Solving Structured Hierarchical Games Using Differential Backward Induction

Li, Zun; Jia, Feiran; Mate, Aditya; Jabbari, Shahin; Chakraborty, Mithun; Tambe, Milind; Vorobeychik, Yevgeniy

From large-scale organizations to decentralized political systems, hierarchical strategic decision making is commonplace. We introduce a novel class of structured hierarchical games (SHGs) that formally capture such hierarchical strategic interactions. In an SHG, each player is a vertex in a tree, and strategic choices of players are sequenced from root to leaves, with root moving first, followed by its children, then followed by their children, and so on until the leaves. A player's utility in an SHG depends on its own decision, and on the choices of its parent and all the tree leaves. SHGs thus generalize simultaneous-move games, as well as Stackelberg games with many followers. We leverage the structure of both the sequence of player moves as well as payoff dependence to develop a novel gradient-based back propagation-style algorithm, which we call Differential Backward Induction (DBI), for approximating equilibria of SHGs. We then provide a sufficient condition for convergence of DBI. Finally, we demonstrate the efficacy of the proposed algorithmic approach in finding approximate equilibrium solutions to several classes of SHGs.