Strategic decision-making under ambiguity: insights from exploring a simple linked two-game model

Strategic decision-making is one of the most important functions of the manager. These decision problems, however, are made much more challenging when they are plagued by ambiguity and interdependencies with rivals. In fact, irreducible ambiguity makes traditional optimization techniques inapplicable, leaving a manager to struggle to identify an approach to use to generate higher payoffs. While a literature on addressing ambiguity exists in the behavioral realm and when it is converted to the equivalent of risk under subjective beliefs, a gap remains when ambiguity is left as unknowable. We address that gap here by considering these problems in a game-theoretical structure in order to address three related and relevant research questions: When does ambiguity matter in such problems? How much does that ambiguity matter (i.e., how costly is it)? What kind of approach or heuristic might help improve payoffs in such problems? We use computational simulation to provide the answers. We discuss the implications.