Exploration-exploitation trade-off features a saltatory search behaviour.

Area of Science:

• Human behavior research
• Statistical physics
• Cognitive science

Background:

• Understanding human movement patterns is crucial for various fields, including ecology, urban planning, and behavioral economics.
• The exploration-exploitation trade-off, a fundamental concept in decision-making, describes the dilemma between searching for new opportunities and exploiting existing ones.

Purpose of the Study:

• To investigate the scale-free nature of human searching activity in virtual environments.
• To develop and analytically solve a statistical model for the human exploration-exploitation trade-off.
• To characterize the different types of movement behaviors predicted by the model.

Main Methods:

• Conducting searching experiments in diverse virtual environments with a gender-balanced participant group.
• Developing a novel analytical statistical model of the coherent-noise type.
• Solving the model to derive predictions about movement patterns.

Main Results:

• Human searching activity exhibits scale-free spread, independent of gender, across large spatio-temporal scales.
• The developed statistical model analytically describes the exploration-exploitation trade-off.
• The model predicts various saltatory behaviors, including Lévy flights under uncertainty and Brownian walks when confidence is high.

Conclusions:

• Human searching behavior demonstrates universal, gender-independent characteristics.
• The proposed model provides a robust framework for understanding decision-making in exploration and exploitation.
• The model's ability to predict diverse movement patterns highlights its explanatory power for human navigation and foraging strategies.