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 emotional than their print counterparts. Using Facebook’s announcement of an algorithmic update as a quasi-experiment, I show that newsrooms have an immediate response to algorithm changes.A structural model shows that readers value both like-minded and unexpectedly slanted posts, but platforms overweight these forces. Media outlets respond by adjusting content to maximize platform-driven viewership, accounting for about 80% of the extra slant in user exposure. Counterfactuals indicate that roughly 90% of this extra slant can be eliminated through an algorithm redesign, substantially flattening exposure across users. These findings highlight the central role of platform in shaping both the supply and distribution of news, and a trade-off between engagement and exposure diversity in platform design.

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.