Truthful Cheap Talk: Why Operational Flexibility May Lead to Truthful Communication
Bibliographic record
Abstract
This paper shows that operational flexibility interacting with informational uncertainty may lead to truthful information exchange in equilibrium even when the communication is nonbinding and unverifiable, i.e., “cheap talk.” We consider a model consisting of a manufacturer releasing a new product with uncertain release date and demand, and a retailer who must determine the allocation of limited capacity between a preexisting third-party product and the manufacturer’s new product that may or may not be released on time. The manufacturer has a private forecast about the likelihood of the product release and/or about the demand, which he shares (either truthfully or not) with the retailer. We show that under the “traditional” supply chain structure (one-time opportunity to order) no truthful equilibrium can emerge. However, if (1) the supply chain structure allows for postponement, i.e., the ability to delay orders at a certain cost by the retailer, and (2) the manufacturer has informational uncertainty about the retailer’s capacity, then truthful information exchange may emerge in equilibrium, where the manufacturer transmits his true forecast and the retailer treats the transmission as truthful. The genesis of this effect is preference reversal, where the manufacturer is not sure which way to distort the forecast to best motivate the retailer to wait for the new product. Thus, we show that a truth-revealing mechanism can emerge from a relatively rich setup featuring two-sided information asymmetry interacting with postponement. This paper was accepted by Gad Allon, operations management.
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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 | 0.016 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".