Group formation on a small-world: experiment and modelling

TL;DR

This study investigates how humans coordinate in a small-world network game, developing a data-driven agent model to compare human and agent behaviors and understand decision-making processes.

Contribution

It introduces a probability matching-based agent model to simulate human decision-making in group formation on small-world networks.

Findings

Humans use limited neighborhood information but perceive risk optimally.

Agents can outperform humans in certain parameter settings.

The model enables simulation of mixed human-agent collectives.

Abstract

As a step towards studying human-agent collectives we conduct an online game with human participants cooperating on a network. The game is presented in the context of achieving group formation through local coordination. The players set initially to a small world network with limited information on the location of other players, coordinate their movements to arrange themselves into groups. To understand the decision making process we construct a data-driven model of agents based on probability matching. The model allows us to gather insight into the nature and degree of rationality employed by the human players. By varying the parameters in agent based simulations we are able to benchmark the human behaviour. We observe that while the players utilize the neighbourhood information in limited capacity, the perception of risk is optimal. We also find that for certain parameter ranges the agents are able to act more efficiently when compared to the human players. This approach would allow us to simulate the collective dynamics in games with agents having varying strategies playing alongside human proxies.