For the classical backward induction algorithm, the input is an arbitrary $n$-person positional game with perfect information modeled by a finite acyclic directed graph (digraph) and the output is a profile $(x_1, \ldots, x_n)$ of pure positional strategies that form some special subgame perfect Nash equilibrium. We extend this algorithm to work with digraphs that may have directed cycles. Each digraph admits a unique partition into strongly connected components, which will be treated as the outcomes of the game. Such a game will be called a {\em deterministic graphical multistage}(DGMS) game. If we identify the outcomes corresponding to all strongly connected components, except terminal positions, we obtain the so-called {\em deterministic graphical}(DG) games, which are frequent in the literature. The outcomes of a DG game are all terminal positions and one special outcome $c$ that is assigned to all infinite plays. We modify the backward induction procedure to adapt it for the DGMS games. However, by doing so, we lose two important properties: the modified algorithm always outputs a {\em Nash equilibrium} (NE) only when $n = 2$ and, even in this case, this NE may be not {\em subgame perfect}. (Yet, in the zero-sum case it is.) The lack of these two properties is not a fault of the algorithm, just (subgame perfect) Nash equilibria in pure positional strategies may fail to exist in the considered game. {\bf Keywords:} deterministic graphical (multistage) game, game in normal and in positional form, saddle point, Nash equilibrium, Nash-solvability, game form, positional structure, directed graph, digraph, directed cycle, acyclic digraph.

8 pages