Faculty Bibliography

If you're injuring someone, you should stop—and pay for the damage you've caused. Why, this book asks, does this simple proposition, generally accepted, not apply to climate change? In Climate Justice, a bracing challenge to status quo thinking on the ethics of climate change, renowned author and legal scholar Cass Sunstein clearly frames what's at stake and lays out the moral imperative: When it comes to climate change, everyone must be counted equally, regardless of when they live or where they live—which means that wealthy nations, which have disproportionately benefited from greenhouse gas emissions, are obliged to help future generations and people in poor nations that are particularly vulnerable. Invoking principles of corrective justice and distributive justice, Sunstein argues that rich countries should pay for the harms they have caused and that all of us are obliged to take steps to protect future generations from serious climate-related damage. He shows how “choice engines,” informed by artificial intelligence, can enable people to save money and to reduce the harms they produce. The book casts new light on the “social cost of carbon,”—the most important number in climate change debates—and explains how intergenerational neutrality and international neutrality can help all nations, crucially the United States and China, do what must be done.

In the current era, product differentiation is increasing; it is often fueled by big data and artificial intelligence (AI). Whereas product differentiation is generally welfare enhancing when consumers are informed and fully rational, such differentiation might reduce welfare when consumers suffer from misperceptions, either because of a lack of information or because of behavioral biases. We show that the positive and normative implications of product differentiation depend on whether consumers over-or underestimate the benefits from some products. In particular, over-estimation of the benefits is a potential source of significant welfare losses. We also study sellers' incentives to promote, or combat, misperception. Our analysis can inform policymakers who are debating regulation that can make product differentiation more difficult (or easier), especially when the differentiation is instigated by AI algorithms powered by big data.

For fans of Thinking Fast and Slow and The Power of Habit, a groundbreaking new study of how disrupting our well-worn routines, both good and bad, can rejuvenate our days and reset our brains to allow us to live happier and more fulfilling lives.Have you ever noticed that what is thrilling on Monday tends to become boring on Friday? Even exciting relationships, stimulating jobs, and breathtaking works of art lose their sparkle after a while. People stop noticing what is most wonderful in their own lives. They also stop noticing what is terrible. They get used to dirty air. They stay in abusive relationships. People grow to accept authoritarianism and take foolish risks. They become unconcerned by their own misconduct, blind to inequality, and are more liable to believe misinformation than ever before. But what if we could find a way to see everything anew? What if you could regain sensitivity, not only to the great things in your life, but also to the terrible things you stopped noticing and so don’t try to change? Now, neuroscience professor Tali Sharot and Harvard law professor (and presidential advisor) Cass R. Sunstein investigate why we stop noticing both the great and not-so-great things around us and how to “dishabituate” at the office, in the bedroom, at the store, on social media, and in the voting booth. This groundbreaking work, based on decades of research in the psychological and biological sciences, illuminates how we can reignite the sparks of joy, innovate, and recognize where improvements urgently need to be made. The key to this disruption—to seeing, feeling, and noticing again—is change. By temporarily changing your environment, changing the rules, changing the people you interact with—or even just stepping back and imagining change—you regain sensitivity, allowing you to more clearly identify the bad and more deeply appreciate the good.

This is the original version of the Tanner Lectures on Human Values, delivered at Harvard University in 1994. The central question is this: How is law possible in a heterogeneous society, composed of people who sharply disagree about basic values? Such disagreements involve the most important issues of social life: the distribution of wealth, the role of race and gender, the nature of free speech and private property. Much of the answer to this puzzle lies in an appreciation of how people who disagree on fundamental issues can achieve incompletely theorized agreements on particular cases. Lecture I sets out the basic idea of incompletely theorized agreements and argues that such agreements have many virtues. Lecture II opposes rules to rulelessness. Its principal goal is to point the way toward a more refined understanding of the ideal of the rule of law, one that sees a degree of particularity, and a degree of lawmaking at the point of application, as an important part of that ideal. The lectures end with the suggestion that incompletely theorized agreements on particular outcomes play a large role not only in law, but also in many other sectors of social life, prominently including democratic discussion.

