  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.
