Agent-Based Model of Competitive Strategy

An agent-based model of competitive strategy represents firms as autonomous, heterogeneous, adaptive agents whose decision rules and local interactions generate emergent industry-level dynamics that no single firm designs. Davis, Eisenhardt, and Bingham's 2007 roadmap for developing theory through simulation places this kind of computational modeling in the sweet spot between inductive case research and formal mathematics, well suited to longitudinal, nonlinear, and interactive strategy phenomena. Instead of solving for an equilibrium, the analyst builds firms with strategies, lets them compete over many simulated periods, and studies the market structures, survival patterns, and performance dispersions that emerge. The method gives strategy researchers a controlled laboratory for theory building about competitive dynamics that are too complex and path-dependent for closed-form analysis.