The Court’s opinion in Dobbs v. Jackson Women’s Health Organization embraces a form of due process traditionalism. With Burkean and Thayerian arguments at work, the Court offers an understanding of the Due Process Clause which allows substantive protection of rights only if they are vindicated by tradition and essential to “ordered liberty.” Within the opinion’s logic, the major challenge is to accept due process traditionalism without simultaneously throwing a variety of nontraditional or antitraditionalist substantive due process cases into doubt. There are uneasy relationships between the traditionalist thrust of the Court’s opinion and the Court’s nontraditional or antitraditionalist jurisprudence in other areas of constitutional law. A central reason must be that the Court believes in some forms of moral progress. The central weakness of the Dobbs opinion is its rejection of the idea that moral progress can and should play a role in the understanding of constitutional rights.

Scientific evidence regularly guides policy decisions, with behavioural science increasingly part of this process. In April 2020, an influential paper proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.

Does the Constitution guarantee free speech on university campuses? The Supreme Court has essentially said so, at least insofar as we are speaking of public universities. Some private universities are interested in adhering to the First Amendment, even though it does not bind them. But what do First Amendment protections entail or require? It is clear that in general, universities may not discriminate on the basis of viewpoint. It is also clear that as educational institutions, universities may sometimes regulate speech to protect their essential mission – by, for example, forbidding “true threats,” prohibiting plagiarism, protecting speakers from being shouted down, forbidding students from taking over buildings, and ensuring that students and teachers focus on the topics of their courses. By exploring thirty-seven scenarios, it is possible to concretize these general propositions, and to see which questions are easy and which questions are hard. The broadest conclusion is that to the extent that universities seek to comply with the First Amendment, they must permit a great deal of speech that is offensive, hateful, and even horrifying.

Liberals see human beings as subjects, not objects. They reject despotism in its many forms. With Justice Robert Jackson, liberals believe that “compulsory unification of opinion achieves only the unanimity of the graveyard.” Liberal authoritarianism is an oxymoron. Illiberal democracy is illiberal, and liberals oppose it for that reason. Liberals are puzzled by many of those, on the left and the right, who describe themselves as “antiliberal” or “postliberal.” With respect to some claims of “antiliberals” or “postliberals,” liberals agree with Amos Tversky and Daniel Kahneman: “The refutation of a caricature can be no more than a caricature of refutation.” With respect to those claims of “antiliberals” or “postliberals” that do not amount to a caricature, liberals insist on the importance of freedom of thought and action and deliberative democracy, and on the need to respect reasonable pluralism.

There are two justifications for the major questions doctrine. The first justification, vigorously offered by Justice Neil Gorsuch, might be described as Lockean; it sees the doctrine as an effort to preserve legislative primacy and to reduce the policymaking authority of the executive branch. On the Lockean view, the major questions doctrine is a clear-statement principle, and it is in evident tension with textualism. The second justification, vigorously offered by Justice Amy Coney Barrett, might be described as Wittgensteinian; it sees the doctrine as an effort to capture Congress's likely instructions. The Wittgensteinian justification fits comfortably with textualism, and it does not operate as a clear-statement principle at all. The Court can be seen as having adopted an incompletely theorized agreement in favor of the major questions doctrine, but at some point, the two justifications might lead in different directions. While neither justification is implausible, both of them run into serious objections.

In 1921, John Maynard Keynes and Frank Knight independently insisted on the importance of making a distinction between uncertainty and risk. Keynes referred to matters about which “there is no scientific basis on which to form any calculable probability whatever.” Knight claimed that “Uncertainty must be taken in a sense radically distinct from the familiar notion of Risk, from which it has never been properly separated.” Knightian uncertainty exists when people cannot assign probabilities to imaginable outcomes. People might know that a course of action might produce bad outcomes A, B, C, D, and E, without knowing much or anything about the probability of each. Contrary to a standard view in economics, Knightian uncertainty is real. Dogs face Knightian uncertainty; horses and elephants face it; human beings face it; in particular, human beings who make policy, or develop regulations, sometimes face it. Knightian uncertainty poses challenging and unresolved issues for decision theory and regulatory practice. It bears on many problems, potentially including those raised by artificial intelligence. It is tempting to seek to eliminate the worst-case scenario (and thus to adopt the maximin rule), but serious problems arise if eliminating the worst-case scenario would (1) impose high risks and costs, (2) eliminate large benefits or potential “miracles,” or (3) create uncertain risks.

