Browsing by Subject "Reinforcement learning"

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Price discrimination with inequity-averse consumers : a reinforcement learning approach
(2021) Buchali, Katrin
With the advent of big data, unique opportunities arise for data collection and analysis and thus for personalized pricing. We simulate a self-learning algorithm setting personalized prices based on additional information about consumer sensi- tivities in order to analyze market outcomes for consumers who have a preference for fair, equitable outcomes. For this purpose, we compare a situation that does not consider fairness to a situation in which we allow for inequity-averse consumers. We show that the algorithm learns to charge different, revenue-maximizing prices and simultaneously increase fairness in terms of a more homogeneous distribution of prices.
Strategic choice of price-setting algorithms
(2023) Schwalbe, Ulrich; Muijs, Matthias; Grüb, Jens; Buchali, Katrin
Recent 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.