Cass R. Sunstein

Bio: Cass R. Sunstein is an academic researcher from Harvard University. The author has contributed to research in topics: Supreme court & Constitution. The author has an hindex of 117, co-authored 787 publications receiving 57639 citations. Previous affiliations of Cass R. Sunstein include Brigham Young University & Indiana University.

Of Artificial Intelligence and Legal Reasoning

Abstract: Can computers, or artificial intelligence, reason by analogy? This essay urges that they cannot, because they are unable to engage in the crucial task of identifying the normative principle that links or separates cases. Current claims, about the ability of artificial intelligence to reason analogically, rest on an inadequate picture of what legal reasoning actually is. For the most part, artificial intelligence now operates as a kind of advanced version of LEXIS, offering research assistance rather than analogical reasoning. But this is a claim about current technology, not about inevitable limitations of artificial intelligence; things might change in the future.

Requiring Active Choosing is a Form of Paternalism

Abstract: Many people insist on drawing a line between active choosing and paternalism, but that line is often illusory. Whenever private or public institutions override people’s desire not to choose, and insist on active choosing, they are likely to be behaving paternalistically, through a kind of choice-requiring paternalism. Active choosing can be seen as a form of libertarian paternalism if people are permitted to opt out of choosing in favor of a default (and in that sense not to choose). This is a distinctive approach – “simplified active choosing” – and in many contexts, it has considerable appeal. By contrast, active choosing is a form of nonlibertarian paternalism insofar as people are required to choose. These points have implications for a range of issues in law and policy, suggesting that those who favor active choosing, or insist on it, may well be overriding people’s preferences (for better or for worse).

Fuel Economy 2.0

Abstract: Motor vehicle fuel-economy standards have long been a cornerstone of U.S. policy to reduce fuel consumption in the light-duty vehicle fleet. In 2011 and 2012 these standards were significantly expanded in an effort to achieve steep reductions in oil demand and greenhouse gas emissions through 2025. In 2018, following a review of the standards, the Environmental Protection Agency and National Highway Traffic Safety Administration proposed to instead freeze the standards at 2020 levels, citing high program costs (and potential safety issues). The current debate over the future of U.S. efficiency standards provides an opportunity to consider whether the existing approach could be improved to achieve environmental goals at a lower cost. The current policy prescribes standards that focus on efficiency alone, as opposed to lifetime consumption, and treats vehicle categories differentially, meaning that it imposes unnecessarily high costs and does not deliver guaranteed petroleum/greenhouse gas (GHG) savings. On the basis of a commitment to cost-benefit analysis, defining U.S. regulatory policy for more than 30 years, we propose a novel reform that has three main features: 1) the regulation of expected fuel consumption/GHG emissions directly without consideration of the type or size of the vehicle; 2) use of existing data to assign lifetime fuel consumption/GHG emissions to each model; and 3) creation of a robust cap-and-trade market for automakers to reduce compliance costs. We show that this approach would increase the certainty of reductions in fuel consumption/GHG emissions in transportation and do so at a far lower cost per gallon/ton of GHG avoided. Such an approach would be consistent with existing statutory authorities at the Environmental Protection Agency and the Department of Transportation.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

The behaviour change wheel: a new method for characterising and designing behaviour change interventions.

Abstract: Improving the design and implementation of evidence-based practice depends on successful behaviour change interventions. This requires an appropriate method for characterising interventions and linking them to an analysis of the targeted behaviour. There exists a plethora of frameworks of behaviour change interventions, but it is not clear how well they serve this purpose. This paper evaluates these frameworks, and develops and evaluates a new framework aimed at overcoming their limitations. A systematic search of electronic databases and consultation with behaviour change experts were used to identify frameworks of behaviour change interventions. These were evaluated according to three criteria: comprehensiveness, coherence, and a clear link to an overarching model of behaviour. A new framework was developed to meet these criteria. The reliability with which it could be applied was examined in two domains of behaviour change: tobacco control and obesity. Nineteen frameworks were identified covering nine intervention functions and seven policy categories that could enable those interventions. None of the frameworks reviewed covered the full range of intervention functions or policies, and only a minority met the criteria of coherence or linkage to a model of behaviour. At the centre of a proposed new framework is a 'behaviour system' involving three essential conditions: capability, opportunity, and motivation (what we term the 'COM-B system'). This forms the hub of a 'behaviour change wheel' (BCW) around which are positioned the nine intervention functions aimed at addressing deficits in one or more of these conditions; around this are placed seven categories of policy that could enable those interventions to occur. The BCW was used reliably to characterise interventions within the English Department of Health's 2010 tobacco control strategy and the National Institute of Health and Clinical Excellence's guidance on reducing obesity. Interventions and policies to change behaviour can be usefully characterised by means of a BCW comprising: a 'behaviour system' at the hub, encircled by intervention functions and then by policy categories. Research is needed to establish how far the BCW can lead to more efficient design of effective interventions.

International Norm Dynamics and Political Change

Abstract: Norms have never been absent from the study of international politics, but the sweeping “ideational turn” in the 1980s and 1990s brought them back as a central theoretical concern in the field. Much theorizing about norms has focused on how they create social structure, standards of appropriateness, and stability in international politics. Recent empirical research on norms, in contrast, has examined their role in creating political change, but change processes have been less well-theorized. We induce from this research a variety of theoretical arguments and testable hypotheses about the role of norms in political change. We argue that norms evolve in a three-stage “life cycle” of emergence, “norm cascades,” and internalization, and that each stage is governed by different motives, mechanisms, and behavioral logics. We also highlight the rational and strategic nature of many social construction processes and argue that theoretical progress will only be made by placing attention on the connections between norms and rationality rather than by opposing the two.

Risk as Feelings

Abstract: Virtually all current theories of choice under risk or uncertainty are cognitive and consequentialist. They assume that people assess the desirability and likelihood of possible outcomes of choice alternatives and integrate this information through some type of expectation-based calculus to arrive at decision. The authors propose an alternative theoretical perspective, the risk-as-feelings hypothesis, that highlights the role of affect experienced at the moment of decision making. Drawing on research from clinical, physiological, and other subfield of psychology, they show that emotional reactions to risky situations often drive behavior. The risk-as-feelings hypothesis is shown to explain a wide range of phenomena that have resisted interpretation in cognitive-consequentialist terms.

A perspective on judgment and choice: Mapping bounded rationality.

Abstract: Early studies of intuitive judgment and decision making conducted with the late Amos Tversky are reviewed in the context of two related concepts: an analysis of accessibility, the ease with which thoughts come to mind; a distinction between effortless intuition and deliberate reasoning. Intuitive thoughts, like percepts, are highly accessible. Determinants and consequences of accessibility help explain the central results of prospect theory, framing effects, the heuristic process of attribute substitution, and the characteristic biases that result from the substitution of nonextensional for extensional attributes. Variations in the accessibility of rules explain the occasional corrections of intuitive judgments. The study of biases is compatible with a view of intuitive thinking and decision making as generally skilled and successful.