Bibliographic record
Abstract
Business divorce is an arduous affair. The process of resolving deadlocks is time consuming and expensive, typically requiring the services of lawyers, financial experts and judges. Prolonged resolution processes, cost-inefficient administration of those processes, and inequitable outcomes impose high monetary and non-monetary costs on the parties themselves and on society as a whole. Asset valuation, which is required to complete the transfer of assets in a business divorce, can pose particular problems for closely-held businesses.In contrast to publicly-traded companies with active markets for equity ownership, closely-held companies may be very difficult for outsider investors and appraisers to evaluate. The economic value of closely-held businesses is often intertwined with the human capital of the founders, their relationships with business associates (including key suppliers and customers), and their tacit business knowledge. The true economic value of closely-held businesses may not be fully reflected in the official business documents and financial statements; instead, the best wisdom concerning the value of the business may lie in the minds of the business owners themselves. This article studies business deadlocks and their resolution. We advance a proposal to reform the way that courts resolve business deadlocks and value business assets. Specifically, we argue that Shotgun mechanisms, where one owner names a single buy-sell price and the other owner is compelled to either buy or sell shares at the named price, should play a larger role in the judicial management of business divorce. Since the party proposing the offer may end up either buying or selling shares, the party has an incentive to identify and name a fair price. Thus, accurate asset valuation is achieved without the use of outside appraisers or inefficient public auctions. We also show that our proposal is aligned with current statutory rules and case law. General partnerships and limited liability companies (LLCs), the most commonly chosen legal entities, are the focus of this study. Important lessons and insights for the judicial resolution of business deadlock are derived from our analysis of the private design and implementation of Shotgun provisions. Although Shotgun provisions have the potential for achieving equitable, expedient, and cost-efficient outcomes, these mechanisms pose challenges in private contractual settings, including the risk of opportunistic behaviors by owners who are at an informational or financial advantage. We argue that these risks are less severe in the judicial context than they are in the private context. Since courts have the ability to design the Shotgun procedure ex-post rather than ex-ante, they are in a better position to identify the presence and nature of the asymmetries and to tailor the mechanism accordingly. Although our arguments regarding the benefits of ex-post judicial design of Shotgun mechanisms are logically consistent and supported by current legal cases, actual field data on the use of these mechanisms is not available. To begin to fill this void, we conducted a series of controlled laboratory experiments with human subjects to assess whether the Shotgun mechanism will have the predicted effects. Our experimental design simulated a deadlocked business venture with two owners where only one of the two owners knew the true value of the business assets. Two different treatments were considered. In the first treatment, the better-informed owner was compelled to make buy-sell offer; in the second treatment, the less-informed owner was compelled to make the buy-sell offer. Our experimental findings support our arguments: (1) Inequitable outcomes arose when the less-informed owner made the buysell offer, and (2) equitable outcomes were obtained when the better informed owner made the buy-sell offer. Specifically, when obligated to make a buy-sell offer, the better-informed owner truthfully revealed his private i
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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 | 0.021 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 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".