Balance between breadth and depth in human many-alternative decisions.

Area of Science:

• Cognitive Science
• Decision Science
• Behavioral Economics

Background:

• Everyday decisions involve allocating finite resources (e.g., time, attention) to evaluate multiple options.
• Balancing breadth (sampling many options) versus depth (sampling few options) is crucial for maximizing utility.

Purpose of the Study:

• To investigate how humans practically balance breadth and depth in many-alternative decisions under resource constraints.
• To identify human strategies and deviations from optimal resource allocation.

Main Methods:

• Developed a novel experimental paradigm simulating a many-alternative choice scenario.
• Participants allocated a finite budget to sample apricot suppliers to estimate fruit quality.

Main Results:

• Low budget capacity led to breadth-seeking (sampling many suppliers).
• High budget capacity shifted behavior towards depth-seeking (sampling few suppliers intensively).
• Sampling increased with capacity via a power law; richer environments favored depth.

Conclusions:

• Human resource allocation in complex choices exhibits rich, near-optimal behavior with identifiable biases.
• Individuals adapt their breadth-depth strategy based on resource availability and environmental richness.