An analysis of paperclip arbitrage
Bibliographic record
Abstract
While bartering is arguably the world's oldest form of trade there are still many instances where it surprises us. One such case is the remarkable story of Kyle MacDonald who, by means of a sequence of bartering exchanges between July 2005 and July 2006, managed to trade a small red paperclip for a full sized house in the town of Kipling Saskatchewan. Although there are many factors to consider in this achievement, his feat raises basic questions about the nature of the trades made and to what extent they are repeatable by others. Furthermore, it raises issues as to whether such events could occur in Agent–based Electronic Environments – and under what conditions. In this paper we provide an intuitive model for the type of trading environment experienced Kyle and study its consequences. In particular the work is focused on understanding whether such trading phenomena require altruistic agents to be present in the environment and under what conditions agents can reach their individual goals. Results cover both the case of a single
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".