Abstract
In the modern economy, consumption and other important economic choices are increasingly shaped by algorithms powered by artificial intelligence (AI). Despite their importance, relatively little is known about the economic effects of these algorithms, their normative desirability and any consequent economic policy prescriptions. This research proposal is based on two interlinked project components both studying the economic consequences of deploying popular classes of Al-powered algorithms. The first component focuses on Recommender Systems (RS). These are algorithms that estimate user preferences over a given set of items and use these estimates to match users to items. Amazon, Netflix, Youtube, Spotify and social media apps are popular commercial applications of this technology allowing to filter items (products, media, user posts) to produce personalized experiences. Another prominent application is the advertising business. The main goal of the first component is to conduct an experimental analysis of RS in the spirit of Calvano, Calzolari, Decicolò ad Pastorello (2020, American Economic Review). We plan to build simplified but reasonably realistic RSs and analyze the way their recommendations evolve in different economic settings. We also plan to analyze how firms' and consumers' behavior changes in the presence of RSs, studying in particular whether recommendations are biased and in what direction, whether the presence of RS intensifles or attenuates competition, and how it changes the competitive landscape, affecting the relative position of incumbents and entrants. The second component focuses on two classes of algorithms (Q-learners and Neural Networks) and on their deployment to assist bidders in the online advertising auctions where internet advertising is traded. Online advertising is a major financial engine for most digital platforms. Within this sector, we will focus on the search auctions used to sell ads on search engines, a market that accounts for approximately half of the total revenues of online advertising (i.e., $120 billion in 2020 in the US). The second component of the study will thus provide the first evidence on the use of AI algorithms in the multi-items auctions where digital ad is traded (according to both VCG and GSP auction mechanisms). It will use the same experimental approach described above for component 1, but in an auction setting. We will account for both individual and joint bidding, as shown to be crucial in Decarolis and Roviatti (202 1, American Economic Review). Compared to previous studies, a major feature of our work will also be to address the question of information availability by exploring how different algorithms (Q-learners vs Neural Networks) respond to the availability of more granular information and how the selling platform can distort the information it passes to the advertisers' algorithms in order to increase its revenues.
Project details
Unibo Team Leader: Vincenzo Denicolò
Unibo involved Department/s:
Dipartimento di Scienze Economiche
Coordinator:
Università Commerciale Luigi Bocconi MILANO(Italy)
Total Unibo Contribution: Euro (EUR) 67.473,00
Project Duration in months: 24
Start Date:
28/09/2023
End Date:
28/02/2026
