Incentive Compatible Active Learning

TL;DR

This paper develops incentive compatible active learning algorithms tailored for economic experiments, enabling efficient and truthful preference and belief inference without significant increases in sample complexity.

Contribution

It introduces novel incentive compatible active learning methods for economic preference and belief elicitation, balancing truthful responses with sample efficiency.

Findings

Incentive compatible algorithms achieve fast learning rates.

Sample complexity remains low despite incentive constraints.

Applicable to preference and belief inference in economic experiments.

Abstract

We consider active learning under incentive compatibility constraints. The main application of our results is to economic experiments, in which a learner seeks to infer the parameters of a subject's preferences: for example their attitudes towards risk, or their beliefs over uncertain events. By cleverly adapting the experimental design, one can save on the time spent by subjects in the laboratory, or maximize the information obtained from each subject in a given laboratory session; but the resulting adaptive design raises complications due to incentive compatibility. A subject in the lab may answer questions strategically, and not truthfully, so as to steer subsequent questions in a profitable direction. We analyze two standard economic problems: inference of preferences over risk from multiple price lists, and belief elicitation in experiments on choice over uncertainty. In the first setting, we tune a simple and fast learning algorithm to retain certain incentive compatibility properties. In the second setting, we provide an incentive compatible learning algorithm based on scoring rules with query complexity that differs from obvious methods of achieving fast learning rates only by subpolynomial factors. Thus, for these areas of application, incentive compatibility may be achieved without paying a large sample complexity price.