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Abstract

What drives people’s exploration in complex scenarios wherethey have to actively acquire information? How do peopleadapt their selection of queries to the environment? We explorethese questions using Entropy Mastermind, a novel variantof the Mastermind code-breaking game, in which participantshave to guess a secret code by making useful queries. Partici-pants solved games more efficiently if the entropy of the gameenvironment was low; moreover, people adapted their initialqueries to the scenario they were in. We also investigatedwhether it would be possible to predict participants’ querieswithin the generalized Sharma-Mittal information-theoreticframework. Although predicting individual queries was dif-ficult, the modeling framework offered important insights onhuman behavior. Entropy Mastermind opens up rich possibili-ties for modeling and behavioral research.