AI@CBS

This paper theoretically and empirically investigates the effects of liquidity regulation on the banking system. We document that the current quantity-based liquidity rule has reduced banks’ liquidity risks. However, the mandated liquidity buffer appears to crowd out bank lending and lead to a migration of liquidity risks to banks that are not subject to liquidity regulation. These findings motivate a model of liquidity regulation with endogenous liquidity premiums and heterogeneous banks.

The contemporary art world is conservatively estimated to be a $65 billion USD market that employs millions of human artists, sellers, and collectors globally. Recent attention paid to AI-made art in prestigious galleries, museums, and popular media has provoked debate around how these statistics will change. Unanswered questions fuel growing anxieties. Are AI-made and human-made art evaluated in the same ways? How will growing exposure to AI-made art impact evaluations of human creativity? Our research uses a psychological lens to explore these questions in the realm of visual art.

This case tells the story of Microsoft’s 2018 acquisition of GitHub and the subsequent launch of GitHub Copilot, a tool that uses generative artificial intelligence to suggest snippets of code to software developers in real time. Set in late 2021, when Copilot was still in beta, the case asks how Microsoft and GitHub should roll out Copilot to the public.

The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing applications. For example, consumers compare and review products online, individuals interact with their voice assistants to search, shop, and express their needs, investors seek to extract signals from firms’ press releases to improve their investment decisions, and firms analyze sales call transcripts to increase customer satisfaction and conversions.

We consider experimentation in settings where, due to interference or other concerns, experimental units are coarse. ‘Region-split’ experiments on online platforms, where an intervention is applied to a single region over some experimental horizon, are one example of such a setting. Synthetic control is the state-of-the-art approach to inference in such experiments. The cost of these experiments is high since the opportunity cost of a sub-optimal intervention is borne by an entire region over the length of the experiment.