Abstract
This paper proposes a new negotiation game, based
on the weighted voting paradigm in cooperative
game theory, where agents need to form coalitions
and agree on how to share the gains. Despite the
prevalence of weighted voting in the real world,
there has been little work studying people’s behavior in such settings. We show that solution concepts
from cooperative game theory (in particular, an extension of the Deegan-Packel Index) provide a good
prediction of people’s decisions to join coalitions in
an online version of a weighted voting game. We
design an agent that combines supervised learning
with decision theory to make offers to people in
this game. We show that the agent was able to obtain higher shares from coalitions than did people
playing other people, without reducing the acceptance rate of its offers. We also find that people display certain biases in weighted voting settings, like
creating unnecessarily large coalitions, and not rewarding strong players. These results demonstrate
the benefit of incorporating concepts from cooperative game theory in the design of agents that interact
with other people in weighted voting systems.