Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.