In oligopolistic market settings, multinational firms’ foreign market entries are considered competitive moves against a rival to maintain competitive parity. This paper introduces a simulation model to investigate the industry and firm-level factors determining the profit-optimizing degree of oligopolistic reaction. We simulate a world with two competing firms and use deep reinforcement learning to train these firms to find a profit-optimizing internationalization strategy for different industry- and firm-level parameters. Running 10,000 simulation scenarios with varying parameters, we find, on average, a considerably high degree of collocation between the two firms. Interestingly, while mid-to-high levels of collocation are driven by low competitive intensity, entry barriers, liabilities of foreignness, firm endowments, and high market size variation, edge cases of full collocation emerge under high competitive intensity and low first mover advantages and firm endowments. High entry barriers, liabilities of foreignness, and low market size variation drive the opposite case of full avoidance. By adding a competitive dynamics lens to our experiments and exploring the order and motivation of market entries in our simulation, we can deduce four competitive strategies (market-seeking, pre-emptive vs. defensive, destructive) leading to collocation.
