Fast Model-Selection through Adapting Design of Experiments Maximizing Information Gain

To perform model-selection efficiently, we must run informative experiments. Here, we extend a seminal method for designing Bayesian optimal experiments that maximize the information gained from data collected. We introduce two computational improvements that make the procedure tractable: a search algorithm from artificial intelligence and a sampling procedure shrinking the space of possible experiments to evaluate. We collected data for five different experimental designs of a simple imperfect information game and show that experiments optimized for information gain make model-selection possible (and cheaper). We compare the ability of the optimal experimental design to discriminate among competing models against the experimental designs chosen by a "wisdom of experts" prediction experiment. We find that a simple reinforcement learning model best explains human decision-making and that subject behavior is not adequately described by Bayesian Nash equilibrium. Our procedure is general and can be applied iteratively to lab, field and online experiments.