Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,021 | 0,052 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,010 | 0,032 |
| Communication savante | 0,009 | 0,017 |
| Science ouverte | 0,003 | 0,014 |
| Intégrité de la recherche | 0,008 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».