Exploring the space of human exploration using Entropy Mastermind

What drives peoples exploration in complex scenarios where they have to actively acquire information by making queries? How do people adapt their selection of queries to their environment? We explore these questions using Entropy Mastermind, a novel variant of the Mastermind code-breaking game, in which participants have to guess a secret code by making useful queries. Participants solved games more efficiently and more quickly if the entropy of the game environment was low; moreover, people adapted their initial queries to the scenario they were in. We also investigated whether it would be possible to predict participants queries within the generalized Sharma-Mittal information-theoretic framework. Although predicting individual queries is difficult, the modeling framework offered important insight on human behavior. Entropy Mastermind offers rich possibilities for modeling and behavioral research.