Toward Choice-Theoretic Foundations for Behavioral Welfare Economics

Interest in behavioral economics has grown in recent years, stimulated largely by accumulating evidence that the standard model of consumer decision making provides an inadequate, positive description of human behavior. Behavioral models are increasingly finding their way into policy evaluation, which inevitably involves welfare analysis. No consensus concerning the appropriate standards and criteria for behavioral welfare analysis has emerged yet. This paper summarizes our effort to develop a unified framework for behavioral welfare economics (for a detailed discussion see Bernheim and Rangel 2007) — one that can be viewed as a natural extension of standard welfare economics. Standard welfare analysis is based on choice, not on utility or preferences. In its simplest form, it instructs the planner to respect the choices an individual would make for himself. The guiding normative principle is an extension of the libertarian deference to freedom of choice, which takes the view that it is better to give a person the thing he would choose for himself rather than something that someone else would choose for him. We show that it is possible to extend the standard choice-theoretic approach to welfare analysis to situations where individuals make inconsistent choices, which are prevalent in behavioral economics

Perturbing Inputs to Prevent Model Stealing

We show how perturbing inputs to machine learning services (ML-service) deployed in the cloud can protect against model stealing attacks. In our formulation, there is an ML-service that receives inputs from users and returns the output of the model. There is an attacker that is interested in learning the parameters of the ML-service. We use the linear and logistic regression models to illustrate how strategically adding noise to the inputs fundamentally alters the attacker's estimation problem. We show that even with infinite samples, the attacker would not be able to recover the true model parameters. We focus on characterizing the trade-off between the error in the attacker's estimate of the parameters with the error in the ML-service's output

Probabilistic Default Reasoning with Conditional Constraints

We propose a combination of probabilistic reasoning from conditional constraints with approaches to default reasoning from conditional knowledge bases. In detail, we generalize the notions of Pearl's entailment in system Z, Lehmann's lexicographic entailment, and Geffner's conditional entailment to conditional constraints. We give some examples that show that the new notions of z-, lexicographic, and conditional entailment have similar properties like their classical counterparts. Moreover, we show that the new notions of z-, lexicographic, and conditional entailment are proper generalizations of both their classical counterparts and the classical notion of logical entailment for conditional constraints.Comment: 8 pages; to appear in Proceedings of the Eighth International Workshop on Nonmonotonic Reasoning, Special Session on Uncertainty Frameworks in Nonmonotonic Reasoning, Breckenridge, Colorado, USA, 9-11 April 200

The Welfare Economics of Default Options in 401(k) Plans

Default contribution rates for 401(k) pension plans powerfully influence workers’ choices. Potential causes include opt-out costs, procrastination, inattention, and psychological anchoring. We examine the welfare implications of defaults under each theory using the framework for behavioral welfare economics developed by Bernheim and Rangel (2009). We show how the optimal default, the magnitude of the welfare effects, and the degree of normative ambiguity depend on the behavioral model, the scope of the choice domain deemed welfare-relevant, the use of penalties for passive choice, and other 401(k) plan features. In some settings, non-participation emerges as the optimal default, contrary to common wisdom.