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Actavis And Error Costs: A Reply To Critics, Aaron S. Edlin, C. Scott Hemphill, Herbert J. Hovenkamp, Carl Shapiro
Actavis And Error Costs: A Reply To Critics, Aaron S. Edlin, C. Scott Hemphill, Herbert J. Hovenkamp, Carl Shapiro
All Faculty Scholarship
The Supreme Court’s opinion in Federal Trade Commission v. Actavis, Inc. provided fundamental guidance about how courts should handle antitrust challenges to reverse payment patent settlements. In our previous article, Activating Actavis, we identified and operationalized the essential features of the Court’s analysis. Our analysis has been challenged by four economists, who argue that our approach might condemn procompetitive settlements.
As we explain in this reply, such settlements are feasible, however, only under special circumstances. Moreover, even where feasible, the parties would not actually choose such a settlement in equilibrium. These considerations, and others discussed in the reply, serve to …
Against Settlement Of (Some) Patent Cases, Megan M. La Belle
Against Settlement Of (Some) Patent Cases, Megan M. La Belle
Scholarly Articles
For decades now, there has been a pronounced trend away from adjudication and toward settlement in civil litigation. This settlement phenomenon has spawned a vast critical literature beginning with Owen Fiss’s seminal work, Against Settlement. Fiss opposes settlement because it achieves peace rather than justice, and because settlements often are coerced due to power and resource imbalances between the parties. Other critics have questioned the role that courts play (or ought to play) in settlement proceedings, and have argued that the secondary effects of settlement – especially the lack of decisional law – are damaging to our judicial system. Still, …
Machine Learning And Law, Harry Surden
Machine Learning And Law, Harry Surden
Publications
This Article explores the application of machine learning techniques within the practice of law. Broadly speaking “machine learning” refers to computer algorithms that have the ability to “learn” or improve in performance over time on some task. In general, machine learning algorithms are designed to detect patterns in data and then apply these patterns going forward to new data in order to automate particular tasks. Outside of law, machine learning techniques have been successfully applied to automate tasks that were once thought to necessitate human intelligence — for example language translation, fraud-detection, driving automobiles, facial recognition, and data-mining. If performing …