Truthful Linear Regression

We consider the problem of fitting a linear model to data held by individuals who are concerned about their privacy. Incentivizing most players to truthfully report their data to the analyst constrains our design to mechanisms that provide a privacy guarantee to the participants; we use differential privacy to model individuals' privacy losses. This immediately poses a problem, as differentially private computation of a linear model necessarily produces a biased estimation, and existing approaches to design mechanisms to elicit data from privacy-sensitive individuals do not generalize well to biased estimators. We overcome this challenge through an appropriate design of the computation and payment scheme.Comment: To appear in Proceedings of the 28th Annual Conference on Learning Theory (COLT 2015

The Empirical Implications of Privacy-Aware Choice

This paper initiates the study of the testable implications of choice data in settings where agents have privacy preferences. We adapt the standard conceptualization of consumer choice theory to a situation where the consumer is aware of, and has preferences over, the information revealed by her choices. The main message of the paper is that little can be inferred about consumers' preferences once we introduce the possibility that the consumer has concerns about privacy. This holds even when consumers' privacy preferences are assumed to be monotonic and separable. This motivates the consideration of stronger assumptions and, to that end, we introduce an additive model for privacy preferences that does have testable implications

The teller\u27s tale : the role of the storyteller in the life of the story

unavailabl

The Strange Case of Privacy in Equilibrium Models

We study how privacy technologies affect user and advertiser behavior in a simple economic model of targeted advertising. In our model, a consumer first decides whether or not to buy a good, and then an advertiser chooses an advertisement to show the consumer. The consumer's value for the good is correlated with her type, which determines which ad the advertiser would prefer to show to her---and hence, the advertiser would like to use information about the consumer's purchase decision to target the ad that he shows. In our model, the advertiser is given only a differentially private signal about the consumer's behavior---which can range from no signal at all to a perfect signal, as we vary the differential privacy parameter. This allows us to study equilibrium behavior as a function of the level of privacy provided to the consumer. We show that this behavior can be highly counter-intuitive, and that the effect of adding privacy in equilibrium can be completely different from what we would expect if we ignored equilibrium incentives. Specifically, we show that increasing the level of privacy can actually increase the amount of information about the consumer's type contained in the signal the advertiser receives, lead to decreased utility for the consumer, and increased profit for the advertiser, and that generally these quantities can be non-monotonic and even discontinuous in the privacy level of the signal

Politics and the FOMC: Do Political Preferences Influence the Decisions of Central Bankers?

This thesis tests empirically whether the political preferences of Federal Open Market Committee (FOMC) members, indicated by party affiliation, the partisan direction of donations to political campaigns, and the party of the President affect their voting behavior when setting monetary policy. I use two main empirical strategies in this project. The first is a linear probability model that examines the correlation between a range of background characteristics of FOMC members--including political affiliation, educational attainment, and work background--on the probability of casting a dissent vote against the majority decision of the FOMC at a particular meeting. The second approach controls for the state of the economy and focuses on whether an FOMC member’s vote on interest rates at a particular meeting was for an increase, a decrease, or no change. To control for the state of the economy and its effect on FOMC interest rate decisions, I use predictions from Taylor-like rules that translate measures of economic activity and inflation into prescriptions for interest rates. I then use a multinominal logit specification to assess how partisan affiliation (and several other factors) affect voting choices after controlling for the Taylor-Rule prescriptions. To implement both empirical strategies for this analysis, I constructed a unique data set that ranges from 1970 to 2018, where each observation is a person-meeting. My somewhat surprising results indicate that partisanship emerges based on the party of the sitting President rather than through the party affiliation of FOMC members. In particular, during Republican Administrations, FOMC members downweight the signal from economic conditions when considering decreases in interest rates and also are considerably more likely to vote for rate decreases than is the case during Democratic Administrations. Additionally, I find that my bank president variable is no longer significant, which is surprising because the prior literature finds that bank presidents are hawkish

Online Learning and Profit Maximization from Revealed Preferences

We consider the problem of learning from revealed preferences in an online setting. In our framework, each period a consumer buys an optimal bundle of goods from a merchant according to her (linear) utility function and current prices, subject to a budget constraint. The merchant observes only the purchased goods, and seeks to adapt prices to optimize his profits. We give an efficient algorithm for the merchant's problem that consists of a learning phase in which the consumer's utility function is (perhaps partially) inferred, followed by a price optimization step. We also consider an alternative online learning algorithm for the setting where prices are set exogenously, but the merchant would still like to predict the bundle that will be bought by the consumer for purposes of inventory or supply chain management. In contrast with most prior work on the revealed preferences problem, we demonstrate that by making stronger assumptions on the form of utility functions, efficient algorithms for both learning and profit maximization are possible, even in adaptive, online settings