# Religion, Race, Gender, and the 2024 Election I guess it started back with my first job when I was freshly a freshman in college. I needed a mandatory class due to a schedule change, and I ended up enrolling in the first section that approved my permission-to-enroll request. The class was titled "Mystics, Prophets, and Social Change" and was taught by Prof. Gaston Espinosa. It was during a tumultuous time before the 2024 election, and he needed research assistants for his project, "Religion, Race, Gender, and the 2024 Election." I, who was in dire need of a job at my parents' behest, gladly signed up for the position and got to work earnestly. ## Optimized Indexation One fateful day, I received an email while absently sitting in my abstract algebra class. It was an offer to work at the Financial Economics Institute as a research assistant for Prof. Benjamin Gillen. I excitedly accepted it, and upon our meeting, he posited two project options. Being rather unable to understand either, I selected the one titled "Optimized Indexation," which covered building a Python pipeline to optimize securities. I was new to the idea of portfolio optimization and found how detached it was from the companies' content to be quite interesting. Portfolio optimization relies only on the price, the concrete numeric signals. I figured it could be fully detached from the prospect of securities and applied to a variety of other numeric objects. I channeled the ideas from my Abstract Algebra class and figured we could strip out all the fluff from something like voter groups and break it down into a mathematical object that could be optimized. ## Electoral Equilibrium Thus, combining my two previous research projects yielded Electoral Equilibrium. Inspired by the development plan in Optimized Indexation, I booted up Claude, described my theorized methodology, threw in my spontaneous ideas, and developed the initial design document. Over time, that document grew to roughly 72 pages as I ideated new data sources and development methods. Election polls would only get us a fraction of the way; we needed an array of data sources to train the LLM to respond to the multifacets of potential prompts with some degree of accuracy. Social media shot up as a prime target. Still, we also needed more legitimate sources, such as news outlets — especially since each outlet targets a specific audience and occupies a distinct position on the political spectrum. In my Stochastic Operations Research class, we learned that prediction markets document people's true beliefs about the prospect of an event, making them the only signal with real money behind it. A tweet costs nothing to post; a Polymarket position does. That said, prediction markets turned out to be more nuanced than I initially thought. They don't measure what traders believe politically; they measure what traders believe will happen. That's a second-order signal, not a first-order one. You can't use a change in win probability to supervise a model that's trying to estimate a change in vote-share margin; the math doesn't work. So, prediction markets became a calibration benchmark rather than a training input. Once a shock resolves, we compare the model's predictions with market prices, and systematic divergence tells us where to recalibrate. The social media strategy has evolved considerably. The obvious choice was Twitter; API access was far too pricy to justify. Thankfully, millions of tweets from the 2020 election cycle, the MeToo movement, and other major political events sit freely available in public datasets. The paid API would have given me a trickle; the archives gave me a flood. For live collection, I turned to Bluesky, which runs an open firehose at no cost, and Apify, which scrapes X's public posts without requiring any subscription. Telegram and TikTok rounded out the picture, with the former serving as a nesting ground for the far right due to its lax content moderation. At the same time, the latter skews heavily toward Gen Z, Latino, and Asian American voters whose voices were underrepresented elsewhere. Two platforms I underestimated early: Reddit and Discord. Reddit's subreddit structure turns out to be a better demographic proxy than most platform-level proxies. Subreddits like r/Catholicism or r/BlackPeopleTwitter are self-selecting around a specific identity in a way that a platform-wide demographic average isn't. Discord was a later addition, prompted by a dataset called Discord-Unveiled-Compressed: 2.05 billion messages from 3,167 public servers spanning 2015 to 2024, collected via Discord's public API. Discord skews young and male in a way that fills a gap the other platforms don't cover well. The AI initially assumed Bluesky would give me a good signal from secular and younger voters. I had to clarify that it's more accurately described as the far-left mirror image of Truth Social, populated by people who migrated off Twitter after the Musk acquisition: a useful signal, but a narrow one. The demographic inference problem was its own puzzle. Rather than treating everyone on a given platform as a single block, I planned a three-stage bio classifier: keyword matching first, then a SetFit model trained on ideologically sorted user bios from a political-polarization corpus, and finally, language as a last resort. A user whose bio reads "Proud Latina Catholic" gets weight in both the Latino and Catholic blocs simultaneously. The classifier will run on a Raspberry Pi 5 with a dedicated NPU chip, which handles the embedding pass at sub-millisecond latency while the logistic head runs on the Pi's CPU. Then another problem arrived: we have too much data. Even with an HPC, there was no way to process those millions of data points in a feasible amount of time. So, once again echoing my OR class's teachings, I decided to sample up to 200,000 points from each source. It would be stratified by the time window around a shock event, by inferred demographic bloc (so low-volume groups like Jewish and Muslim voters wouldn't be drowned out by secular and women voters), and by sentiment extremity, since the most polarized posts are the most informative for fine-tuning. The sampled dataset will then pass through an LLM cleaning stage that flags off-topic posts, removes spam, normalizes political abbreviations, and deduplicates across platforms. But that wasn't it. Model drift was still a critical issue. As time goes on, the data will need to evolve. Thus, I decided to implement a continuous-update architecture: a nightly pipeline that ingests new social media posts, cleans them using an LLM, scores them with RoBERTa, and appends them to the training dataset. When enough new shock events accumulate, a fine-tuning run is automatically triggered on the HPC. The prediction markets provide a feedback loop on top of that — every inference call is logged. Once a shock resolves, the model's win-probability estimate can be compared against what the market actually did. Systematic divergence shows up in the audit log and informs how much weight to give each source type the next time around. The model doesn't just reflect the world at the time it was trained; it grows with it and gets corrected when it's wrong. ## The Demographic Architecture Problem The hardest design problem wasn't the LLM or the optimizer. It was the demographic architecture. The initial design tried to nest the demographics: race at the top, religion modifying race, and gender modifying the race-religion intersection. The logic was sound: a Catholic Latina is a specific person with a specific political trajectory, not just the average of three separate groups. But the nested structure required cross-tabulations at the race × religion × gender level, and those cross-tabulations produce extremely sparse cells. Asian Muslims represent about 0.1% of the electorate. There is almost no survey data for that cell in most election cycles, which means you're imputing, and imputed values contaminate the covariance matrix in the Monte Carlo. The answer was a parallel three-stratum architecture. Instead of nesting, the three demographic dimensions each independently cover approximately 100% of the electorate: - **Race/Ethnicity**: Black/African American, Latino, Asian American, White, Other - **Religion**: Evangelical, Catholic, Mainline Protestant, Secular, Jewish, Muslim, Other - **Gender**: Women, Men, Other No cross-tabulations needed. Every voter belongs to exactly one cell in each stratum. Effective loyalty becomes a weighted average across the three:

