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Abstract

We study the coordination in a group of humans by means of experiments and simulations. Experiments
with human participants were implemented in a multiclient game setting, where players move on a virtual
hexagonal lattice, can observe their and other players’ positions on a screen, and receive a payoff for reaching
specific goals on the playing field. Flocking behavior was incentivized by larger payoffs if multiple players
reached the same goal field. We choose two complementary simulation methods to explain the experimental
data: a minimal cognitive force approach, based on the maximization of future movement options in the agents’
local environment, and multiagent reinforcement learning (RL), which learns behavioral policies to maximize
reward based on past observations. Comparison between experimental and computer simulation data suggests
that group coordination in humans can be achieved through nonspecific, information-based strategies. We also
find that although the RL approach can capture some key aspects of the experimental results, it achieves lower
performance compared to both the cognitive force simulation and the experiment, and matches the observed
human behavior less closely.