Suboptimal tradeoffs in information seeking

Wai Tat Fu, Wayne D. Gray

Research output: Contribution to journalArticlepeer-review


Explicit information-seeking actions are needed to evaluate alternative actions in problem-solving tasks. Information-seeking costs are often traded off against the utility of information. We present three experiments that show how subjects adapt to the cost and information structures of environments in a map-navigation task. We found that subjects often stabilize at suboptimal levels of performance. A Bayesian satisficing model (BSM) is proposed and implemented in the ACT-R architecture to predict information-seeking behavior. The BSM uses a local decision rule and a global Bayesian learning mechanism to decide when to stop seeking information. The model matched the human data well, suggesting that adaptation to cost and information structures can be achieved by a simple local decision rule. The local decision rule, however, often limits exploration of the environment and leads to suboptimal performance. We propose that suboptimal performance is an emergent property of the dynamic interactions between cognition and the environment.

Original languageEnglish (US)
Pages (from-to)195-242
Number of pages48
JournalCognitive Psychology
Issue number3
StatePublished - May 2006


  • ACT-R
  • Adaptive search
  • Bayesian learning
  • Cognitive modeling
  • Information seeking
  • Problem solving
  • Satisficing
  • Sequential decision making
  • Suboptimal tradeoffs

ASJC Scopus subject areas

  • Neuropsychology and Physiological Psychology
  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Linguistics and Language
  • Artificial Intelligence


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