publ-mit-podpubl-mit-podSchwalbe, UlrichMuijs, MatthiasGrüb, JensBuchali, Katrin2024-04-082024-04-082023-02-062023https://hohpublica.uni-hohenheim.de/handle/123456789/6807Recent experimental simulations have shown that autonomous pricing algorithms are able to learn collusive behavior and thus charge supra-competitive prices without being explicitly programmed to do so. These simulations assume, however, that both firms employ the identical price-setting algorithm based on Q-learning. Thus, the question arises whether the underlying assumption that both firms employ a Q-learning algorithm can be supported as an equilibrium in a game where firms can chose between different pricing rules. Our simulations show that when both firms use a learning algorithm, the outcome is not an equilibrium when alternative price setting rules are available. In fact, simpler price setting rules as for example meeting competition clauses yield higher payoffs compared to Q-learning algorithms.engPricing algorithmAlgorithmic collusionReinforcement learning330AlgorithmusPreisbildungWorkingPaper1833308824urn:nbn:de:bsz:100-opus-21276