People buy some goods that they do not enjoy and wish did not exist. They might even be willing to pay a great deal for such goods, whether the currency involves time or money. One reason involves signaling to others; so long as the good exists, nonconsumption might give an unwanted signal to friends or colleagues. Another reason involves self-signaling; so long as the good exists, nonconsumption might give an unwanted signal to an agent about himself or herself. Yet another reason involves a combination of network effects and status competition; nonconsumption might deprive people of the benefits of participating in a network, and thus cause them to lose relative position. With respect to real-world goods (including activities) of this kind, there is typically heterogeneity in relevant populations, with some people deriving positive utility from goods to which other people are indifferent, or which other people deplore. Efforts to measure people’s willingness to pay for goods of this kind will suggest a welfare gain, and possibly a substantial one, even though the existence of such goods produces a welfare loss, and possibly a substantial one. We might call this the Barbie Problem: Notwithstanding the success of the (terrific) 2023 movie, it is reasonable to speculate that many children, and even more parents, wish that there was no such thing as Barbie, even if children play with Barbie, and even if parents purchase Barbie. Ties and high heels might count as Barbies. Collective action, private or public, is necessary to eliminate goods that people consume but wish did not exist. Legal responses here are limited, but they might be contemplated when someone successfully maneuvers people into a situation in which they are incentivized to act against their interests, by consuming a product or engaging in an activity they do not enjoy, in order to avoid offering an unwanted signal.

Do people like financial nudges? To answer that question we conducted a survey presenting people with 36 hypothetical scenarios describing financial nudges. We varied levels of transparency (i.e. explaining how the nudge worked), framing (nudges framed in terms of spending, or saving), and ”System” (nudges could either target System 1 or System 2). Participants were a random sample of 2,000 people drawn from a representative Australian population. All financial nudges were tested across six domains: approval, perceived benefit, perceived ethics, perceived manipulation, the likelihood of use, as well as the likelihood of use if the nudge were to be proposed by a bank. Results indicate that people generally approve of financial nudges, rating them as neutral to positive across all domains (except for manipulation, which was reverse coded). We find effects of framing and System. People have strong and significant preferences for System 2 nudges, and nudges framed in terms of savings. Transparency was not found to have a significant impact on how people rate financial nudges. Financial nudges continue to be rated positive, regardless of the messenger. Looking at demographics, we find that participants who were female, younger (under 35), living in urban areas and richer (earning over 80,000 AUD) were most likely to favour financial nudges. We discuss the implications for these results as applied to the financial sector.

In Republican and Democratic administrations, regulatory and funding decisions have both been made with close reference to benefit-cost analysis (BCA). With respect to regulation, there has been a great deal of scholarly discussion of BCA and its limits, but almost no attention has been paid to the role of BCA in government funding. That is a serious gap, not least in connection with climate-related risks, such as wildfire, drought, extreme heat, and flooding. In OMB Circular A-94, the Office of Management and Budget has long required applicants for federal funding to demonstrate that the benefits of their projects would exceed the costs. Under Circular A-94, efficiency-based BCA can produce results that fail to maximize welfare and that are also highly inequitable. The 2023 revision of Circular A-94, focused on welfare and equity, reflects an effort to incorporate new academic thinking over the past three decades, which is now—not uncontroversially—being brought directly into policy. At the same time, the new Circular A-94 raises fresh questions about how best to promote welfare, and to consider equity, in practice. Pressing issues involve the use of distributional weights in funding decisions and also the use of averages across populations, which might be seen as a form of distributional weighting. More broadly, the trajectory of this benefit-cost guidance, which predates the guidance for regulation and originally covered regulation, helps uncover the logic under which BCA has been operating and deeper challenges and tensions within BCA, in the past and going forward.

Why are take up rates incomplete or low when the relevant opportunities are unambiguously advantageous to people who are eligible for them? How can public officials promote higher take up of opportunities? All over the world, these are challenges of the first order. There are three primary barriers to take up: learning costs, compliance costs, and psychological costs. These costs lower the net expected benefit of opportunities, and reduce participation in otherwise advantageous programs. Fully rational agents would consider these costs in their take up decisions, and in light of behavioral biases, such costs loom especially large and may seem prohibitive. Experimental and other evidence suggest methods for reducing the barriers to take up and the effects of behavioral biases. Use of such methods has the potential to significantly increase access to a wide range of opportunities that would increase individual well-being and social welfare.

