Human Transport Theory — Neolithic builders quarried and deliberately hauled the stones from Preseli to Stonehenge.
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Glacial Transport Theory — Glaciers during a past ice age moved the stones part or all of the way, leaving them for humans to collect.
The debate is often framed qualitatively, but here ChatGPT put the competing hypotheses into a statistical model comparison using the Akaike Information Criterion (AIC). This approach allows us to quantify which theory better explains the total body of evidence — and to measure the strength of that preference.
ChatGPT chose and researched the criteria without my input,to avoid any accusations I have biased the test. For instance I would have included the recent Bevins et al Newall Boulder paper which it doesn't appear to do so. The prompt used was: "Using Akaike Information Criterion (AIC) evaluate the Human vs the Glacial Transport Theories for the bluestones at Stonehenge" and then a further prompt to "dig deep and run a realistic calculation". The text and analysis is by ChatGPT.
AIC in Brief
AIC evaluates models by balancing:
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Goodness of fit (how well the model explains the data)
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Parsimony (penalising models with more free parameters)
For a model :
Where:
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= number of estimated parameters
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= maximum likelihood of the model given the data
Lower AIC indicates the better balance of fit and simplicity. A ΔAIC greater than 6 is typically considered strong evidence for the lower-AIC model.
Building the Models
ChatGPT treated each transport theory as a predictive model:
Human Transport predicts:
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Specific source outcrops match the bluestones.
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Evidence of quarrying at Preseli.
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No glacial erratic trail into Salisbury Plain.
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Dates of extraction and placement coincide with human activity.
Glacial Transport predicts:
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Erratics from Preseli present in southern England.
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Geological signs of glaciation in Wessex.
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Stones represent a random glacial mix.
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No need for extensive Neolithic transport infrastructure.
The Dataset
ChatGPT compiled 10 published, peer-reviewed observations from the last two decades. Examples:
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Geochemical fingerprinting of rhyolitic debitage at Stonehenge ties it to Craig Rhos-y-felin, and dolerite matches Carn Goedog — two very specific Preseli sources.
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Quarry-like features with Neolithic radiocarbon dates have been reported (though contested).
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Surveys of ~50,000 Avon terrace pebbles found no extra-basinal erratics.
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The last British–Irish Ice Sheet never reached Salisbury Plain; no local glacial deposits are known.
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The “Newall boulder” from 1924 excavations matches Preseli lithology and is argued not to be a glacial erratic.
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Strontium isotopes of cremated remains at Stonehenge reveal individuals from the Preseli region.
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Counter-evidence: some geologists interpret at least one Stonehenge boulder as glacial, and others argue quarry “features” may be natural.
Assigning Likelihoods
For each observation, ChatGPT estimated the likelihood that such evidence would be seen if the model were true. For example:
Observation | P(Human) | P(Glacial) |
---|---|---|
No erratic trail to Stonehenge | 0.85 | 0.05 |
Quarry-like features at Preseli | 0.80 | 0.30 |
One boulder possibly glacial | 0.35 | 0.65 |
Multiplying the probabilities across all ten observations gives the model likelihood. With (no parameters estimated from the data), AIC reduces to .
ID | Observation | P(Human) | P(Glacial) | ln P(Human) | ln P(Glacial) |
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E1 | Rhyolitic debitage at Stonehenge matches Craig Rhos-y-felin (specific Preseli outcrop). | 0.9 | 0.3 | -0.10536 | -1.20397 |
E2 | Spotted dolerite matches Carn Goedog; quarry-like features & dates ~3350–3000 BC reported. | 0.8 | 0.3 | -0.22314 | -1.20397 |
E3 | Avon terrace survey (~50k pebbles) found no extra-basinal erratics near Stonehenge. | 0.85 | 0.05 | -0.16252 | -2.99573 |
E4 | No glacial deposits on Salisbury Plain; Devensian limit far away; Anglian evidence absent locally. | 0.8 | 0.2 | -0.22314 | -1.60944 |
E5 | The 'Newall boulder' from the 1924 excavation geochemically tied to Craig Rhos-y-felin; argued non-erratic. | 0.75 | 0.25 | -0.28768 | -1.38629 |
E6 | Strontium isotopes of cremations show some individuals from West Wales (Preseli region). | 0.6 | 0.4 | -0.51083 | -0.91629 |
E7 | Preseli lithologies concentrated at Stonehenge; no 'erratic train' between Preseli and Wessex. | 0.85 | 0.1 | -0.16252 | -2.30259 |
E8 | Bluestones include multiple distinct Preseli lithologies traceable to specific sources (non-random mix). | 0.8 | 0.4 | -0.22314 | -0.91629 |
E9 | Peer-reviewed critique argues quarry 'features' may be natural (reduces strength of quarry claim). | 0.4 | 0.6 | -0.91629 | -0.51083 |
E10 | Peer-reviewed paper promotes glacial-transport reading of at least one Stonehenge boulder. | 0.35 | 0.65 | -1.04982 | -0.43078 |
Results
Model | ln L | AIC | ΔAIC | Akaike Weight |
---|---|---|---|---|
Human Transport | −3.86 | 7.73 | 0.00 | 0.9999 |
Glacial Transport | −13.48 | 26.95 | 19.22 | 0.0001 |
Interpretation:
ΔAIC ≈ 19 is far beyond the usual “strong evidence” threshold. The Human Transport model overwhelmingly outperforms the Glacial Transport model given the current dataset. The Akaike weight (~99.99%) can be read as the probability that the Human model would be selected as the better among these two, if the analysis were repeated with similar data.
Robustness Check
ChatGPT ran a conservative sensitivity test, reducing the likelihoods for Human-leaning evidence and increasing those for Glacial-leaning evidence. Even then:
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Human: AIC = 11.17, weight = 0.983
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Glacial: AIC = 19.25, weight = 0.017
The ranking stayed the same, and the Human model retained decisive support.
Why the Human Model Wins
The statistical edge comes from multiple, converging lines of evidence:
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Tight geological matches to specific Preseli outcrops.
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Lack of supporting glacial deposits or erratic trails in the region.
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Archaeological connections between Preseli and Salisbury Plain during the relevant time frame.
While some uncertainties remain — especially regarding the nature of “quarry” features — the overall pattern fits human agency much more closely than glacial happenstance.
Conclusion
Applying AIC reframes the bluestone transport debate from a narrative clash to a quantitative test. On the present evidence, the Human Transport theory is not just slightly better — it is orders of magnitude more likely than the Glacial Transport theory to explain the Stonehenge bluestones.
(Grok produced similar numbers when asked the same question and was given the same freedom to research and score)
Rerunning the test with the fully referenced evidence table from https://www.sarsen.org/2025/08/evidence-for-glacial-transport-theory.html gave this:
Results:
Model | ln L | AIC (k=0) | ΔAIC | Akaike weight |
---|---|---|---|---|
Human transport | −1.625 | 3.25 | 0.00 | ~1.0000 |
Glacial transport | −18.971 | 37.94 | 34.69 | ~0.00000003 |
Interpretation:
With ΔAIC ≈ 34.7, the Human Transport theory is overwhelmingly preferred over the Glacial Transport theory on this dataset — the Akaike weight is essentially 100% in favour of human transport.
For ΔAIC = 34.7: exp(−0.5×34.7)=exp(−17.35)≈2.9×10−8exp(−0.5×34.7)=exp(−17.35)≈2.9×10−8
This is so close to zero that, statistically, you can be extremely certain the higher AIC model is not the best.