Plato as a Game Theorist towards an International Trade Policy
Bibliographic record
Abstract
In the beginning of the second book of his Politeia (Republic) Plato in passage 2.358e–359a–c raises the issue of the administration of justice as a means of motivating people to behave fairly regarding their relationships and when cooperating with each other because, at the end, this is mutually beneficial for all of them. We argue that this particular passage could be seen as a part of a wider process of evolution and development of the institutions of the ancient Athenian economy during the Classical period (508–322 BCE) and could be interpreted through modern theoretical concepts, and more particularly, game theory. Plato argued that there are two players, each with two identical strategies, to treat the other justly or unjustly. In the beginning, each player chooses the “unjust” strategy, trying to cheat the other. In this context, which could be seen as a prisoner’s dilemma situation, both end with the worst possible outcome, that is, deceiving each other and this has severe financial consequences for both of them. Realizing this, in a repeated game situation, with increasing information on the outcome and on each other, they choose the “just” strategy so achieving the best outcome and transforming the game in a cooperative one. We analyze this, formulating a dynamic game which is related to international commercial transactions, after explaining how such a situation could really arise in Classical Athens. We argue that this is the optimal scenario for both parties because it minimizes the risk of deceiving each other and creates harmony while performing financial transactions.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.009 |
| 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".