Non-hierarchical signalling: two-stage financing game
Bibliographic record
Abstract
The literature analyzing games where some players have private information about their "types" is usually based on the duality of "good" and "bad" types (GB approach), where "good" type denotes the type with better quality. In contrast, this paper analyzes a signalling game without types hierarchy. Different types have the same average qualities but different profiles of quality over time which are their private information. We apply this idea to analyze a financing-investment game where firms' insiders have private information about the firm's profit profile over time. If transporting cash between period is costless equilibrium is pooling with up-front equity financing. Otherwise equilibrium is either pooling with debt when the economy is stagnating, or separating when the economy is growing (some firms issue debt and some firms issue shares). This provides new theoretical results that cannot be explained by the standard GB models and which are consistent with some financial market phenomena.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".