100 Horsemen and the empty city: A game theoretic examination of deception in Chinese military legend
Bibliographic record
Abstract
Abstract We present game theoretic models of two of the most famous military bluffs from history. These include the legend of Li Guang and his 100 horsemen (144 BC), and the legend of Zhuge Liang and the Empty City (228 AD). In both legends, the military commander faces a much stronger opposing army, but instead of ordering his men to retreat, he orders them to act in a manner consistent with baiting the enemy into an ambush. The stronger opposing army, uncertain whether it is facing a weak opponent or an ambush, then decides to flee and avoid battle. Military scholars refer to both stories to illustrate the importance of deception in warfare, often highlighting the creativity of the generals’ strategies. We model both situations as signaling games in which the opponent is uncertain whether the general is weak (i.e. has few soldiers) or strong (i.e. has a larger army waiting to ambush his opponent if they engage in combat). We then derive the unique Perfect Bayesian Equilibrium of the games. When the probability of a weak general is high enough, the equilibrium involves mixed strategies, with weak generals sometimes fleeing and sometimes bluffing about their strength. The equilibrium always involves the generals and their opponents acting as they did in the historical examples with at least a positive probability. When the probability of a weak general is lower (which is reasonable given the reputations of Li Guang and Zhuge Liang), then the unique equilibrium always involves bluffing by the general and retreat by his opponent.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".