  Humans use language to collectively execute abstract strategies besides using
it as a referential tool for identifying physical entities. Recently, multiple
attempts at replicating the process of emergence of language in artificial
agents have been made. While existing approaches study emergent languages as
referential tools, in this paper, we study their role in discovering and
implementing strategies. We formulate the problem using a voting game where two
candidate agents contest in an election with the goal of convincing population
members (other agents), that are connected to each other via an underlying
network, to vote for them. To achieve this goal, agents are only allowed to
exchange messages in the form of sequences of discrete symbols to spread their
propaganda. We use neural networks with Gumbel-Softmax relaxation for sampling
categorical random variables to parameterize the policies followed by all
agents. Using our proposed framework, we provide concrete answers to the
following questions: (i) Do the agents learn to communicate in a meaningful way
and does the emergent communication play a role in deciding the winner? (ii)
Does the system evolve as expected under various reward structures? (iii) How
is the emergent language affected by the community structure in the network? To
the best of our knowledge, we are the first to explore emergence of
communication for discovering and implementing strategies in a setting where
agents communicate over a network.
