Efficiently Learning from Revealed Preference

In this paper, we consider the revealed preferences problem from a learning perspective. Every day, a price vector and a budget is drawn from an unknown distribution, and a rational agent buys his most preferred bundle according to some unknown utility function, subject to the given prices and budget constraint. We wish not only to find a utility function which rationalizes a finite set of observations, but to produce a hypothesis valuation function which accurately predicts the behavior of the agent in the future. We give efficient algorithms with polynomial sample-complexity for agents with linear valuation functions, as well as for agents with linearly separable, concave valuation functions with bounded second derivative.Comment: Extended abstract appears in WINE 201

Learning Economic Parameters from Revealed Preferences

A recent line of work, starting with Beigman and Vohra (2006) and Zadimoghaddam and Roth (2012), has addressed the problem of {\em learning} a utility function from revealed preference data. The goal here is to make use of past data describing the purchases of a utility maximizing agent when faced with certain prices and budget constraints in order to produce a hypothesis function that can accurately forecast the {\em future} behavior of the agent. In this work we advance this line of work by providing sample complexity guarantees and efficient algorithms for a number of important classes. By drawing a connection to recent advances in multi-class learning, we provide a computationally efficient algorithm with tight sample complexity guarantees ($\Theta(d/\epsilon)$ for the case of $d$ goods) for learning linear utility functions under a linear price model. This solves an open question in Zadimoghaddam and Roth (2012). Our technique yields numerous generalizations including the ability to learn other well-studied classes of utility functions, to deal with a misspecified model, and with non-linear prices

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

Infants' Selectively Pay Attention to the Information They Receive from a Native Speaker of Their Language

From the first moments of their life, infants show a preference for their native language, as well as toward speakers with whom they share the same language. This preference appears to have broad consequences in various domains later on, supporting group affiliations and collaborative actions in children. Here, we propose that infants' preference for native speakers of their language also serves a further purpose, specifically allowing them to efficiently acquire culture specific knowledge via social learning. By selectively attending to informants who are native speakers of their language and who probably also share the same cultural background with the infant, young learners can maximize the possibility to acquire cultural knowledge. To test whether infants would preferably attend the information they receive from a speaker of their native language, we familiarized 12-month-old infants with a native and a foreign speaker, and then presented them with movies where each of the speakers silently gazed toward unfamiliar objects. At test, infants' looking behavior to the two objects alone was measured. Results revealed that infants preferred to look longer at the object presented by the native speaker. Strikingly, the effect was replicated also with 5-month-old infants, indicating an early development of such preference. These findings provide evidence that young infants pay more attention to the information presented by a person with whom they share the same language. This selectivity can serve as a basis for efficient social learning by influencing how infants' allocate attention between potential sources of information in their environment

The Assistive Multi-Armed Bandit

Learning preferences implicit in the choices humans make is a well studied problem in both economics and computer science. However, most work makes the assumption that humans are acting (noisily) optimally with respect to their preferences. Such approaches can fail when people are themselves learning about what they want. In this work, we introduce the assistive multi-armed bandit, where a robot assists a human playing a bandit task to maximize cumulative reward. In this problem, the human does not know the reward function but can learn it through the rewards received from arm pulls; the robot only observes which arms the human pulls but not the reward associated with each pull. We offer sufficient and necessary conditions for successfully assisting the human in this framework. Surprisingly, better human performance in isolation does not necessarily lead to better performance when assisted by the robot: a human policy can do better by effectively communicating its observed rewards to the robot. We conduct proof-of-concept experiments that support these results. We see this work as contributing towards a theory behind algorithms for human-robot interaction.Comment: Accepted to HRI 201

Economics, Biology, and Culture: Hodgson on History

This book addresses what the author claims, with considerable justification, to be the foremost challenge confronting the social and behavioral sciences today: the problem of historical specificity. Hodgson poses the question by asking whether we need different theories to understand social and economic behavior in different societies at different stages of their development. He answers the question in the affirmative, and criticizes the economics profession for suggesting that there is one universal model or theory equally suited to all economies and societies at all times. He faults the profession further for no longer worrying much or conducting serious debate about this issue, a development he attributes to the eclipse and eventual demise of institutionalism and historical economics in England, Germany, and the United States