.
Buddy Williams · October 16, 2025
I am gripped by the simple yet profound idea that understanding is simply prediction. Coupled with Shannon entropy, it’s an explosion of insights that I know will stay with me for years and somehow become core to how I view the world.
Introduction
I was a creative and curious kid, taking things apart and trying to put them back together. I still remember the time my dad showed me a relay. I knew this was the component I needed for my tree house security system on my grandparents’ farm. My childhood was a bit of a disaster. The root cause was my two warring families (mom vs. dad). The world didn’t make sense; it was a scary, violent place for me. Reflecting on those early years, it’s clear to me now that I craved a sense of control and had the curiosity and drive to pursue it. I left home at an early age. I missed out on traditional school and college and pursued the path of the self-taught. Again, looking back, I can see how this was fortunate. The modern era didn’t shape my mind; it was shaped by ancient philosophers, biographies, and logicians of the 1600s-1700s. They thought differently back then. The world still didn’t make sense, but slowly I began to work it out: religion, philosophy, psychology, culture, politics, economics, computer science, and artificial intelligence. I was obsessed with macro analysis. I wanted to understand reality, to control it, and make it a safe yet surprising place. My craving for control was, in hindsight, a craving for predictability, a microcosm of humanity’s universal drive to reduce entropy.
Closed-minded
The idea for Cyclic Rationality came to me during my reading of Bernoulli’s Fallacy by Aubrey Clayton. I was starting to see a problem with the way the rationalist community was thinking, fundamentally. I saw Effective Altruism, Less Wrong, and AI safety researchers, along with X-risk/S-risk communities, making numerous projections about various topics, especially in AI, that seemed unreasonable. These grand narratives didn’t add up for me. Moreover, I noticed my peers were not open-minded. I had many exploratory ideas, and when I would discuss them with others, I was met with blank faces. I was baffled. Why couldn’t they see what I was seeing? Their questions were always so restrictive and closed. So, I set off on a journey to understand the difference between our thinking. From this pursuit, several key ideas emerged: Elephant-rider analogy [from Carl Jung and Jonathan Haidt], mind viruses [how ideology spreads and controls via cognitive weakness], and Cyclic Rationality, described below.
To understand why this narrowness feels so suffocating, we need to trace how modern reason evolved.
Death of Delusion
Before the 1500s, the rule of the day was delusion. This was just before Galileo and his contemporaries. Then the scientific revolution exploded. The new rule of the day was evidence. The idea that we should test our beliefs was the new rule of the day. Support for delusion died among the educated elites. No longer did people seriously entertain wild stories. A modern example of this is Stephen Wolfram, whose NKS claims were vast, yet unverifiable due to the limits of reductionist verification for complex systems. Although Stephen’s ideas have not been proven empirically, reality may very well be computational.
Bifurcation of Reason
Post the scientific revolution, a bifurcation started where the humanities and the sciences were split. When subjectivity became suspect, imagination became an indulgence rather than an instrument of discovery. In The Two Cultures, C. P. Snow argued that the growing division between science and the humanities was a major handicap in solving the world’s problems.
The bifurcation was not absolute, but it was significant enough to cause the problem I was seeing with my peers. They couldn’t seem to speculate or hold thought experiments for any length of time. Subjectivity was seen as a non-starter. There are many reasons for this: industrial-era education, specialization, and the explosion of formal models. Logic was no longer practical; it was formal. The world was on fire with progress, so surely nothing is wrong?
What We Lost
When I look at this picture, from delusion to evidence, I ask what we lost? It seems clear that people have largely lost the ability to do creative thinking. We can number crunch with the best of them, but how good are our dreams? If you want to be inspired, you are better off reading a sci-fi or fantasy book than attending MIT. For the majority of human history, we dreamed. We told stories. We made up unbelievable crap, but at least we dreamed. I started to realize that we lost a significant ability to explore the space of possibilities.
Mission
So, I set out to formalize a new way to think, a new rationality model. A way that embraces our ancestral roots, that most precious capability, to recapture our imagination! I’ve called it Cyclic Rationality. Below, I’ll lay out its beliefs and how to practically use it. It doesn’t dismiss science or measurement; instead, it seeks to place it in a better, more helpful place so that we can unlock our ability to dream anew.
Thesis
Reality is complex and emergent.
The universe self-organizes through local interactions that generate unpredictable global behaviors. Complexity means the whole cannot be reduced to its parts without loss (see: cellular automata, chaos theory, computational irreducibility).
Reality exhibits bounded predictability.
Some things are predictable, others are not. The placement of the line between the two is where mistakes occur. Predictions [based on projections] are the result of idealizing problems [simplifying while retaining the interesting parts]. These reductions always omit interactions with other phenomena, resulting in map–territory relation fallacies.
All models are incomplete.
Every description of reality is a reduction. Just as no formal system can be both complete and consistent, no model can fully capture its territory (see Gödel’s incompleteness). The scientific method, though powerful, is a bottom-up reductive tool. Complex systems also require a top-down inclusive perspective to capture complexity [see ants, human brains, and our immune system].
Human understanding is the result of learning predictive patterns.
Predictability makes people feel safe because it enables control. We seek to forecast the future, yet there is no known pattern underlying all reality. We benefit from maintaining a position of epistemic humility. (see Shannon entropy)
Two Rationalities: qualitative and quantitative.
Qualitative thinking explores possibility space [all possible combinations of information]. Quantitative thinking measures and verifies, grounding ideas in empirical evidence, shielding us from delusion. Qual without Quan produces delusional-dreamers; Quan without Qual produces smart-idiots [sterile reductionism].
Qualitative reasoning should precede quantitative verification.
Insight begins with imaginative synthesis by bravely stepping into the unknown. If we prioritize quantitative verification, our ability to explore the unknown is inhibited. We must remain open to discovering new methods of measurement that expand our epistemic reach.
Cyclic Rationality unites the two.
Cyclic Rationality is the disciplined alternation between imaginative expansion and empirical contraction, the rhythm of discovery itself. We dream up possibilities and try to verify them, understanding that we may not always succeed.
Conclusion
This article could be easily expanded into a book with far more nuance and specificity. We could map out the history of thinking methods. We could show that creativity never actually died; it was merely blunted and discouraged. The point of Cyclic Rationality is to help us do better.
I hope that when you encounter someone you disagree with, maybe someone who is extra woo-woo, you’ll exercise your imagination. In my experience, I often disagree with the methods they used, but I usually find remarkable insight in their conclusions.
I hope that as you think about the future, you’ll exercise your imagination to design, rather than quantify, exceptionally inspiring visions. I find it more inspiring to be a pioneer than to be a mere explorer; we really can do both.
If understanding is prediction, then imagination is the courage to predict the impossible.