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Related Experiment Videos

Suboptimal tradeoffs in information seeking.

Wai-Tat Fu1, Wayne D Gray

  • 1Carnegie Mellon University, USA. wfu@cmu.edu

Cognitive Psychology
|December 17, 2005
PubMed
Summary
This summary is machine-generated.

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People often stop seeking information too early in problem-solving, leading to suboptimal performance. A Bayesian satisficing model (BSM) explains this behavior by showing how simple rules adapt to information costs and structures.

Area of Science:

  • Cognitive Science
  • Decision Making
  • Human-Computer Interaction

Background:

  • Problem-solving requires information seeking, which involves trade-offs between information costs and utility.
  • Understanding how individuals adapt to information-rich environments is crucial for designing effective decision-support systems.

Purpose of the Study:

  • To investigate how individuals adapt their information-seeking behavior in response to varying costs and information structures.
  • To develop and validate a computational model that predicts information-seeking behavior in problem-solving tasks.

Main Methods:

  • Three experiments were conducted using a map-navigation task to observe human information-seeking strategies.
  • A Bayesian satisficing model (BSM) was implemented within the ACT-R cognitive architecture to simulate decision-making processes.

Related Experiment Videos

  • The BSM employed a local decision rule and a global Bayesian learning mechanism to determine when to cease information seeking.

    • Main Results:

    • Participants frequently exhibited suboptimal performance, stabilizing at levels below optimal outcomes.
    • The Bayesian satisficing model (BSM) demonstrated a strong ability to predict human information-seeking behavior.
    • Adaptation to environmental cost and information structures was achieved through a simple local decision rule.

    Conclusions:

    • Suboptimal performance in information-seeking tasks can emerge from the dynamic interplay between cognitive processes and environmental factors.
    • A simple local decision rule, while facilitating adaptation, can limit exploration and lead to suboptimal outcomes.
    • The BSM provides a valuable framework for understanding and predicting information-seeking behavior in complex environments.