What makes tarot and periodic tables resist meaningful scientific integration?
This reads the question as being about symbolic classification systems — why a grid like tarot floats free of science while the periodic table fuses with it — and I should flag up front that the corpus is about AI reasoning, not tarot or chemistry, so the synthesis is lateral: it speaks to *when symbol systems lock onto reality and when they just rearrange tokens.*
This explores why some symbol-grids (the periodic table) merge into science while others (tarot) stay sealed off — and the honest first thing to say is that nothing in this collection studies tarot or chemistry directly. What it does have is a deep seam on the same underlying problem: when does a system of symbols actually grip the world, and when is it just well-formed shuffling? Read that way, the corpus has more to offer than you'd expect. The sharpest doorway is the argument that computation always presupposes an 'experiencing mapmaker' who carves continuous physics into discrete symbols Can computation arise without a conscious mapmaker?. Both tarot and the periodic table are exactly such carvings — someone alphabetized a messy continuum into named cards or numbered cells. The difference is what the symbols are answerable to. The periodic table's cells were forced to predict undiscovered elements and got corrected when they were wrong; tarot's cards answer only to other cards.
That distinction — form versus genuine grip — shows up vividly in the finding that logically *invalid* chains of reasoning perform almost as well as valid ones Does logical validity actually drive chain-of-thought gains?. A system can carry all the outward structure of inference — steps, transitions, a confident grammar — while the structure, not any real inferential validity, is doing the work. That's a precise description of why tarot resists integration: it has rich internal form (suits, spreads, correspondences) and none of it is load-bearing against an external test. The periodic table's form, by contrast, is parasitic on a real regularity it didn't invent.
The collection also complicates the easy verdict that 'more formal = more scientific.' Full formalization can actually destroy the thing you were trying to capture: partial symbolic abstraction beats both plain language and total formal logic, because complete formalization strips out semantic information that the looser representation preserved Why does partial formalization outperform full symbolic logic?. This suggests the resistance isn't simply that tarot is 'too symbolic' — it's that a symbol system integrates with science only when its abstraction is anchored to something it can be wrong about. And there's a related warning that structure can be real or merely metaphorical: reasoning topologies map to *actual* computational graph types, not decorative analogies Can reasoning topologies be formally classified as graph types?. The periodic table is a real graph of a real periodicity; tarot is a metaphorical one.
So the lateral answer the corpus points to: a classification grid integrates with science not because it's orderly, symbolic, or even predictive-looking, but because its symbols were minted by a mapmaker who left them exposed to correction by a world outside the grid. Tarot keeps its symbols inside the deck. The thing you might not have known you wanted to know is that AI reasoning research keeps rediscovering this same fault line — form that mimics validity, formalization that loses meaning — which is really a general law about symbol systems, not a fact about cards or elements.
Sources 4 notes
Computational systems depend on a conscious mapmaker who alphabetizes continuous physics into discrete symbols. No increase in algorithmic complexity can generate this agent; it must logically precede the computation it makes possible.
Illogical chain-of-thought exemplars matched valid CoT performance on BIG-Bench Hard, showing that structural properties—not logical validity—drive the gains. The model learns the form of reasoning, not genuine inference.
QuaSAR and Logic-of-Thought both achieve 4-8% accuracy gains by enriching natural language with selective symbolic elements rather than replacing it. Full formalization loses semantic information; pure language lacks structure. Augmentation preserves both.
CoT, ToT, and GoT map precisely to path graphs, trees, and arbitrary directed graphs respectively. The topology is not metaphorical but defines actual computational structure—GoT's in-degree > 1 enables divide-and-conquer synthesis that trees cannot express.