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The enormous scale of the available information and productson the Internet has necessitated the development of algorithmsthat intermediate between options and human users. These al-gorithms do not select information at random, but attempt toprovide the user with relevant information. In doing so, thealgorithms may incur potential negative consequences relatedto, for example, “filter bubbles.” Building from existing al-gorithms, we introduce a parametrized model that unifies andinterpolates between recommending relevant information andactive learning. In a concept learning paradigm, we illustratethe trade-offs of optimizing prediction and recommendation,show that there is a broad parameter region of stable perfor-mance that optimizes for both, identify a specific regime thatis most robust to human variability, and identify the cause ofthis optimized performance. We conclude by discussing im-plications for the cognitive science of concept learning and thepractice of machine learning in the real world.

Researchers have debated whether instructional-based teach-ing or exploration-based active learning is better for decadeswith unsatisfying results. A main obstacle is the difficulty inprecisely controlling and characterizing the pedagogical meth-ods used and the learning conditions in empirical studies. Toaddress this, we leveraged existing computational models ofteaching and active learning to formalize the methods and thelearning process. We compared the two pedagogical methodsin a concept-learning framework and investigated their effec-tiveness under various scenarios. Our results show that whenthe learner and teacher are conceptually aligned, teaching isat least as effective as, and often much more effective than ac-tive learning, but when the alignment is broken, active learningcan yield moderate improvement over teaching. We concludeby discussing our results’ implications for the debate and theprospects of bringing computational models to bear on com-plex real-world problems that are resistant to simple experi-mental investigation.

The enormous scale of the available information and products on the Internet has necessitated the development of algorithms that intermediate between options and human users. These algorithms attempt to provide the user with relevant information. In doing so, the algorithms may incur potential negative consequences stemming from the need to select items about which it is uncertain to obtain information about users versus the need to select items about which it is certain to secure high ratings. This tension is an instance of the exploration–exploitation trade‐off in the context of recommender systems. Because humans are in this interaction loop, the long‐term trade‐off behavior depends on human variability. Our goal is to characterize the trade‐off behavior as a function of human variability fundamental to such human–algorithm interaction. To tackle the characterization, we first introduce a unifying model that smoothly transitions between active learning and recommending relevant information. The unifying model gives us access to a continuum of algorithms along the exploration–exploitation trade‐off. We then present two experiments to measure the trade‐off behavior under two very different levels of human variability. The experimental results inform a thorough simulation study in which we modeled and varied human variability systematically over a wide rage. The main result is that exploration–exploitation trade‐off grows in severity as human variability increases, but there exists a regime of low variability where algorithms balanced in exploration and exploitation can largely overcome the trade‐off. [ABSTRACT FROM AUTHOR]

Traditionally, learning has been modeled as passively obtaining information or actively exploring the environment. Recent research has introduced models of learning from teachers that involve reasoning about why they have selected particular evidence. We introduce a computational framework that takes a critical step toward unifying active learning and teaching by recognizing that meta‐reasoning underlying reasoning about others can be applied to reasoning about oneself. The resulting Self‐Teaching model captures much of the behavior of information‐gain‐based active learning with elements of hypothesis‐testing‐based active learning and can thus be considered as a formalization of active learning within the broader teaching framework. We present simulation experiments that characterize the behavior of the model within three simple and well‐investigated learning problems. We conclude by discussing such theory‐of‐mind‐based learning in the context of core cognition and cognitive development. According to rational pedagogy models, learners take into account the way in which teachers generate evidence, and teachers take into account the way in which learners assimilate that evidence. The authors develop a framework for integrating rational pedagogy into models of active exploration, in which agents can take actions to influence the evidence they gather from the environment. The key idea is that a single agent can be both teacher and learner. [ABSTRACT FROM AUTHOR]