In the last two decades, there has been an extraordinary outpouring of careful historical work on two of the most fundamental questions in constitutional law: (1) whether Congress may delegate open-ended discretionary power to the executive branch (or others) and (2) whether Congress may restrict the president’s power to remove high-level officials in the executive branch. The best reading of the new evidence is that there was no robust nondelegation doctrine at the founding period, if there was a nondelegation doctrine at all. Though the issue is closer, the best reading of the new evidence is that during the founding period, the Constitution was understood to authorize Congress to restrict the president’s power of removal, even over principal officers (with important qualifications). Understood in terms of its original public meaning, the Constitution almost certainly allowed Congress to grant very broad discretion to the executive branch and also permitted Congress to limit the president’s removal authority over (some) principal officers. What is remarkable is that in both contexts, no originalist on the Court has been convinced by the relevant evidence, or even seriously grappled with it. Any explanation of the apparent impotence of historical evidence in this context (or others) would be speculative, but there are three plausible accounts. The first points to a simple lack of awareness of the relevant evidence and the crucial role of epistemic communities in constitutional law. The second is Bayesian and spotlights rational updating. The third points to motivated reasoning. All three accounts offer lessons for lawyers and others seeking to marshal historical evidence to disrupt engrained judicial beliefs.

There is a good argument that the foundations of modern administrative law were laid in 1980, with the disparate opinions of a sharply divided Court in Industrial Union Department v. American Petroleum Institute (commonly referred to as “the Benzene Case”). Consider four points. (1) The Benzene Case is now understood to be the first contemporary appearance of the Major Questions Doctrine. (2) The Benzene Case marked the return of the nondelegation doctrine, signaled most plainly by Justice William Rehnquist’s elaborate concurring opinion, but also by a favorable reference in the plurality opinion by Justice John Paul Stevens and an open-minded sentence from Justice Lewis Powell. (3) The Benzene Case is the origin of contemporary cost-benefit default principles, permitting or requiring agencies to exempt de minimis risks, to consider costs, and to engage in some form of cost-benefit balancing, unless Congress has squarely said otherwise. (4) The Benzene Case essentially defined “significant risk,” with a precise numerical definition (one in one thousand) that persists at the Department of Labor to this day. At the same time, a close analysis of the plurality opinion in the Benzene Case shows that it is best understood as a specification, above all, of the Absurdity Canon – a Church of the Holy Trinity v. United States for the modern administrative state. So understood, the Benzene Case had, and continues to have, an important and salutary effect on regulatory programs. Its significant current role, more than four decades after the opinions were issued, is an intriguing case study in doctrinal development, and in particular in how Supreme Court decisions can plant small seeds that become big trees.

This article discusses the opportunities and costs of AI in behavioural science, with particular reference to consumer welfare. We argue that because of pattern detection capabilities, modern AI will be able to identify (1) new biases in consumer behaviour and (2) known biases in novel situations in which consumers find themselves. AI will also allow behavioural interventions to be personalised and contextualised and thus produce significant benefits for consumers. Finally, AI can help behavioural scientists to “see the system,” by enabling the creation of more complex and dynamic models of consumer behaviour. While these opportunities will significantly advance behavioural science and offer great promise to improve consumer outcomes, we highlight several costs of using AI. We focus on some important environmental, social, and economic costs that are relevant to behavioural science and its application. For consumers, some of those costs involve privacy; others involve manipulation of choices.

Chater & Loewenstein, superb and distinguished social scientists, have misfired. Their complaint is baseless: In the real world of policymaking, behavioral science is mostly being used to reform systems, not to alter individual behavior. Nor is there empirical support for the proposition that interventions aimed at helping individuals make systemic reform less likely. Chater & Loewenstein (C&L), superb and distinguished social scientists, have misfired. In the real world of policymaking, behavioral science is mostly being used to reform systems, not to alter individual behavior. Nor is there empirical support for the proposition that interventions aimed at helping individuals make systemic reform less likely. Some conspiracy theories are true, but theirs is groundless.

Machine learning algorithms are increasingly able to predict what goods and services particular people will buy, and at what price. It is possible to imagine a situation in which relatively uniform, or coarsely set, prices and product characteristics are replaced by far more in the way of individualization. Companies might, for example, offer people shirts and shoes that are particularly suited to their situations, that fit with their particular tastes, and that have prices that fit their personal valuations. In many cases, the use of algorithms promises to increase efficiency and to promote social welfare; it might also promote fair distribution. But when consumers suffer from an absence of information or from behavioral biases, algorithms can cause serious harm. Companies might, for example, exploit such biases in order to lead people to purchase products that have little or no value for them or to pay too much for products that do have value for them. Algorithmic harm, understood as the exploitation of an absence of information or of behavioral biases, can disproportionately affect members of identifiable groups, including women and people of color. Since algorithms exacerbate the harm caused to imperfectly informed and imperfectly rational consumers, their increasing use provides fresh support for existing efforts to reduce information and rationality deficits, especially through optimally designed disclosure mandates. In addition, there is a more particular need for algorithm-centered policy responses. Specifically, algorithmic transparency—transparency about the nature, uses, and consequences of algorithms—is both crucial and challenging; novel methods designed to open the algorithmic “black box” and “interpret” the algorithm’s decision-making process should play a key role. In appropriate cases, regulators should also police the design and implementation of algorithms, with a particular emphasis on the exploitation of an absence of information or of behavioral biases.

