Working Papers

Digital platforms shape not only what news people see but also what outlets choose to produce. This paper quantifies the equilibrium effects of algorithmic curation on news supply and exposure. Linking print and online headlines from major U.S. newspapers, I document that online headlines are more ideologically slanted and more negative than their print counterparts. Using Facebook’s 2025 restoration of political content, I show that outlets substantially increase the extent to which online headlines depart from their print counterparts for stories more exposed to the policy.  A structural model shows that readers value both like-minded and unexpectedly slanted posts, while the platform amplifies these forces when allocating attention. Media outlets respond by increasing slant to compete for platform-driven viewership, with these supply responses accounting for about 80% of the extra slant in user exposure. Counterfactuals show that removing algorithmic amplification eliminates roughly 90% of this supply-induced slant and substantially flattens exposure across users. These findings show that recommendation systems shape not only the distribution of news but also the incentives governing its supply.

This paper studies how an information provider designs disclosure to keep users engaged when users would prefer to learn quickly. The framework applies to expert services, online content, and personalized news environments. We show that optimal disclosure depends on relative patience, belief disagreement, and whether communication can be personalized. When beliefs differ, the principal may cater to the user’s bias by initially revealing information aligned with that bias. Comparing personalized and non-personalized communication, we find a trade-off: non-personalized communication delivers information faster but with lower quality, while personalization slows disclosure but can improve learning. The paper provides a theory of engagement, personalization, and information quality.

Automated market makers are digital trading platforms that replace order books with algorithmic pricing rules. This paper studies how these rules shape price discovery when liquidity providers respond strategically to trades. We develop a dynamic model of liquidity provision under adverse selection and test its predictions using 19.2 million transactions from 31 Uniswap v2 pools. We show that liquidity providers are not purely passive: many actively adjust prices through swaps, and their trades tend to offset prior liquidity-taker trades, especially when those trades appear less informed. The results highlight how automated platform design shapes strategic behavior and market outcomes.