Browsing by Person "Buchali, Katrin"

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    Consumer prices : effects of learning algorithms and pandemic-related policy measures
    (2023) Buchali, Katrin; Schwalbe, Ulrich
    When it comes to product prices, two major topics have dominated the public debate in recent years: One is pricing with the help of artificial intelligence, and the other is the price level, which has risen more than usual with the onset of the COVID-19 pandemic. Higher prices create a loss of consumer surplus and possibly total welfare, which is the reason this topic has become ubiquitous in political discussions. This dissertation contributes to the debate by extending the existing literature on algorithmic pricing, which is said to facilitate personalized pricing, as well as collusive behavior and to enhance the general understanding of how government measures enforced during the COVID-19 pandemic contributed to (short-time) price developments. Thereby, the first part of the thesis addresses the concern that tacit collusion might occur if firms employ learning algorithms, as several simulation studies have demonstrated that algorithms using reinforcement learning are able to coordinate their pricing behavior and, as a result, achieve a collusive outcome without having been programmed for it. We discuss several conceptual challenges as well as challenges in the real-world application of algorithms and show by or own simulations that resulting market prices strongly depend on the type of algorithm or heuristic that is used by the firms to set prices. In the subsequent part of the thesis we examine how a self-learning pricing algorithm performs when faced with inequity-averse consumers. From our simulations we can conclude that consumers sense of fairness, which have prevented firms from engaging in price discrimination in the past years, can be incorporated into firms pricing decisions with the help of learning algorithms, making differential pricing strategies more feasible. The discussion surrounding the above-average price levels in many countries during the COVID-19 pandemic is extended in the third part of the thesis. We present empirical evidence for the impact of government-imposed restrictions and, as a consequence of their enforcement, reduced mobility on consumer prices during the COVID-19 pandemic. We show that the stringency of government measures had a positive and significant impact on consumer prices mainly in the food sector, which means that more stringent measures induced higher consumer prices in these categories.
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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.
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    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.