The Paternalistic Turn in Behavioral Law and Economics: A Critique

The application of behavioral economics to law and economics has taken a paternalistic turn. Behavioralists believe that the fundamental assumptions regarding individual behavior in standard theory do not reflect reality. If individuals are not “rational” in the standard economic sense, then there will be decisionmaking failures: people cannot be relied upon to make individually optimal decisions and thus to maximize welfare as they see it. This Article is organized as follows. Part One is a prelude and gives context. Part Two discusses the fundamental normative standard in behavioral public policy: true preferences. I then proceed to outline the causes of the divergence between true preferences and actual observed preferences. Part Three analyzes some of the knowledge problems is ascertaining the presence of cognitive and behavioral biases. Part Four presents a case study of the difficulties of behavioral policy analysis in the area of consumer credit. Part Five concludes.

Analyzing the Politics of Legislative Debate

This chapter considers why we should be interested in the politics of legislative debate. What does the analysis of legislative debate contribute to our understanding of politics more generally? This is particularly important given that legislative debate is not actually “debate” in any meaningful sense of the word, and that most legislators are not even present when most legislative speeches are made. The answers offered here rest on the assumption that speeches in the legislature allow legislators to commit to policy positions on the official record. If the main concern is politics between parties, debate speeches tend to concern actual policy implementation, likely closer to “true” preferences than electoral aspirations and promises. If the prime concern is politics within parties, debate speeches can give insight into internal party policy divisions, even in settings where the final legislative party vote is tightly whipped.

Predicted preference conjoint analysis

In this paper we propose a new method of eliciting market research information. Instead of asking respondents for their personal choices and preferences, we ask respondents to predict the choices of other respondents to the survey. Such predictions tap respondents’ knowledge of peers, whether based on direct social contacts or on more general cultural information. The effectiveness of this approach has already been demonstrated in the context of political polling. Here we extend it to market research, specifically, to conjoint analysis. An advantage of the new approach is that it can elicit reliable responses in situations where people are not comfortable with disclosing their true preferences, but may be willing to give information about people around them. A theoretical argument demonstrates that predictions should yield utility estimates that are more accurate. These theoretical results are confirmed in four online experiments.

A Neural Network Auction For Group Decision Making Over a Continuous Space

We propose a system for conducting an auction over locations in a continuous space. It enables participants to express their preferences over possible choices of location in the space, selecting the location that maximizes the total utility of all agents. We prevent agents from tricking the system into selecting a location that improves their individual utility at the expense of others by using a pricing rule that gives agents no incentive to misreport their true preferences. The system queries participants for their utility in many random locations, then trains a neural network to approximate the preference function of each participant. The parameters of these neural network models are transmitted and processed by the auction mechanism, which composes these into differentiable models that are optimized through gradient ascent to compute the final chosen location and charged prices.

Response to “Misunderstanding the Match: Do Students Create Rank Lists Based on True Preferences?”

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Implicit attitudes matter for social judgments of others’ preference, but do not make those judgments more or less accurate

Drawing from a large dataset of responses to implicit and explicit attitude measures and social judgments of others’ preferences (N = 97,176) across 95 distinct attitude domains, this Registered Report utilized a componential analysis of judgment accuracy to examine whether implicit attitudes affected the accuracy of social judgment. I found evidence that judgments of the population’s preferences were associated with the population’s true implicit (but not explicit) attitudes, and that individuals projected their implicit attitudes in addition to the projection of explicit attitudes when judging the population’s true preferences. However, I found no evidence that stronger or weaker implicit attitudes were uniquely associated with greater or less accuracy in judging the population’s true preferences. These results provide generalizable evidence that implicit attitudes matter greatly for social judgment accuracy in distinct and nuanced ways.

Prenatal Counseling for Maternal–Fetal Surgery

This chapter analyzes potential biases and competing interests in prenatal counseling when conditions are diagnosed for which intrauterine surgery may be possible. Such counseling often occurs at the multidimensional interface of obstetrics and pediatrics. After considering clinical, social, and historical contexts of such counseling, the chapter presents a case that illustrates how physician demographics, interspecialty differences, divergent clinical experiences, and larger organizational factors may compound practice variation. It considers how biased counseling may influence patients’ decisions and questions whether value-neutral counseling is attainable when such fetal conditions are diagnosed. It concludes that declared commitments to value neutrality cannot insulate pregnant patients from biases and competing interests. In its recommendations, it discusses organizational responses analogous to conflict-of-interest policies. It further suggests that conversations between clinicians and patients that are mutually open about values may enhance rather than undermine patients’ ability to formulate decisions that most closely embody their true preferences.

Strategyproof Mechanisms for Additively Separable and Fractional Hedonic Games

Additively separable hedonic games and fractional hedonic games have received considerable attention in the literature. They are coalition formation games among selfish agents based on their mutual preferences. Most of the work in the literature characterizes the existence and structure of stable outcomes (i.e., partitions into coalitions) assuming that preferences are given. However, there is little discussion of this assumption. In fact, agents receive different utilities if they belong to different coalitions, and thus it is natural for them to declare their preferences strategically in order to maximize their benefit. In this paper we consider strategyproof mechanisms for additively separable hedonic games and fractional hedonic games, that is, partitioning methods without payments such that utility maximizing agents have no incentive to lie about their true preferences. We focus on social welfare maximization and provide several lower and upper bounds on the performance achievable by strategyproof mechanisms for general and specific additive functions. In most of the cases we provide tight or asymptotically tight results. All our mechanisms are simple and can be run in polynomial time. Moreover, all the lower bounds are unconditional, that is, they do not rely on any computational complexity assumptions.