Research

Abstract

We develop an endogenous growth model that formalizes the distinct roles of artificial intelligence (AI) and the brain in technological innovation. Innovation proceeds in two stages: AI first refines existing knowledge to form a refined knowledge base; the brain then uses this base to generate new ideas. The brain is subject to two opposing forces: knowledge spillovers that foster innovation and knowledge burden that hinders it. In a baseline model with exogenous AI progress, faster AI growth increases the per-capita output growth rate but may raise or lower the fraction of labor allocated to R&D. AI progress makes innovation easier (harder) when the relative degree of knowledge spillovers to knowledge burden is sufficiently high (low). The core mechanism underlying these results is a trade-off: faster AI growth alleviates the brain’s knowledge burden yet weakens knowledge spillovers through selective knowledge discarding. Consequently, the equilibrium stock of refined knowledge may fall short of the level required for optimal brain performance, leading to a waste of social intelligence. An extension with endogenous AI innovation further elucidates the complementary roles of AI and the brain in upstream and downstream innovation and their broader economic implications. 

Abstract

Bernheim and Whinston (1990) famously show that contact across multiple markets may facilitate collusion given that firms or markets are not identical. As pricing decisions are increasingly made by algorithms, antitrust authorities are concerned that algorithms may autonomously collude on supracompetitive prices. In this paper, we test how multimarket contact affects the behavior of pricing facilitated by reinforcement learning algorithms via Q-learning. 

Abstract

A notable phenomenon in the digital economy is the emergence of digital villages: e-commerce platforms support and subsidize suppliers in less developed rural areas to manufacture and sell products online. We argue that, despite platforms' philanthropic claims, these actions are strategically designed to enhance profitability. By providing early subsidies to young sellers, platforms incentivize entry and reduce learning costs, later recouping these investments as sellers gain experience and increase sales. Using a dynamic model of two-sided markets, we analyze the intertemporal and cross-side pricing strategies of a platform with market power. Our findings indicate that sellers' network externalities and learning-by-doing effects reinforce each other, motivating the platform to subsidize them. This study bridges two typically distinct areas of the economy: global online platforms and less developed rural regions. 

Abstract

Many online retail platforms, such as Amazon and JD.com, have recently begun providing sellers with proprietary consumer data. In this paper, we investigate how the sharing of such data can incentivize seller collusion through personalized pricing. We find that the effect of data sharing on collusion sustainability and profitability depends on the mode operated by the platform. When a platform acts as both a host and seller, sharing data leads to more collusive outcomes. However, when a platform only intermediates purchases, sharing data hinders collusion. Our results suggest that imposing a ban on data sharing may be ineffective and harmful to consumer welfare. 

Abstract

Platforms often enforce pricing policies onto sellers such as the minimum margin agreement (MMA). MMAs require that platforms receive a guaranteed profit-margin on seller goods. If the margin is not met then any difference between the actual margin is taken from the seller. I develop a model where a seller sells through a direct channel and serves as a supplier for a separate platform. Under MMAs, a platform can threaten the seller to flood the market demand by pricing low and capturing a guaranteed profit. I show that MMAs potentially lead to an increase in platform facilitated purchases and inflate both direct and intermediated prices. Furthermore, I find that MMAs may be more prevalent when the seller faces competition and platforms can steer buyers towards specific goods.