Playing Coopetitive Polymatrix Games with Small Manipulation Cost
Shivakumar Mahesh, Nicholas Bishop, Le Cong Dinh, Long Tran-Thanh

TL;DR
This paper presents a method for an agent to win in iterated coopetitive polymatrix games by minimally manipulating game matrices without explicit communication, ensuring convergence to desired utilities and polynomial-time policy computation.
Contribution
It introduces a payoff matrix manipulation scheme and strategies that guarantee winning policies with small manipulation costs in coopetitive polymatrix games.
Findings
Proposed a provably effective manipulation scheme for winning strategies.
Demonstrated policies with infinitesimally small manipulation costs in concrete games.
Extended results to n-player settings with similar guarantees.
Abstract
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…
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Taxonomy
TopicsGame Theory and Applications · Business Strategy and Innovation · Experimental Behavioral Economics Studies
