违反协商协议:损害赔偿的比较分析

Frank Giaoui
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引用次数: 1

摘要

传统上,评估合同违约的经济损失和补偿性损害赔偿面临两个实际困难:司法的不确定性和技术的复杂性。在缺乏客观数据的情况下,而在有资料的情况下,目前的财务和统计方法过于复杂或昂贵,司法紧张程度就特别高。为了减少效率低下的议价、不必要的诉讼和不确定的司法判决,需要一种既符合事实又比目前的定量方法更简单的替代方法。本文从人身损害原则出发,假设可行的评估方法包括为某些经济损失制定损害赔偿表。它利用在美国和法国谈判或达成协议时违反企业协议的情况来说明这一点。在回顾了数百个合同案例的抽样数据后,本文强调了过去25年来看似对立的判例法的趋同,以此作为标准化损害赔偿方法的起点。实证分析表明,原告结果与索赔数量、证据复杂程度、商业风险和律师事务所规模之间存在很强的相关性。基于这些结果,本文为寻求提高其成功机会的各方制定了实用建议。它描述了实现统计表示所需的额外经验分析的基础。将损害赔偿时间表与人工智能相结合,将产生预测决策支持系统,以评估获得损害赔偿的可能性和这些损害赔偿的数量。这将引发一个良性循环:帮助法官做出自由裁量的决定,提高预测模型的准确性,从而为所有利益相关者提供更多使用这些模型的激励。因此,它们的使用将简化诉讼程序,并最终为社会创造超出今天想象的价值。
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Breaches of Agreements to Negotiate: A Comparative Analysis of Damages
Assessing economic loss and compensatory damages for contract breaches traditionally navigates between two practical difficulties: judicial uncertainty and technical complexity. Judicial tension is exceptionally high when objective data is missing, and when information exists, current financial and statistical methodologies are too complex or costly. To reduce inefficient bargaining, unnecessary litigations, and uncertain judicial decisions, there is a need for alternative methods that are both factual and simpler than current quantitative methods. This paper takes from the personal injury doctrine to posit that viable assessment methods include the development of damages schedules for certain economic losses. It uses breaches of corporate agreements to negotiate or to agree in the US and France to illustrate so. After reviewing data sampled from several hundred contract cases, this paper highlights a convergence of seemingly opposed case laws over the last 25 years as a starting point for a standardized damages methodology. The empirical analysis shows strong correlations between plaintiff outcomes and claims quantum, evidentiary levels of sophistication, business risk, and law firm size. Based on these results, this article formulates practical suggestions for parties seeking to improve their chances of success. It delineates the groundwork for additional empirical analysis needed to achieve statistical representation. Using damages schedules combined with artificial intelligence would give rise to predictive decision support systems that assess the probability of obtaining damages and the quantum of those damages. This would trigger a virtuous cycle: assisting judges in their discretionary decisions, and improving the accuracy of predictive models, thus, giving more incentives for all stakeholders to use them. Hence, their use would streamline litigation and eventually generate value for society beyond what can be imagined today.
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