Ask A Genius 1451: Fuzzy Logic, Quantum Thinking, and the Brain’s Probabilistic Mind
Author(s): Rick Rosner and Scott Douglas Jacobsen
Publication (Outlet/Website): Ask A Genius
Publication Date (yyyy/mm/dd): 2025/07/13
Scott Douglas Jacobsen and Rick Rosner delve into fuzzy logic as a model for non-binary truth, linking it to quantum mechanics, computational theory, and how the brain processes incomplete information. Rosner suggests fuzzy logic reflects how humans intuitively simulate the world—through probabilistic, context-sensitive frameworks—not rigid, rule-based systems.
Scott Douglas Jacobsen: So, let us start with the easier ones—fuzzy logic. Degrees of truth. Modelling vagueness. Instead of binary categories like hot or cold, fuzzy logic allows gradations: hot, warm, cool, cold—continuous ranges of truth. You can build systems with values like true, false, maybe; or true, false, indeterminate; or even true, false, indeterminate, and meaningless.
A meaningless question would be something like Chomsky’s famous example: “Do colourless green ideas sleep furiously?” Grammatically correct, but semantically meaningless. It has a syntactic structure, but no coherent content.
So fuzzy logic systems can be three-valued, four-valued, or even infinitely valued. The point is that truth is not always binary—there is often a spectrum. That is the core insight.
What are your thoughts on fuzzy logic?
Rick Rosner: Well, I always end up circling back to quantum mechanics. Quantum mechanics is the mathematics of what you can do informationally when you do not have complete information, and that is fuzzy logic.
I remember when fuzzy logic started gaining attention back in the 1970s. People were excited—it opened up new possibilities. Moreover, sure, it probably did. However, ultimately, it is still quantum-adjacent. It all ties back to quantum physics.
Quantum computing, for example, deals with information structures that are not binary. It creates multivalued systems—not in terms of true/false, but in terms of superposition and parallelism—little multi-worlds where many possibilities are computed simultaneously.
Take the travelling salesperson problem. Say a salesperson has to visit 10 cities. What is the most efficient route? That problem is computationally brutal with classical computers. You have to test all possible routes. As you go from 10 cities to 12 to 20, the computational load explodes.
There is a term from computational theory—P vs NP—that covers how fast problems scale. Moreover, this one scales fast. It is an NP-hard problem.
However, quantum computing can “unexplode” it. It can run multiple possibilities at once using quantum parallelism. That is the trick—it lets you solve otherwise intractable problems more efficiently.
Still, it is quantum mechanics. It is just the math of incomplete information applied powerfully.
Moreover, it is possible that evolution found similar shortcuts in the brain. Our minds do not explicitly compute every scenario. We operate with tacit knowledge. We simulate reality based on fuzzy, probabilistic frameworks, not strict rule-based logic.
So these systems—fuzzy logic, multivalued logics, and ordered degrees of truth—they reflect how we think. We often operate with semi-truths. “This is more true than that.” That is how our brains work.
All of that could be modelled with quantum mechanics—it falls under the umbrella of information theory. The problem is, our information theory is still incomplete.
We have yet to understand the contexts in which information exists entirely. Most of the time, we assume the context is obvious—so obvious that we do not even recognize it as a requirement. However, context shapes meaning, and we tend to overlook that entirely.
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