Edição ampliada e definitiva de um dos maiores clássicos sobre tomada de decisão, escrito por Richard H. Thaler, ganhador do prêmio Nobel de economia, e Cass R. Sunstein, professor de Harvard. * Best-seller do New York Times * Mais de ...

Many consumers suffer from inadequate information and behavioral biases, which can produce internalities, understood as costs that people impose on their future selves. In these circumstances, “Choice Engines,” powered by Artificial Intelligence (AI), might produce significant savings in terms of money, health, safety, or time. Consider, for example, choices among motor vehicles or appliances. Choice Engines might also take account of externalities, and they might nudge or require consumers to do so as well. Different consumers care about different things, of course, which is a reason to insist on a high degree of freedom of choice, even in the presence of internalities and externalities. It is important to emphasize that Choice Engines and AI might be enlisted by insufficiently informed or self-interested actors, who might exploit inadequate information or behavioral biases, and thus reduce consumer welfare. It is also important to emphasize that Choice Engines and AI might show behavioral biases, perhaps the same ones that human beings are known to show, perhaps others that have not been named yet, or perhaps new ones, not shown by human beings, that cannot be anticipated.

Do people like algorithms? In this study, intended as a promissory note and a description of a research strategy, we offer the following highly preliminary findings. (1) In a simple choice between a human being and an algorithm, across diverse settings and without information about the human being or the algorithm, people in our tested groups are about equally divided in their preference. (2) When people are given a very brief account of the data on which an algorithm relies, there is a large shift in favor of the algorithm over the human being. (3) When people are given a very brief account of the experience of the relevant human being, without an account of the data on which the relevant algorithm relies, there is a moderate shift in favor of the human being. (4) When people are given both (a) a very brief account of the experience of the relevant human being and (b) a very brief account of the data on which the relevant algorithm relies, there is a large shift in favor of the algorithm over the human being. One lesson is that in the tested groups, at least one-third of people seem to have a clear preference for either a human being or an algorithm – a preference that is unaffected by brief information that seems to favor one or the other. Another lesson is that a brief account of the data on which an algorithm relies does have a significant effect on a large percentage of the tested groups, whether or not people are also given positive information about the human alternative. Across the various surveys, we do not find persistent demographic differences, with one exception: men appear to like algorithms more than women do. These initial findings are meant as proof of concept, or more accurately as a suggestion of concept, intended to inform a series of larger and more systematic studies of whether and when people prefer to rely on algorithms or human beings, and also of international and demographic differences.

The U.S. Supreme Court has eliminated the right to abortion and is revisiting all sorts of other fundamental questions today—about voting rights, affirmative action, gun laws, and much more. Once-arcane theories of constitutional interpretation are profoundly affecting the lives of all Americans. In this brief and urgent book, Harvard Law School professor Cass Sunstein provides a lively introduction to competing approaches to interpreting the Constitution—and argues that the only way to choose one is to ask whether it would change American life for the better or worse. If a method of interpretation would eliminate the right of privacy, allow racial segregation, or obliterate free speech, it would be unacceptable for that reason. But some Supreme Court justices are committed to “originalism,” arguing that the meaning of the Constitution is settled by how it was publicly understood when it was ratified. Originalists insist that their approach is dictated by the Constitution. That, Sunstein argues, is a big mistake. The Constitution doesn’t contain instructions for its own interpretation. Any approach to constitutional interpretation needs to be defended in terms of its broad effects—what it does to our rights and our institutions. It must respect those rights and institutions—and safeguard the conditions for democracy itself.

