Effects of feedback and complexity on repeated decisions from description

Tomás Lejarraga and Cleotilde Gonzalez

Organizational Behavior and Human Decision Processes, 2011, vol. 116, issue 2, 286-295

Abstract: Recent evidence from research in risky choice shows that decisions from experience differ significantly from decisions from description, particularly when problems include a small-probability event and when samples from experience are small. Little is known, however, about the cognitive processes behind repeated decisions where both descriptive and experiential information are available for the decision maker. While previous findings suggest that feedback makes choices “deviate” from the predictions of prospect theory (Jessup, Bishara, & Busemeyer, 2008), we find a stronger effect: Our results suggest that information from description is neglected in the presence of feedback. Moreover, we find that in the presence of feedback, descriptions are overlooked irrespective of the level of complexity of the decision scenario. We show that an instance-based learning model and a reinforcement learning model account for the observed decisions by solely relying on observed outcomes. We discuss our findings in the context of organizational behavior.

Keywords: Decisions from experience; Instance-based learning theory; Feedback; Binary choice; Foregone payoffs (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jobhdp:v:116:y:2011:i:2:p:286-295

DOI: 10.1016/j.obhdp.2011.05.001

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