Deep reinforcement learning shows how firms optimize market selection under competition
What is the strategic optimum when competing with a smart competitor? This study investigates how firms should choose markets when they are in competition with an equally resourceful competitor. The paper builds a “chess engine” of global competition and explores strategies that emerge from the simulation. It turns out that existing work on where companies avoid each other and where they go for heads-on competition are extreme cases of a more general solution to the competition “game”. Heads-on competition is particularly high for companies from smaller home markets, and when market entry barriers are low.
Gwosdilin R., Lindner T., Wolfesberger M. 2026. Simulating competitive collocation strategies with deep reinforcement learning. Journal of International Business Studies.
