Why Did Putin Invade Ukraine? Why Did Hamas Attack Israel? Answers Suggested by Economic Models of Dictatorship and Terrorism
Bibliographic record
Abstract
Abstract In this paper I use rational economic models to explain the origins of two recent wars, the invasion of Ukraine by Russia in 2022 and the Hamas attack on Israel in 2023. In both cases the literature has prominently featured two different kinds of motivations for the attacks, one offensive and one defensive. Did Russia invade Ukraine because it was afraid that Ukraine was getting too strong and too close to NATO and Russia was weakened by this? Or did it invade because Putin thought that the West was weak, particularly after the botched withdrawal from Afghanistan? Did Hamas attack Israel on October 7th because it was afraid that the other Arab countries were about to abandon the Palestinian cause and thus forever weaken it? Or did its leaders believe they could advance the Palestinian cause with a successful attack and withstand the Israeli response, having built a vast system of tunnels with money from Qatar, and possibly counting on help from Hezbollah and Iran? I address these questions in this paper with economic models. I use the models – of dictatorship and terrorism – developed in my previous work, to provide answers to these questions. In both cases, the models favor the offensive explanations of the attacks as opposed to the defensive ones. Russia attacked Ukraine because it thought it could win easily. Hamas attacked Israel because of its newfound strength.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".