  Iterated coopetitive games capture the situation when one must efficiently
balance between cooperation and competition with the other agents over time in
order to win the game (e.g., to become the player with highest total utility).
Achieving this balance is typically very challenging or even impossible when
explicit communication is not feasible (e.g., negotiation or bargaining are not
allowed). In this paper we investigate how an agent can achieve this balance to
win in iterated coopetitive polymatrix games, without explicit communication.
In particular, we consider a 3-player repeated game setting in which our agent
is allowed to (slightly) manipulate the underlying game matrices of the other
agents for which she pays a manipulation cost, while the other agents satisfy
weak behavioural assumptions. We first propose a payoff matrix manipulation
scheme and sequence of strategies for our agent that provably guarantees that
the utility of any opponent would converge to a value we desire. We then use
this scheme to design winning policies for our agent. We also prove that these
winning policies can be found in polynomial running time. We then turn to
demonstrate the efficiency of our framework in several concrete coopetitive
polymatrix games, and prove that the manipulation costs needed to win are
bounded above by small budgets. For instance, in the social distancing game, a
polymatrix version of the lemonade stand coopetitive game, we showcase a policy
with an infinitesimally small manipulation cost per round, along with a
provable guarantee that, using this policy leads our agent to win in the
long-run. Note that our findings can be trivially extended to $n$-player game
settings as well (with $n > 3$).
