6 research outputs found
Price discrimination with inequity-averse consumers : a reinforcement learning approach
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
Consumer prices : effects of learning algorithms and pandemic-related policy measures
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.Beim Thema Verbraucherpreise haben in den letzten Jahren vor allem zwei groĂe Themen die öffentliche Debatte dominiert: Zum einen die Preisgestaltung mit Hilfe kĂŒnstlicher Intelligenz und zum anderen das hohe Preisniveau, welches mit dem Ausbruch der COVID-19-Pandemie stĂ€rker als ĂŒblich angestiegen ist. Höhere Preise fĂŒhren zu einem Verlust an Konsumentenrente und möglicherweise auch an Gesamtwohlfahrt, weshalb dieses Thema in der politischen Diskussion allgegenwĂ€rtig wurde. Die Dissertation leistet einen Beitrag zu dieser Debatte, indem sie die vorhandene Literatur zu algorithmischer Preisbildung erweitert, von der angenommen wird, dass sie eine personalisierte Preisbildung sowie kollusives Verhalten begĂŒnstigt, und indem sie das allgemeine VerstĂ€ndnis dafĂŒr verbessert, wie die wĂ€hrend der COVID-19-Pandemie durchgesetzten staatlichen MaĂnahmen zur (kurzfristigen) Preisentwicklung beigetragen haben. Der erste Teil der Arbeit befasst sich mit den BefĂŒrchtungen, dass es zu stillschweigenden Absprachen kommen könnte, wenn Unternehmen lernende Algorithmen einsetzen, da mehrere Simulationsstudien gezeigt haben, dass Algorithmen, die sogenanntes Reinforcement Learning einsetzen, in der Lage sind, ihr Preisverhalten zu koordinieren und infolgedessen ein kollusives Ergebnis zu erzielen, ohne dafĂŒr programmiert worden zu sein. Wir erörtern verschiedene konzeptionelle Herausforderungen sowie HĂŒrden bei der realen Anwendung von Algorithmen und zeigen anhand eigener Simulationen, dass die resultierenden Marktpreise stark von der Art des Algorithmus oder der Heuristik abhĂ€ngen, die von den Unternehmen zur Preisbildung verwendet wird. Im anschlieĂenden Teil der Arbeit wird untersucht, wie sich ein selbstlernender Preisalgorithmus gegenĂŒber ungleichheitsaversen Konsumenten verhĂ€lt. Aus unseren Simulationen können wir schlieĂen, dass das Fairnessempfinden der Verbraucher, das die Unternehmen in den vergangenen Jahren von Preisdiskriminierung abgehalten hat, mit Hilfe von lernenden Algorithmen in die Preisentscheidungen der Unternehmen einflieĂen kann, sodass differenzierte Preisstrategien wahrscheinlicher werden. Die Diskussion ĂŒber das ĂŒberdurchschnittliche Preisniveau in vielen LĂ€ndern wĂ€hrend der COVID-19-Pandemie wird im dritten Teil der Dissertation vertieft. Es wird empirisch untersucht, inwieweit die Auswirkungen staatlich verordneter BeschrĂ€nkungen und - als Folge ihrer Durchsetzung die eingeschrĂ€nkte MobilitĂ€t die Verbraucherpreise wĂ€hrend der COVID-19-Pandemie beeinflusst haben. Es wird gezeigt, dass die Strenge der staatlichen MaĂnahmen einen positiven und signifikanten Einfluss auf die Verbraucherpreise vor allem im Lebensmittelsektor hatten, was bedeutet, dass strengere MaĂnahmen zu höheren Verbraucherpreisen in diesen Kategorien gefĂŒhrt haben
Strategic choice of price-setting algorithms
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
Tumour stage distribution and survival of malignant melanoma in Germany 2002-2011
Background
Over the past two decades, there has been a rising trend in malignant melanoma incidence worldwide. In 2008, Germany introduced a nationwide skin cancer screening program starting at age 35. The aims of this study were to analyse the distribution of malignant melanoma tumour stages over time, as well as demographic and regional differences in stage distribution and survival of melanoma patients.
Methods
Pooled data from 61 895 malignant melanoma patients diagnosed between 2002 and 2011 and documented in 28 German population-based and hospital-based clinical cancer registries were analysed using descriptive methods, joinpoint regression, logistic regression and relative survival.
Results
The number of annually documented cases increased by 53.2% between 2002 (Nâ=â4 779) and 2011 (Nâ=â7 320). There was a statistically significant continuous positive trend in the proportion of stage UICC I cases diagnosed between 2002 and 2011, compared to a negative trend for stage UICC II. No trends were found for stages UICC III and IV respectively. Age (OR 0.97, 95% CI 0.97â0.97), sex (OR 1.18, 95% CI 1.11â1.25), date of diagnosis (OR 1.05, 95% CI 1.04â1.06), âdiagnosis during screeningâ (OR 3.24, 95% CI 2.50â4.19) and place of residence (OR 1.23, 95% CI 1.16â1.30) had a statistically significant influence on the tumour stage at diagnosis. The overall 5-year relative survival for invasive cases was 83.4% (95% CI 82.8â83.9%).
Conclusions
No distinct changes in the distribution of malignant melanoma tumour stages among those aged 35 and older were seen that could be directly attributed to the introduction of skin cancer screening in 2008.
Additional file 3: Table S2. of Tumour stage distribution and survival of malignant melanoma in Germany 2002â2011
Malignant melanoma patients aged 35Â years and above by age at diagnosis, sex, UICC stage, year of diagnosis, place of residence and âdiagnosis during screeningâ, Nâ=â34 739 (UICC 0 and X excluded) (DOCX 40 kb
Additional file 4: Table S3. of Tumour stage distribution and survival of malignant melanoma in Germany 2002â2011
Relative 5-year survival of malignant melanoma patients diagnosed between 2002 and 2011, overall (UICC 0-IV, X) (Nâ=â60 672) and for patients with invasive tumours (UICC I â IV, X) stratified by age, sex, UICC stage, âdiagnosis during screeningâ and place of residence (Nâ=â49 351) (DOCX 39 kb