Learning Time Dependent Choice

Authors Zachary Chase, Siddharth Prasad

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Zachary Chase
  • Department of Mathematics, California Institute of Technology, Pasadena, USA
Siddharth Prasad
  • Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, USA

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Zachary Chase and Siddharth Prasad. Learning Time Dependent Choice. In 10th Innovations in Theoretical Computer Science Conference (ITCS 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 124, pp. 62:1-62:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


We explore questions dealing with the learnability of models of choice over time. We present a large class of preference models defined by a structural criterion for which we are able to obtain an exponential improvement over previously known learning bounds for more general preference models. This in particular implies that the three most important discounted utility models of intertemporal choice - exponential, hyperbolic, and quasi-hyperbolic discounting - are learnable in the PAC setting with VC dimension that grows logarithmically in the number of time periods. We also examine these models in the framework of active learning. We find that the commonly studied stream-based setting is in general difficult to analyze for preference models, but we provide a redeeming situation in which the learner can indeed improve upon the guarantees provided by PAC learning. In contrast to the stream-based setting, we show that if the learner is given full power over the data he learns from - in the form of learning via membership queries - even very naive algorithms significantly outperform the guarantees provided by higher level active learning algorithms.

Subject Classification

ACM Subject Classification
  • Theory of computation → Models of learning
  • Intertemporal Choice
  • Discounted Utility
  • Preference Recovery
  • PAC Learning
  • Active Learning


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