Practicing in the Patent Marketplace
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The growing secondary market for patents is a relatively recent phenomenon. Understanding this unusual and developing market is necessary to navigating it effectively. This Article surveys the marketplace, identifies its key players, and notes some of the unique challenges presented in patent sales. Patents are unique assets with differing impacts in different hands. They are susceptible of effective valuation only by specialists—yet most patents are not worth such costly effort and investment. This Article explores how this vibrant marketplace continues to adapt to value these unique assets more efficiently and to deal with imperfect information in individual transactions.
We would like to thank the workshop participants at University of Michigan Law School, Northwestern University Law School, Notre Dame Law School, University of Toronto Law School, Stanford Law School, and N.Y.U. School of Law; and conference participants at the 2024 American Law and Economics Association Meeting for many helpful comments and suggestions. We are most grateful to Jonathan Morad Artal (Stanford Class of 2025) and Andrea Lofquist (Michigan Class of 2024) for their valuable research assistance and comments on earlier drafts.
We would like to thank the workshop participants at University of Michigan Law School, Northwestern University Law School, Notre Dame Law School, University of Toronto Law School, Stanford Law School, and N.Y.U. School of Law; and conference participants at the 2024 American Law and Economics Association Meeting for many helpful comments and suggestions. We are most grateful to Jonathan Morad Artal (Stanford Class of 2025) and Andrea Lofquist (Michigan Class of 2024) for their valuable research assistance and comments on earlier drafts.
The flexibility to renegotiate can facilitate long-term contracting and thereby beneficial reliance investments and risk allocation. The prospect of modification can induce contracting parties who expect their bargaining power to improve to enter into contracts earlier and realize the advantages of longer-term relationships. Otherwise, those parties might decline to contract or delay until those opportunities realize, thereby foregoing the benefits of long-term risk allocation or reliance investments. The parties decide not only whether, but also when, to make legally binding commitments to each other. Courts should be more lenient in enforcing contract modifications that, prompted by a shift in bargaining power, may have only a redistributive effect. Parties can design under-compensatory damages that would provide a credible threat of breach ex post to facilitate ex post modification. Requiring good faith in modification (along with damages) can constrain possible holdup and protect reliance investments and risk allocation.
For thoughtful comments, the authors thank Jennifer Arlen, Rick Brooks, Kevin Davis, Brian Galle, Jacob Goldin, and participants in workshops at Columbia Law School, Texas A&M University School of Law, and the American Law and Economics Association Annual Meeting. Ji Young Kim provided excellent research assistance.
For thoughtful comments, the authors thank Jennifer Arlen, Rick Brooks, Kevin Davis, Brian Galle, Jacob Goldin, and participants in workshops at Columbia Law School, Texas A&M University School of Law, and the American Law and Economics Association Annual Meeting. Ji Young Kim provided excellent research assistance.
For thoughtful comments, the authors thank Jennifer Arlen, Rick Brooks, Kevin Davis, Brian Galle, Jacob Goldin, and participants in workshops at Columbia Law School, Texas A&M University School of Law, and the American Law and Economics Association Annual Meeting. Ji Young Kim provided excellent research assistance.
The negative moral emotions of guilt and shame impose real social costs but also create opportunities for policymakers to engender compliance with legal rules in a cost-effective manner. This Essay presents a unified model of guilt and shame that demonstrates how legal policymakers can harness negative moral emotions to increase social welfare. The prospect of guilt and shame can deter individuals from violating moral norms and legal rules, thereby substituting for the expense of state enforcement. But when legal rules and law enforcement fail to induce total compliance, guilt and shame experienced by noncompliers can increase the law’s social costs. The Essay identifies specific circumstances in which rescinding a legal rule will improve social welfare because eliminating the rule reduces the moral costs of noncompliance with the law’s command. It also identifies other instances in which moral costs strengthen the case for enacting legal rules and investing additional resources in enforcement because deterrence reduces the negative emotions experienced by noncompliers.
Critics of generative AI often describe it as a “plagiarism machine.” They may be right, though not in the sense they mean. With rare exceptions, generative AI doesn’t just copy someone else’s creative expression, producing outputs that infringe copyright. But it does get its ideas from somewhere. And it’s quite bad at identifying the source of those ideas. That means that students (and professors, and lawyers, and journalists) who use AI to produce their work generally aren’t engaged in copyright infringement. But they are often passing someone else’s work off as their own, whether or not they know it. While plagiarism is a problem in academic work generally, AI makes it much worse because authors who use AI may be unknowingly taking the ideas and words of someone else.
Disclosing that the authors used AI isn’t a sufficient solution to the problem because the people whose ideas are being used don’t get credit for those ideas. Whether or not a declaration that “AI came up with my ideas” is plagiarism, failing to make a good-faith effort to find the underlying sources is a bad academic practice.
We argue that AI plagiarism isn’t—and shouldn’t be—illegal. But it is still a problem in many contexts, particularly academic work, where proper credit is an essential part of the ecosystem. We suggest best practices to align academic and other writing with good scholarly norms in the AI environment.