Online games: a novel approach to explore how partial information influences human random searches

TL;DR

This study uses an online game to analyze how partial information and cues influence human search strategies, revealing optimized search behaviors and the evolution of strategies with initial knowledge.

Contribution

Introduces an experimental online game to investigate human search behavior under partial information, supported by models and analytical approximations.

Findings

Search is optimized for cues at intermediate spatial scales without initial info.

Initial information accelerates search times.

Informed strategies evolve into non-stationary processes with characteristic scales.

Abstract

Many natural processes rely on optimizing the success ratio of a search process. We use an experimental setup consisting of a simple online game in which players have to find a target hidden on a board, to investigate the how the rounds are influenced by the detection of cues. We focus on the search duration and the statistics of the trajectories traced on the board. The experimental data are explained by a family of random-walk-based models and probabilistic analytical approximations. If no initial information is given to the players, the search is optimized for cues that cover an intermediate spatial scale. In addition, initial information about the extension of the cues results, in general, in faster searches. Finally, strategies used by informed players turn into non-stationary processes in which the length of each displacement evolves to show a well-defined characteristic scale that is not found in non-informed searches.