This article discusses the opportunities and costs of AI in behavioural science. We argue that because of pattern detection capabilities, modern AI will be able to identify (1) new biases in human behaviour and (2) known biases in novel situations. AI will also allow behavioural interventions to be personalised and contextualised, and thus produce significant benefits. Finally, AI can help behavioural scientists to 'see the system,' by enabling the creation of more complex and dynamic models of human behaviour. While these opportunities will significantly advance behavioural science and offer great promise to improve the lives of citizens and consumers, we highlight several costs of using AI. We focus on some important environmental, social, and economic costs that are relevant to behavioural science and its application. Some of those costs involve privacy; others involve manipulation.

This article discusses the opportunities and costs of AI in behavioural science. We argue that because of pattern detection capabilities, modern AI will be able to identify (1) new biases in human behaviour and (2) known biases in novel situations. AI will also allow behavioural interventions to be personalised and contextualised, and thus produce significant benefits. Finally, AI can help behavioural scientists to 'see the system,' by enabling the creation of more complex and dynamic models of human behaviour. While these opportunities will significantly advance behavioural science and offer great promise to improve the lives of citizens and consumers, we highlight several costs of using AI. We focus on some important environmental, social, and economic costs that are relevant to behavioural science and its application. Some of those costs involve privacy; others involve manipulation.

Os seres humanos geralmente identificam coerência e planejamento, quando nenhum desses elementos sequer existe. Tal situação ocorre nos filmes, na literatura, na história, na economia e na psicanálise – e no Direito Constitucional. No âmbito do cinema, a saga de Star Wars não foi idealizada a priori, a despeito das repetidas afirmações de George Lucas, principal autor da obra; isto agregou à construção da série os componentes de surpresa e improviso - inclusive para o próprio George Lucas. Desta maneira, se é natural que a interpretação assuma um papel penetrante e orientador na imaginação criativa, em forma de manifestação de um novo pensamento, em trabalhos individuais, mais natural ainda é que essa “serendipidade” ocorra em obras de inúmeros autores ao ser escrita ao longo do tempo. A “serendipidade” impõe rigorosas exigências na busca pela coerência na arte, na literatura, na história e no direito. Essa busca leva muitas pessoas a descreverem de forma imprecisa a natureza de seu próprio processo criativo, como George Lucas. A imprecisão surge em resposta à séria necessidade humana de conferir sentido e identificar padrões, o que é um obstáculo significativo para a compreensão e para a reflexão crítica. Independentemente de serem Jedi ou Sith, inúmeros Constitucionalistas se parecem bastante com o autor de Star Wars, quanto à simulação da essência do seu processo criativo.

Connie Converse, a folksinger from the 1950s, is sometimes described as “the first singer-songwriter.” Her tale raises enduring questions about opportunity, what is lost and what is found, and the role of serendipity and luck. It also offers lessons about canon formation and reformation. It even has something to say about the foundations of liberalism.

This paper explores the application of message framing as a management practice to promote change in employee behaviour for corporate sustainability. We conduct a field experiment in a German automotive company to test the effects of three different frames (emotional, normative and gain) on pro-environmental actions in relation to electric vehicle choices of 170 employees. The frames are applied via two communication channels: first, via emails to remind employees about ordering a new car and second, via pop-up notifications appearing in the online system where employees complete their orders. We find that the interventions applied in emails, but not in pop-up notifications, have significant positive effects on electric vehicle adoption. Yet, the durability of the effects is limited. Overall, gain framing in the form of cost saving information has the longest and most powerful impact on electric car choices. Our findings have implications for workplaces where employees might not yet possess strong pro-environmental beliefs, showing that employee sustainable behaviour can be enhanced by emphasising complementary gain motives.

The judgments of human beings can be biased; they can also be noisy. Across a wide range of settings, use of algorithms is likely to improve accuracy, because algorithms will reduce both bias and noise. Indeed, algorithms can help identify the role of human biases; they might even identify biases that have not been named before. As compared to algorithms, for example, human judges, deciding whether to give bail to criminal defendants, show Current Offense Bias and Mugshot Bias; as compared to algorithms, human doctors, deciding whether to test people for heart attacks, show Current Symptom Bias and Demographic Bias. These are cases in which large data sets are able to associate certain inputs with specific outcomes. But in important cases, algorithms struggle to make accurate predictions, not because they are algorithms but because they do not have enough data to answer the question at hand. Those cases often, though not always, involve complex systems. (1) Algorithms might not be able to foresee the effects of social interactions, which can depend on a large number of random or serendipitous factors, and which can lead in unanticipated and unpredictable directions. (2) Algorithms might not be able to foresee the effects of context, timing, or mood. (3) Algorithms might not be able to identify people’s preferences, which might be concealed or falsified, and which might be revealed at an unexpected time. (4) Algorithms might not be able to anticipate sudden or unprecedented leaps or shocks (a technological breakthrough, a successful terrorist attack, a pandemic, a black swan). (5) Algorithms might not have “local knowledge,” or private information, which human beings might have. Predictions about romantic attraction, about the success of cultural products, and about coming revolutions are cases in point. The limitations of algorithms are analogous to the limitations of planners, emphasized by Hayek in his famous critique of central planning. It is an unresolved question whether and to what extent some of the limitations of algorithms might be reduced or overcome over time, with more data or various improvements; calculations are improving in extraordinary ways, but some of the relevant challenges cannot be solved with ex ante calculations.

