Aversion to option loss in a restless bandit task

Contributors:
  1. Peter Tran

Date created: | Last Updated:

: DOI | ARK

Creating DOI. Please wait...

Create DOI

Category: Project

Description: In everyday life people need to make choices without full information about the environment, which poses an explore-exploit dilemma in which one needs to balance the need to learn about the world and the need to obtain rewards from it. The explore-exploit dilemma is often studied using the multi-armed restless bandit task, in which people repeatedly select from multiple options, and human behaviour is modelled as a form of reinforcement learning via Kalman filters. Inspired by work in the judgment and decision-making literature, we present two experiments using multi- armed bandit tasks in both static and dynamic environments, in situations where options can become unviable and vanish if they are not pursued. A Kalman filter model using Thompson sampling provides an excellent account of human learning in a standard restless bandit task, but there are systematic departures in the vanishing bandit task. We estimate the structure of this loss aversion signal and consider theoretical explanations for the results.

License: CC-By Attribution 4.0 International

This project represents a pending preprint submitted to PsyArXiv . Learn more about how to work with preprint files. View preprint

Files

Loading files...

Citation

Tags

Recent Activity

Loading logs...

This website relies on cookies to help provide a better user experience. By clicking Accept or continuing to use the site, you agree. For more information, see our Privacy Policy and information on cookie use.
Accept
×

Start managing your projects on the OSF today.

Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.

Create an Account Learn More Hide this message