Network Science Applications in Industrial Organization

Khademorezaian, Kasra

Network Science Applications in Industrial Organization

dc.contributor.advisor	Kosmopoulou, Georgia
dc.contributor.author	Khademorezaian, Kasra
dc.contributor.committeeMember	Kim, Myongjin
dc.contributor.committeeMember	Liu, Qihong
dc.contributor.committeeMember	Radhakrishnan, Sridhar
dc.date.accessioned	2024-07-22T13:14:30Z
dc.date.available	2024-07-22T13:14:30Z
dc.date.issued	2024-08-01
dc.date.manuscript	2024
dc.description.abstract	In recent years, CryptoArt prices have surged to levels typically associated with renowned artists' works, a phenomenon both puzzling and understudied. The first chapter investigates how trading networks and platform connections influence prices in the digital art marketplace hosted on the Foundation platform. We find that larger seller trading networks lead to higher auction prices for lower priced digital art, while platform connections play a critical role, especially for the most expensive pieces. Our research underscores that in decentralized online markets, the power to set prices rests with trading networks and platform connections, with differential impact across the distribution of prices. In the second chapter, we examine the dynamics of repeated NFT auctions on the Foundation platform, focusing on how buyer and seller reputation and experience—measured by the number of followers and Katz centrality—impact resale prices. Our findings reveal that the popularity of sellers in primary sales who is also the creator of digital art positively influences both resale prices and the likelihood of resale. The trading experience of buyers raises relative prices but negatively affects the likelihood of trade in matched samples. Extensive trading networks among buyers show varied effects on resale dynamics, demonstrating cautious consideration of opportunities to buy but aggressive bidding behavior when they do. This study provides valuable insights into the evolving digital art market, highlighting factors that drive resale likelihood and prices, and contributing to a deeper understanding of digital asset markets and their parallels with the traditional art market. In the third chapter, We investigate the route network decisions made by airline carriers in the United States, considering both direct and indirect flights and the interdependencies of the markets. Our modeling approach allows us to analyze airlines' decisions regarding their entire route networks, rather than focusing solely on individual market choices. We estimate the likelihood of selecting a route network based on the past network, competitors' networks, and route characteristics. We find that routes facilitating greater opportunities for indirect flights are more likely to be served by airlines, and competition increases the likelihood of serving a route.	en_US
dc.identifier.uri	https://hdl.handle.net/11244/340485
dc.language	en_US	en_US
dc.subject	Networks	en_US
dc.subject	NFT	en_US
dc.subject	Airlines	en_US
dc.thesis.degree	Ph.D.	en_US
dc.title	Network Science Applications in Industrial Organization	en_US
ou.group	Dodge Family College of Arts and Sciences::Department of Economics	en_US