Less is more: information needs, information wants, and what makes causal models useful

Samantha Kleinberg, Jessecae K. Marsh

Research output: Contribution to journalArticlepeer-review

Abstract

Each day people make decisions about complex topics such as health and personal finances. Causal models of these domains have been created to aid decisions, but the resulting models are often complex and it is not known whether people can use them successfully. We investigate the trade-off between simplicity and complexity in decision making, testing diagrams tailored to target choices (Experiments 1 and 2), and with relevant causal paths highlighted (Experiment 3), finding that simplicity or directing attention to simple causal paths leads to better decisions. We test the boundaries of this effect (Experiment 4), finding that including a small amount of information beyond that related to the target answer has a detrimental effect. Finally, we examine whether people know what information they need (Experiment 5). We find that simple, targeted, information still leads to the best decisions, while participants who believe they do not need information or seek out the most complex information performed worse.

Original languageEnglish
Article number57
JournalCognitive Research: Principles and Implications
Volume8
Issue number1
DOIs
StatePublished - Dec 2023

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience

Keywords

  • Causal models
  • Decision-making
  • Information complexity

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