\mu^{\text{eff}} = \lambda_1 \sum_i w_i \mu_i^{\text{race}} + \lambda_2 \sum_R v_R \mu_R^{\text{rel}} + \lambda_3 \sum_G g_G \mu_G^{\text{gen}}

The λ weights are calibrated from historical election data rather than assumed. The optimizer only rebalances the race weights — religion and gender are fixed from the voter panel, reflecting the actual electorate composition rather than being treated as strategic levers. This architecture is an acknowledged approximation. A Latino Evangelical's political behavior isn't perfectly reconstructed by independently summing the Latino marginal, the Evangelical marginal, and the male marginal; that intersection has dynamics the additive model flattens. The theoretically correct alternative is Multilevel Regression with Poststratification (MRP), which models joint distributions with hierarchical Bayesian shrinkage. MRP isn't implemented here because it requires full joint cross-tabulation data and individual-level survey responses that aren't available in a 7-week timeline. What is implemented is raking — iterative proportional fitting across all three marginal tables until they converge — which enforces joint consistency without requiring the full joint distribution. The paper will compare the additive model and raked outputs; a large divergence between them is itself a finding. ## The Optimizer The optimizer's objective evolved, too. The first instinct was to borrow the Markowitz formulation directly: minimize coalition variance subject to hitting the win threshold. That's wrong for a campaign operating from a structural deficit. If your baseline loyalty is below the win threshold, minimizing variance guarantees a loss: you're fleeing from the only mechanism (high-variance demographic bets) that could push you over the line. The correct objective is to maximize the probability of exceeding the threshold, which naturally incorporates the value of volatility in deficit scenarios without universally penalizing it. This turns out to be a quasiconvex optimization problem — a fractional program in the form of a Sharpe ratio — solved via CVXPY's Disciplined Quasiconvex Programming module. The quasiconvexity proof is a required deliverable in the paper's methodology section. The Monte Carlo simulation that quantifies win probability underwent its own correction. The first design used a Dirichlet distribution for sampling coalition weights from the simplex, which seemed natural. It's wrong: a Dirichlet's off-diagonal covariances are mathematically constrained to be negative, meaning the simulation can't model a scenario where two demographic blocs move in the same direction — which is precisely what wave elections look like. The replacement is a Logistic-Normal distribution parameterized via isometric log-ratio coordinates, which preserves arbitrary covariance structure while keeping all samples on the probability simplex. ## The Cleaning Problem One decision that took longer to get right than it should have was which model to use for the LLM cleaning step. The initial plan used Gemini Pro via the Google AI Studio API. The problem is reproducibility. Cloud-hosted LLMs undergo continuous silent weight updates and safety classifier changes. A prompt processed in June 2026 won't produce identical output in July 2026, regardless of temperature settings. Because the cleaning output feeds directly into the RoBERTa scorer and the fine-tuning dataset, any non-determinism here propagates throughout the entire pipeline, rendering the downstream deterministic seed contract meaningless. The fix is to use a locally-hosted open-weight model — Qwen2.5-7B-Instruct or Mistral-7B-Instruct — whose exact weights can be frozen and archived with the replication package. ## Good Night So, a scheduling conflict landed me in a religion class I hadn't sought out. A job listing pulled me into portfolio theory I didn't yet understand. One afternoon in abstract algebra, I started wondering whether you could strip the content out of a voter group the same way you strip the content out of a stock, leaving only the signal, the number, the thing that could be optimized. It turned out you could. Electoral Equilibrium is, at its core, an argument that political coalitions behave like portfolios. Loyalty shifts like returns. Demographic groups carry covariance. A winning coalition satisfies a threshold constraint the same way a minimum-variance portfolio satisfies a return target. The math doesn't care whether the underlying object is a tech stock or an Evangelical voter in a swing county — it only sees the structure. I don't know how accurate the model will be. That's what the 2028 election is for. But I know the question it's asking is the right one: not who is winning, but what would have to change for someone to still win if the world shifted overnight. That's a question polling aggregators don't answer. It's the question every campaign strategist is actually asking. And it's the question I stumbled into by accident.

Planning it Out