The Travelerâs Dilemma and its Backward Induction Argument
Bibliographic record
Abstract
This thesis is an examination of the traveler’s dilemma and its backward induction argument. I begin by explaining relevant terminology, the prisoner’s dilemma, and the iterated prisoner’s dilemma; the discussion of which aids my examination of the traveler’s dilemma and its backward induction argument. \nMy evaluation of the traveler’s dilemma involves a dissection of the game into its different components, a presentation of the salient similarities and differences between the traveler’s dilemma and the prisoner’s dilemma, and the exploration of three possible solutions. The first two solutions are adapted from ones initially created to solve other backward induction argument problems. The third solution is original and its foundation rests on the unique structure of the traveler’s dilemma. I focus on this third solution and consider several objections to it. \nI end this thesis with some ancillary comments about the possibility of generalizing the third solution to other backward induction argument problems.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".