Rise of the War Machines: Charting the Evolution of Military Technologies from the Neolithic to the Industrial Revolution
Bibliographic record
Abstract
The causes and consequences of technological evolution in world history have been much debated. Of particular importance in many of the theoretical and empirical studies on this topic is innovation in military technologies, details of which are comparatively well preserved in the archaeology and historical record and which are often seen as drivers of broad socio-cultural processes. Here we analyze data on the evolution of key military technologies in a stratified sample of the world’s political systems from the Neolithic to the industrial revolution using Seshat: Global History Databank. Empirically testing a series of previously speculative theories reveals that world population size (as proxy for the potential numbers of innovators), the connectivity between areas of innovation and adoption, and major past innovations such as iron metallurgy and horse riding, all serve as strong predictors of change in military technology. We discuss how the approach showcased here could be extended not only to explain more of the causes and consequences of military innovation but of technological change more generally, with important ramifications for our understanding of the drivers of world history and of the evolution of social complexity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".