Managing Financial Risks while Performing International Commercial Transactions. Intertemporal Lessons from Athens in Classical Times
Bibliographic record
Abstract
In this paper, we mainly focus on two institutional aspects that are related to financial risk, that is, profiteering and the use of non-fraudulent coins when performing financial transactions. We argue that these two prerequisites were important for the success of the commercially oriented economy of the Athenian state in comparison with its allies in the East Mediterranean during the classical period. In particular, we briefly explain the structure of the Athenian economy, and then we focus on the agoranomoi and the dokimastai, the two main financial institutions related to (i) measures against profiteering and (ii) ensuring the purity of the currency when performing commercial transactions. Then, following a game theoretical approach, we provide a fictional example as to how the two institutions functioned in practice. Our findings confirm that these institutions were crucial in reducing financial risk when performing international commercial transactions, since they provided symmetrical information on the quality and purity of the currencies circulating in the Athenian economy. In the case of the Athenian state, we further convey that measures against profiteering and the use of unadulterated currency comprise intertemporal axioms, in the sense that their importance is not merely a phenomenon of modern times, but rather, on the contrary, one that dates back to much earlier times.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".