In evaluating interventions, policymakers should consider both their welfare effects, including their effects on people’s emotional states, and their effects on distributive justice, including their effects on those at the bottom of the economic ladder. The arguments for investigating welfare effects, and effects on distributive justice, are meant as objections to efforts to evaluate behaviorally informed interventions solely in terms of (for example) revealed preferences and effects on participation rates. The arguments are also meant as a plea for investigation and specification of the effects of such interventions on experienced well-being. If interventions give people a sense of security and safety, that is a strong point in their favor; if they make people feel frightened and sad, that is a strong point against them. A central concern is that policymakers sometimes neglect the emotional impact, whether negative or positive, of behaviorally informed interventions. Personalized approaches can promote distributive goals and also target interventions to those who are most likely to be helped by them.

Artificial intelligence (AI), including generative AI, is not human, but restrictions on the activity or use of AI, or on the dissemination of material by or from AI, might raise serious first amendment issues if those restrictions (1) apply to or affect human speakers and writers or (2) apply to or affect human viewers, listeners, and readers. Here as elsewhere, it is essential to distinguish among viewpoint-based restrictions, content-based but viewpoint-neutral restrictions, and content-neutral restrictions. Much of free speech law, as applied to AI, is in the nature of “the law of the horse”: established principles of multiple kinds applied to a novel context. But imaginable cases raise unanswered questions, including (1) whether AI as such has constitutional rights, (2) whether and which person or persons might be a named defendant if AI is acting in some sense autonomously, and (3) whether and in what sense AI has a right to be free from (for example) viewpoint-based restrictions, or whether it would be better, and correct, to say that human viewers, listeners, and readers have the relevant rights, even if no human being is speaking. Most broadly, it remains an unanswered question whether the First Amendment protects the rights of human viewers, listeners, and readers, seeking to see, hear, or read something from AI.

The concept of the rule of law is invoked for purposes that are both numerous and diverse, and that concept is often said to overlap with, or to require, an assortment of other practices and ideals, including democracy, free elections, free markets, property rights, and freedom of speech. It is best to understand the concept in a more specific way, with a commitment to seven principles: (1) clear, general, publicly accessible rules laid down in advance; (2) prospectivity rather than retroactivity; (3) conformity between law on the books and law in the world; (4) hearing rights; (5) some degree of separation between (a) law-making and law enforcement and (b) interpretation of law; (6) no unduly rapid changes in the law; and (7) no contradictions or palpable inconsistency in the law. This account of the rule of law conflicts with those offered by (among many others) Friedrich Hayek and Morton Horwitz, who conflate the idea with other, quite different ideas and practices. Of course it is true that the seven principles can be specified in different ways, broadly compatible with the goal of describing the rule of law as a distinct concept, and some of the seven principles might be understood to be more fundamental than others.

Judges and lawyers sometimes act as if a constitutional or statutory term must, as a matter of semantics, be understood to have a particular meaning, when it could easily be understood to have another meaning, or several other meanings. When judges and lawyers act as if a legal term has a unique semantic meaning, even though it does not, they should be seen to be drawing extravagant inferences. Some constitutional provisions are treated this way; consider the idea that the vesting of executive power in a president of the United States necessarily includes the power to remove, at will, a very wide range of people who are involved in execution of the laws. Some statutory provisions are also treated this way; consider the idea that the term “air pollutant” necessarily includes greenhouse gases. Those who draw extravagant inferences might be engaged in a form of motivated reasoning; their (unarticulated) values and preferences might be responsible for the particular inferences they draw. Alternatively, they might be engaged in an unacknowledged form of Dworkinian reasoning, in which they are attempting to make the best constructive sense out of a legal term. There is a relationship between extravagant inferences and the perception of having been subject to "constitutional gaslighting."

