Predecisional information search adaptively reduces three types of uncertainty

Area of Science:

• Cognitive Psychology
• Decision Science
• Behavioral Economics

Background:

• Previous research suggests decision-making involves reducing uncertainty.
• The precise mechanisms and consistency of information search strategies remain unclear.

Purpose of the Study:

• To analyze information search decisions in a free exploration setting.
• To identify the drivers of predecisional information search and their underlying uncertainties.
• To investigate the adaptive and individual nature of information search strategies.

Main Methods:

• Analysis of over 1,000,000 information-search decisions from 2,500+ individuals.
• Utilized a decisions-from-experience paradigm separating information search from choice.
• Employed a data-driven approach with a formal measurement framework.

Main Results:

• Identified three primary drivers of information search: structural, estimation, and computational uncertainty reduction.
• Demonstrated that information search selection is adaptive, sequential, and influenced by environmental knowledge.
• Observed significant interindividual heterogeneity in the recruitment of information search drivers.

Conclusions:

• Human information search is complex, driven by multiple, context-dependent factors.
• Monolithic accounts focusing solely on estimation uncertainty or cost-benefit analysis are insufficient.
• Broader theories are required to encompass the multifaceted nature of human information-search behavior.