Detecting Algorithmic Collusion: Insights from Moment Screening Methods

Yıl 2024, , 1066 - 1084, 27.09.2024

Yalçıner Yalçın , Selcen Öztürk

https://doi.org/10.25295/fsecon.1477143

Öz

The development of global, automated, and dynamic manufacturing processes is having a growing impact on industries. Virtual machines commonly function behind the scenes, supporting a variety of operations. Algorithms are the essential intelligence of these virtual machines, greatly increasing efficiency and effectiveness within marketplaces. Algorithms have the ability to promote competition and increase efficiency, eventually improving market competitiveness. However, algorithmic collusion can be maintained using “dynamic pricing” techniques, which are typically associated with automated pricing. Algorithmic collusion leads to increases in prices and/or decreases in the quality of products and services. The main objective and the function of competition authorities is to fight against those formations. In this regard, cartel screening is an important first step toward detecting collusive activity. In this paper, we used several moment screens to capture the effects of algorithmic pricing. Our findings suggest that algorithmic pricing exhibits non-collusive behavior within the particular industry and time frame examined in our analysis.

Anahtar Kelimeler

Algorithmic Collusion, Behavioral Screens, Cartel Detection, Dynamic Pricing, Machine Learning, Online Marketplace

Teşekkür

This work is derived from Yalçıner Yalçın’s Ph.D thesis

Algoritmik Anlaşmaların Tespiti: Moment Tarama Yönteminden Çıkarımlar

Öz

Küresel, otomatize ve dinamik üretim süreçlerinin gelişimi endüstriler üzerinde giderek daha önemli bir etkiye sahip olmaktadır. Sanal makineler genellikle sahne arkasında işlev görerek çeşitli operasyonları desteklemektedir. Algoritmalar bu sanal makinelerin temel zekâsıdır ve pazar yerlerinde verimliliği ve etkinliği büyük ölçüde artırmaktadır. Algoritmalar rekabeti teşvik etme ve sonunda pazar rekabetini artırma yeteneğine sahiptir. Ancak, genellikle otomatik fiyatlandırmayla ilişkilendirilen "dinamik fiyatlandırma" tekniklerini kullanarak algoritmik anlaşmalar sürdürülebilir. Algoritmik anlaşma ise, fiyatların artmasına ve/veya ürünlerin ve hizmetlerin kalitesinin azalmasına neden olur. Rekabet otoritelerinin ana hedefi ve işlevi, bu oluşumlarla mücadele etmektir. Bu bağlamda, kartel taraması, anlaşma/ittifak faaliyetlerini tespit etme yolunda önemli bir ilk adımdır. Bu çalışmada, algoritmik fiyatlandırmanın etkilerini yakalamak için moment tarama tekniği kullanılmaktadır. Bulgular, analizde incelenen belirli endüstri ve zaman çerçevesinde algoritmik fiyatlandırmanın anlaşma/ittifak dışı davranış sergilediğini öne sürmektedir.

Anahtar Kelimeler

Algoritmik Anlaşma, Davranışsal Tarama, Kartel Tespiti, Dinamik Fiyatlama, Makine Öğrenmesi, Online Pazar

Kaynakça

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