Abstract
Consider a wildlife photographer that has just entered a rainforest that she has never visited. Looking for a good spot for animal photos, she can spend all her time in the first hideout that she found, slowly learning which animals visit that spot. Alternatively, she can consider other locations, which are potentially better but might also be worse. To identify these better locations she needs to leave her hideout and walk further into the forest, thus missing the opportunity to learn more about the qualities of her first hideout. How should she explore the forest? How does she explore it? Here we describe the computational principles and algorithms underlying exploration in the field of Machine Learning and discuss their relevance to human behavior.
| Original language | American English |
|---|---|
| Pages (from-to) | 104-111 |
| Number of pages | 8 |
| Journal | Current Opinion in Behavioral Sciences |
| Volume | 35 |
| DOIs | |
| State | Published - Oct 2020 |
Bibliographical note
Funding Information:This work was supported by the Israel Science Foundation (Grants 757/16 and 3213/19 ), and by the Gatsby Charitable Foundation . Lotem Elber-Dorozko is grateful to the Azrieli Foundation for the award of an Azrieli Fellowship and Ohad Dan would like to acknowledge the support of the The Hoffman Leadership and Responsibility Fellowship Program . We thank Gianluigi Mongillo for carefully reading the manuscript and for his helpful comments.
Publisher Copyright:
© 2020