How do judgments about law and morality shift? Why do we come to see political or other conduct as acceptable, when we had formerly seen it as unacceptable, immoral, or even horrific? Why do shifts occur in the opposite direction? Why accounts for the power of “the normal”? A clue comes from the fact that some of our judgments are unstable, in the sense that they are an artifact of, or endogenous to, what else we see. This is true of sensory perception: Whether an object counts as blue or purple depends on what other objects surround it. It is also true for ethical judgments: Whether conduct counts as unethical depends on what other conduct is on people's viewscreens. There are plausible evolutionary explanations for these findings. It follows that conduct that was formerly seen as unethical may come to seem ethical, as terrible behavior becomes more common, and also that conduct that was formerly seen as ethical may come to seem unethical, as good behavior becomes more common. In these circumstances, law (and enforcement practices) can have an important signaling effect, giving people a sense of what is normal and what is not.

A uniform value of a statistical life (VSL) is part of established practice within the federal government. Some people have applauded a uniform VSL on the ground that it respects the equality of persons; takes harm to poor people as seriously as it does harm to wealthy people; avoids expressive harms; and builds appropriate wealth redistribution into regulatory policy. Other people have strenuously objected to a uniform VSL, emphasizing that to reduce mortality risks, poor people are willing to pay less than rich people are, and urging that poor people should not have to pay more than they are willing to pay. Whether a uniform VSL is in the interest of poor people depends on whether we are dealing with subsidies or regulations. In the case of subsidies, a uniform VSL is highly likely to benefit poor people. If we are dealing with regulations, we cannot know whether a uniform VSL helps or harms poor people without knowing the incidence of costs (and benefits).

There are many misconceptions about nudges and nudging, and some of them are widespread. For example, some people believe that that nudges are manipulative; that nudges are hidden or covert; that nudges are difficult to define; that nudges are an insult to human agency; that nudges are based on excessive trust in government; that nudges exploit behavioral biases; that nudges depend on a belief that human beings are irrational; and that nudges work only at the margins, do not affect structures, and cannot accomplish much. These are mistakes. Nudges are generally transparent rather than covert or forms of manipulation; nudges are not difficult to define; nudges always respect, and often promote, human agency; because nudges insist on preserving freedom of choice, they do not put excessive trust in government; many nudges are educative, and even when they are not, they tend to make life simpler and more navigable; and some nudges have quite large impacts. It is true that for countless problems, nudges are hardly enough. They cannot eliminate poverty, unemployment, and corruption. But by itself, any individual initiative – whether it is a tax, a subsidy, a mandate, or a ban – is unlikely to solve large problems. Denting them counts as an achievement.

In some circles, there is a misconception that within government, the only or principal uses of behavioral science consist of efforts to nudge individual behavior (sometimes described, pejoratively and unfairly, as “tweaks”). Nothing could be further from the truth. Behavioral science has been used, and is being used, to help inform large-scale reforms, including mandates and bans directed at companies (as, for example, in the cases of fuel-economy mandates and energy efficiency mandates). Behavioral science has been used, and is being used, to help inform taxes and subsidies (as, for example, in the cases of cigarette taxes, taxes on sugar-sweetened beverages, and subsides for electric cars). Behavioral science has been used, and is being used, to help inform nudges imposed on companies (with such goals as reducing greenhouse gas emissions, improving occupational safety, and protecting personal privacy). Some important interventions are indeed aimed at individuals (as with fuel economy labels, nutrition labels, and calorie labels, and automatic enrollment in savings plans); sometimes such interventions have significant positive effects, and there is no evidence that they make more aggressive reforms less likely. It is preposterous to suggest that choice-preserving interventions, such as nudges, “crowd out” more aggressive approaches.

The "supreme law of the land" includes "this Constitution," and federal officers are "bound, by oath or affirmation, to support this Constitution." In recent years, some people have argued that these words require oath-takers to be originalists and to follow the Constitution's "original public meaning," properly understood. An understanding of this argument requires an exploration of the diverse forms and conceptions of originalism, which raise puzzles of their own. Whether or not we embrace some form of originalism, the broader point is this: the claim that the term "this Constitution" mandates a contested theory of interpretation, including a contested form of originalism, belongs in the same category with many other efforts to resolve controversial questions in law by reference to the supposed dictate of some external authority. Whether maddening or liberating, there is nothing that communication just is, nor is there any such dictate. The choice is ours.