Learning Time Dependent Choice
This work addresses the learnability of intertemporal choice models for decision-making and economics, offering incremental improvements in theoretical bounds.
The paper tackles the problem of learning time-dependent choice models, achieving an exponential improvement in learning bounds for a large class of preference models, including exponential, hyperbolic, and quasi-hyperbolic discounting, with VC dimension growing logarithmically in time periods. It also shows that active learning with membership queries outperforms stream-based settings and PAC guarantees.
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.