In this paper, we develop a novel model for voting and seek to explain the effects of partisan allegiances on legislative outcomes. We endogenously show a "fracturing" effect in majority parties and justify the removal of Kevin McCarthy as House Speaker by a "coalition" of Democrats and radical Republicans.
Abstract: We develop a model of collective choice that introduces utility to individual voting and study polarized, partisan legislatures. Party allegiances bring about lower social welfare via inefficient policy selection. Bonding across intra-party factions results in multiple voting equilibria and prevents the formation of optimal inter-party coalitions. However, if the legislature is sufficiently polarized and representatives patient, the majority party fractures with significant implications for future outcomes. Intra-party disputes within the majority also lead to a fracturing equilibrium. As a case study, we examine the ousting of Speaker McCarthy and explain why the minority party voted with the polar majority bloc.
In this paper, I apply ChatGPT as a tool for analyzing patents and innovations and propose a related measure to the 2017 Kogan, Papanikoloau, Seru, and Stoffman (QJE) economic valuation of patents; among other applications.
Abstract: This paper takes a novel LLM approach to patent analysis with generative AI technology. I develop deep learning predictive models that incorporate OpenAI’s ChatGPT textual embedding features to access intricate information about the quality of each invention. These models achieve an R-squared score of 42% predicting patent value, 23% for patent citations, and clearly isolate the worst and best applications. The models enable a complementary measure to the contemporary Kogan, Papanikolaou, Seru, and Stoffman (2017) valuation of patents. Scaling the KPSS value by the AI quality measure leads to a novel valuation with a median deviation of 1.5 times, accounting for potential institutional anticipation. The AI-adjusted valuation generates incremental value for several economic applications, especially cross-sectionally. Furthermore, the application-based measures provide previously inaccessible latent information regarding corporate innovative productivity as well as an opportunity to enhance startup and small-firm corporate policy vis-à-vis patenting.
In this paper, I propose and prove previously unknown criteria for Euclidean triangle similarity with potential applications for a range of problems.
Abstract: We propose and prove two new criteria for triangle similarity. We name these, in conjunction, the SS-AA similarity criteria. These criteria are unique in that they utilize ratios between angles as conditions, which are rarely used, and similarity is not immediately apparent. Generally, if the ratios of a corresponding pair of sides are equal and the ratios of a corresponding pair of angles are equal, then the two triangles are similar. The sole exception is explained in the body of the article.