  A multiagent system is a society of autonomous agents whose interactions can
be regulated via social norms. In general, the norms of a society are not
hardcoded but emerge from the agents' interactions. Specifically, how the
agents in a society react to each other's behavior and respond to the reactions
of others determines which norms emerge in the society. We think of these
reactions by an agent to the satisfactory or unsatisfactory behaviors of
another agent as communications from the first agent to the second agent.
Understanding these communications is a kind of social intelligence: these
communications provide natural drivers for norm emergence by pushing agents
toward certain behaviors, which can become established as norms. Whereas it is
well-known that sanctioning can lead to the emergence of norms, we posit that a
broader kind of social intelligence can prove more effective in promoting
cooperation in a multiagent system.
  Accordingly, we develop Nest, a framework that models social intelligence via
a wider variety of communications and understanding of them than in previous
work. To evaluate Nest, we develop a simulated pandemic environment and conduct
simulation experiments to compare Nest with baselines considering a combination
of three kinds of social communication: sanction, tell, and hint.
  We find that societies formed of Nest agents achieve norms faster. Moreover,
Nest agents effectively avoid undesirable consequences, which are negative
sanctions and deviation from goals, and yield higher satisfaction for
themselves than baseline agents despite requiring only an equivalent amount of
information.
