57 research outputs found
Econometrics for Learning Agents
The main goal of this paper is to develop a theory of inference of player
valuations from observed data in the generalized second price auction without
relying on the Nash equilibrium assumption. Existing work in Economics on
inferring agent values from data relies on the assumption that all participant
strategies are best responses of the observed play of other players, i.e. they
constitute a Nash equilibrium. In this paper, we show how to perform inference
relying on a weaker assumption instead: assuming that players are using some
form of no-regret learning. Learning outcomes emerged in recent years as an
attractive alternative to Nash equilibrium in analyzing game outcomes, modeling
players who haven't reached a stable equilibrium, but rather use algorithmic
learning, aiming to learn the best way to play from previous observations. In
this paper we show how to infer values of players who use algorithmic learning
strategies. Such inference is an important first step before we move to testing
any learning theoretic behavioral model on auction data. We apply our
techniques to a dataset from Microsoft's sponsored search ad auction system
Identification, data combination and the risk of disclosure
Businesses routinely rely on econometric models to analyze and predict consumer behavior. Estimation of such models may require combining a firm's internal data with external datasets to take into account sample selection, missing observations, omitted variables and errors in measurement within the existing data source. In this paper we point out that these data problems can be addressed when estimating econometric models from combined data using the data mining techniques under mild assumptions regarding the data distribution. However, data combination leads to serious threats to security of consumer data: we demonstrate that point identification of an econometric model from combined data is incompatible with restrictions on the risk of individual disclosure. Consequently, if a consumer model is point identified, the firm would (implicitly or explicitly) reveal the identity of at least some of consumers in its internal data. More importantly, we provide an argument that unless the firm places a restriction on the individual disclosure risk when combining data, even if the raw combined dataset is not shared with a third party, an adversary or a competitor can gather confidential information regarding some individuals from the estimated model.
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