Market forces meet behavioral biases: cost misallocation and irrational pricing
Why this work is in the frame
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Bibliographic record
Abstract
Psychological and experimental evidence, as well as a wealth of anecdotal examples, suggests that firms may confound fixed, sunk, and variable costs, leading to distorted pricing decisions. This article investigates the extent to which market forces and learning eventually eliminate these distortions. We envision firms that experiment with cost methodologies that are consistent with real‐world accounting practices, including ones that confuse the relevance of variable, fixed, and sunk costs to pricing decisions. Firms follow “naive” adaptive learning to adjust prices and reinforcement learning to modify their costing methodologies. Costing and pricing practices that increase profits are reinforced. In some market structures, but not in others, this process of reinforcement causes pricing practices of all firms to systematically depart from standard equilibrium predictions.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it