Modeling threats and promises: Explaining the Munich crisis of 1938
Bibliographic record
Abstract
Abstract The use of an incomplete information game model to explore the strategic characteristics of the carrot and stick approach to coercive diplomacy shows that the dynamics of this manipulative bargaining tactic are much more nuanced than standard atheoretical accounts suggest. One unexpected finding is that when information is incomplete, there always exists a deterrence equilibrium under which no attempt is made to overturn the status quo. An all-out conflict or an unsuccessful fait accompli is also possible, but only when information about preferences is not common knowledge. Incomplete information, then, is a double-edged sword, sometimes enhancing the prospects for peace and at other times making conflict more likely. We use a special case of the Carrot and Stick Game model to shed theoretical light on the Munich crisis of 1938, a manufactured crisis if there ever was one. Hitler’s last-minute about-face was motivated by his newfound belief that the British, French, and Czechs intended to resist his planned military invasion of the Sudetenland and his preference to avoid an all-out war. While his preference was unchanged in 1939, his beliefs were not; as our model suggests, the consequences were more than predictable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".