Irreconcilable Differences: Judicial Resolution of Business Deadlock
Bibliographic record
Abstract
This article studies the judicial resolution of business deadlock. Asset valuation, a necessary component of business divorce procedures, can pose serious problems in case of closely-held businesses such as general partnerships and limited liability companies (LLCs). Courts face the challenge of designing valuation mechanisms that will trigger the owners to truthfully reveal their private information. We theoretically and experimentally assess the ex post judicial design and properties of judicially-mandated Shotgun and Private Auction mechanisms. In the former mechanism, the court would require one owner to name a buy-sell price, and the other owner would be required to either buy or sell his or her shares at the named price. In the latter mechanism, the court would mandate both owners to simultaneously submit a price to buy the other owner's assets. Our experimental findings support our theory: The Shotgun mechanism with an informed offeror is superior to the Private Auction in terms of an equity criterion. In the Shotgun mechanism, the informed offeror has an incentive to truthfully reveal his private information and, as a result, an equitable outcome is more likely to be achieved. The analysis presented in this article provides an equity rationale for the judicial implementation of the Shotgun mechanism in business divorce cases, and demonstrates the empirical feasibility of our proposal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".