Research

Shyam Bhagat '26

Algorithms of Empathy: A Comparative Analysis of Human and GenAI Peer Support 

As Generative AI systems become increasingly integrated into mental health
discourse, an important question is raised: Can AI empathy effectively replace
human peer support? In this study, I evaluated 488 posts from the r/ADHD
subreddit, using a paired-sample design to compare top-performing human
responses against those generated by the LLM GPT-4o, by OpenAI. Using a
four-dimensional empathy rubric (Nuanced Empathy, Context Sensitivity,
Relatability, and Adaptive Warmth), my research finds that while AI models
demonstrate superior consistency and safety, human peers retain a statistically
significant advantage in "Relatability" due to shared lived experience.
Additionally, a secondary geopolitical comparison between the U.S.-based
GPT-4o, and China-based DeepSeek reveals distinct alignment philosophies:
the former optimizing for emotional warmth and the latter for factual caution.
My findings suggest a complementary model where AI provides structured,
consistent baseline support, while humans provide the authentic connection
necessary for deep psychological validation.

Laurel Hamilton '26

Strategic Impacts of the NFL’s International Expansion: Driving Global Fan Development & Mitigating Domestic Cannibalization

Since 2007, the National Football League (NFL) has staged 55 regular-season games abroad as a part of its long-term growth strategy to develop international audiences. This study explores whether exporting these games effectively expands the NFL’s global audience without cannibalizing domestic interest. U.S. broadcast data, data from NFL official Instagram accounts, and Google search trends are used to estimate short-run effects. Findings indicate that while domestic engagement drops during weeks with international games, international engagement grows proportionally more, suggesting that the NFL may be able to incur temporary domestic losses in favor of building a larger global fanbase over time. However, a simulation model of seasonal engagement shows that the optimal scheduling of international games depends on the League’s priority markets, demonstrating that the regular-season schedule itself is critical in protecting the NFL’s position as a U.S. market leader while achieving long-term growth globally.

Justin Lee '26

Quantifying Tilt: Analyzing Deviations from Optimal Strategy in Chess

In competitive chess, maintaining consistent, high-level performance over extended play sessions presents a significant cognitive challenge. This study investigates the impact of sustained engagement on decision-making accuracy in online bullet chess. Leveraging a dataset of over 92 million games from the Lichess platform in August 2025, we quantify deviations from optimal moves using Average Centipawn Loss (ACPL), calculated via Stockfish at a moderate search depth. We define consecutive game sessions based on short intervals between games to capture potential fatigue and tilt effects of repeated play. Employing gradient boosting models with CatBoost and interpretability techniques such as SHAP values, we analyze how factors including player rating differentials, time since last win, and opening move category influence performance. Our findings show declines in performance associated with longer sequences of consecutive games and extended losing streaks, highlighting the influence of mental fatigue and emotional carryover on cognitive control under pressure. Additionally, analysis of different opening categories illustrates variability in decision consistency based on opening strategy. Overall, this work highlights the complexity of psychological and tactical determinants regarding chess performance and offers a framework for understanding fatigue-driven errors in cognitively demanding activities.

Ari Nathanson '26

Rent Stabilization in Practice: Landlord Investment Responses in New York City

This paper examines how rent stabilization affects building-level investment in New York City. Using 2024 permit and renovation cost data merged with building characteristics, I compare buildings that contain at least one rentstabilized unit to non-stabilized properties. Across multiple specifications with neighborhood, land use, owner type, unit count, and vintage controls, I estimate the relationship between stabilization status and four outcomes: total permits filed, total estimated renovation cost, the likelihood of filing any permits, and the natural logarithm of total estimated cost. Because some stabilized buildings receive property tax benefits through programs such as 421-a and J-51, an additional control variable indicates whether a building contains at least one tax-exempt stabilized unit. I find that stabilized buildings are associated with significantly lower investment than comparable nonstabilized buildings. Stabilized properties file approximately 0.55 fewer permits per year, spend roughly $240,000 less in total renovation costs, are 6% less likely to undertake any renovation activity, and spend 55% less on renovations. These results are robust to alternative controls, sample restrictions, and fixed-effect specifications. The findings raise questions about long-run housing quality and the investment incentives created by rent regulation.

Precious Esielem '26

Insurance Without Access: The Rural Friction Gap in Medicaid Expansion’s Impact on Medical Debt

Medical debt is a significant barrier to financial stability in the United States, affecting nearly 100 million people and falling most heavily on rural communities. While the Affordable Care Act’s Medicaid expansion was intended to alleviate this burden, the geographic equity of its impact reains under-examined. This paper investigates whether the benefits of expansion are attenuated by the structural limitations of rural healthcare systems. Using a two-way fixed effects difference-in-differences design on a twelve-year county-level panel from 2012 to 2023, I document a statistically significant rural friction gap. Rural expansion counties experienced a total increase in medical debt share of approximately 0.95 perentage points following implementation, while urban counties experienced no statistically significant change. This pattern suggests that expansion failed to deliver measurable financial protection in either setting, with rural counties bearing a disproportionate net increase in debt. This gap is partially explained by differential state-level economic trajectories. When state-specific linear time trends are included, the rural coefficient attenuates, indicating that the result is best interpreted as an upper bound on the true rural friction gap. Event-study analysis confirms that this gap is a widening trend that compounds in the years following implementation. Furthermore, a heterogeneity analysis suggests the gap persists regardless of local hospital closures, implying the friction is driven by chronic, systemic undercapacity such as provider shortages and geographic isolation. These findings suggest that insurance expansion without a corresponding investment in rural healthcare infrastructure is insufficient to provide equitable financial protection.
 

Eric Sankey '26

Market Volatility as a Prior Belief: How Entry Conditions Shape Persistence in Trading

This study examines whether market volatility at the time of an initial investment in cryptocurrency shapes the internal decision rules governing subsequent trading behavior. Here, entry volatility is defined as the standard deviation of logged daily returns in the 30 days preceding a wallet’s first purchase. Over the following 90 days, it was tested whether this volatility in the certainty of returns affected risk perception. A Bayesian exit model was used to simulate traders’ latent beliefs about being in a “dangerous” market state and map that belief to exit probability. Two wallet level parameters were estimated: an exit reluctance parameter (β0), capturing baseline reluctance to sell, and a danger sensitivity parameter (β1), capturing responsiveness to inferred market risk. The latent belief trajectory is computed via Bayesian updating from observed return sequences. After validating the model, it was fit to real wallet data. After analysis, it was determined that the decision rule did not differ between the high and low volatility groups, yet their baseline risk tolerance was significantly different and persisted through state changes. While certain selection biases present confounds, the correlation presents an interesting insight: that prior beliefs may condition an actor’s actions and have a persistent effect over time.